WorldWideScience

Sample records for model mm5 driven

  1. 2-way coupling the hydrological land surface model PROMET with the regional climate model MM5

    Directory of Open Access Journals (Sweden)

    F. Zabel

    2013-05-01

    Full Text Available Most land surface hydrological models (LSHMs consider land surface processes (e.g. soil–plant–atmosphere interactions, lateral water flows, snow and ice in a spatially detailed manner. The atmosphere is considered as exogenous driver, neglecting feedbacks between the land surface and the atmosphere. On the other hand, regional climate models (RCMs generally simulate land surface processes through coarse descriptions and spatial scales but include land–atmosphere interactions. What is the impact of the differently applied model physics and spatial resolution of LSHMs on the performance of RCMs? What feedback effects are induced by different land surface models? This study analyses the impact of replacing the land surface module (LSM within an RCM with a high resolution LSHM. A 2-way coupling approach was applied using the LSHM PROMET (1 × 1 km2 and the atmospheric part of the RCM MM5 (45 × 45 km2. The scaling interface SCALMET is used for down- and upscaling the linear and non-linear fluxes between the model scales. The change in the atmospheric response by MM5 using the LSHM is analysed, and its quality is compared to observations of temperature and precipitation for a 4 yr period from 1996 to 1999 for the Upper Danube catchment. By substituting the Noah-LSM with PROMET, simulated non-bias-corrected near-surface air temperature improves for annual, monthly and daily courses when compared to measurements from 277 meteorological weather stations within the Upper Danube catchment. The mean annual bias was improved from −0.85 to −0.13 K. In particular, the improved afternoon heating from May to September is caused by increased sensible heat flux and decreased latent heat flux as well as more incoming solar radiation in the fully coupled PROMET/MM5 in comparison to the NOAH/MM5 simulation. Triggered by the LSM replacement, precipitation overall is reduced; however simulated precipitation amounts are still of high uncertainty, both

  2. A shallow convection parameterization for the non-hydrostatic MM5 mesoscale model

    Energy Technology Data Exchange (ETDEWEB)

    Seaman, N.L.; Kain, J.S.; Deng, A. [Pennsylvania State Univ., University Park, PA (United States)

    1996-04-01

    A shallow convection parameterization suitable for the Pennsylvannia State University (PSU)/National Center for Atmospheric Research nonhydrostatic mesoscale model (MM5) is being developed at PSU. The parameterization is based on parcel perturbation theory developed in conjunction with a 1-D Mellor Yamada 1.5-order planetary boundary layer scheme and the Kain-Fritsch deep convection model.

  3. Surface Energy Balance in Jakarta and Neighboring Regions As Simulated Using Fifth Mesoscale Model (MM5

    Directory of Open Access Journals (Sweden)

    Yopi Ilhamsyah

    2014-04-01

    Full Text Available The objective of the present research was to assess the surface energy balance particularly in terms of the computed surface energy and radiation balance and the development of boundary layer over Jakarta and Neighboring Regions (JNR by means of numerical model of fifth generation of Mesoscale Model (MM5. The MM5 with four domains of 9 kilometers in spatial resolution presenting the outermost and the innermost of JNR is utilized. The research focuses on the third and fourth domains covering the entire JNR. The description between radiation and energy balance at the surface is obtained from the model. The result showed that energy balance is higher in the city area during daytime. Meanwhile, energy components, e.g., surface sensible and latent heat flux showed that at the sea and in the city areas were higher than other areas. Moreover, ground flux showed eastern region was higher than others. In general, radiation and energy balance was higher in the daytime and lower in the nighttime for all regions. The calculation of Bowen Ratio, the ratio of surface sensible and latent heat fluxes, was also higher in the city area, reflecting the dominations of urban and built-up land in the region. Meanwhile, Bowen Ratio in the rural area dominated by irrigated cropland was lower. It is consistent with changes of land cover properties, e.g. albedo, soil moisture, and thermal characteristics. In addition, the boundary layer is also higher in the city. Meanwhile western region dominated by suburban showed higher boundary layer instead of eastern region.

  4. Weather forecast in north-western Greece: RISKMED warnings and verification of MM5 model

    Directory of Open Access Journals (Sweden)

    A. Bartzokas

    2010-02-01

    Full Text Available The meteorological model MM5 is applied operationally for the area of north-western Greece for one-year period (1 June 2007–31 May 2008. The model output is used for daily weather forecasting over the area. An early warning system is developed, by dividing the study area in 16 sub-regions and defining specific thresholds for issuing alerts for adverse weather phenomena. The verification of the model is carried out by comparing the model results with observations from three automatic meteorological stations. For air temperature and wind speed, correlation coefficients and biases are calculated, revealing that there is a significant overestimation of the early morning air temperature. For precipitation amount, yes/no contingency tables are constructed for 4 specific thresholds and some categorical statistics are applied, showing that the prediction of precipitation in the area under study is generally satisfactory. Finally, the thunderstorm warnings issued by the system are verified against the observed lightning activity.

  5. Spatiao – Temporal Evaluation and Comparison of MM5 Model using Similarity Algorithm

    Directory of Open Access Journals (Sweden)

    N. Siabi

    2016-02-01

    Full Text Available Introduction temporal and spatial change of meteorological and environmental variables is very important. These changes can be predicted by numerical prediction models over time and in different locations and can be provided as spatial zoning maps with interpolation methods such as geostatistics (16, 6. But these maps are comparable to each other as visual, qualitative and univariate for a limited number of maps (15. To resolve this problem the similarity algorithm is used. This algorithm is a simultaneous comparison method to a large number of data (18. Numerical prediction models such as MM5 were used in different studies (10, 22, and 23. But a little research is done to compare the spatio-temporal similarity of the models with real data quantitatively. The purpose of this paper is to integrate geostatistical techniques with similarity algorithm to study the spatial and temporal MM5 model predicted results with real data. Materials and Methods The study area is north east of Iran. 55 to 61 degrees of longitude and latitude is 30 to 38 degrees. Monthly and annual temperature and precipitation actual data for the period of 1990-2010 was received from the Meteorological Agency and Department of Energy. MM5 Model Data, with a spatial resolution 0.5 × 0.5 degree were downloaded from the NASA website (5. GS+ and ArcGis software were used to produce each variable map. We used multivariate methods co-kriging and kriging with an external drift by applying topography and height as a secondary variable via implementing Digital Elevation Model. (6,12,14. Then the standardize and similarity algorithms (9,11 was applied by programming in MATLAB software to each map grid point. The spatial and temporal similarities between data collections and model results were obtained by F values. These values are between 0 and 0.5 where the value below 0.2 indicates good similarity and above 0.5 shows very poor similarity. The results were plotted on maps by MATLAB

  6. Study of tropical cyclone "Fanoos" using MM5 model – a case study

    Directory of Open Access Journals (Sweden)

    S. Ramalingeswara Rao

    2009-01-01

    Full Text Available Tropical cyclones are one of the most intense weather hazards over east coast of India and create a lot of devastation through gale winds and torrential floods while they cross the coast. So an attempt is made in this study to simulate track and intensity of tropical cyclone "Fanoos", which is formed over the Bay of Bengal during 5–10 December 2005 by using mesoscale model MM5. The simulated results are compared with the observed results of India Meteorological Department (IMD; results show that the cumulus parameterization scheme, Kain-Fritsch (KF is more accurately simulated both in track and intensity than the other Betts-Miller (BM and Grell Schemes. The reason for better performance of KF-1 scheme may be due to inclusion of updrafts and downdrafts. The model could predict the minimum Central Sea Level Pressure (CSLP as 983 hPa as compared to the IMD reports of 984 hPa and the wind speed is simulated at maximum 63 m/s compared to the IMD estimates of 65 m/s. Secondly "Fanoos" development from the lagrangian stand point in terms of vertical distribution of Potential Vorticity (PV is also carried out around cyclone centre.

  7. Simulation of coastal winds along the central west coast of India using the MM5 mesoscale model

    Digital Repository Service at National Institute of Oceanography (India)

    Pushpadas, D.; Vethamony, P.; Sudheesh, K.; George, S.; Babu, M.T.; Nair, T.M.B.

    A high-resolution mesoscale numerical model (MM5) has been used to study the coastal atmospheric circulation of the central west coast of India, and Goa in particular. The model is employed with three nested domains. The innermost domain of 3 km...

  8. Simulation of Severe Local Storm by Mesoscale Model MM5 and Validation Using Data from Different Platforms

    Directory of Open Access Journals (Sweden)

    Prosenjit Chatterjee

    2015-01-01

    Full Text Available During premonsoon season (March to May convective developments in various forms are common phenomena over the Gangetic West Bengal, India. In the present work, simulation of wind squall on three different dates has been attempted with the help of mesoscale model MM5. The combination of various physical schemes in MM5 is taken as that found in a previous work done to simulate severe local storms over the Gangetic West Bengal. In the present study the model successfully simulates wind squall showing pressure rise, wind shift, wind surge, temperature drop, and heavy rainfall, in all cases. Convective cloud development and rainfall simulation by the model has been validated by the corresponding product from Doppler Weather Radar located at Kolkata and TRMM satellite product 3B42 (V6, respectively. It is found that the model is capable of capturing heavy rainfall pattern with up to three-hour time gap existing between simulation and observation of peak rainfall occurrence. In all simulations there is spatial as well as temporal shift from observation.

  9. Response of global warming on regional climate change over Korea: An experiment with the MM5 model

    Science.gov (United States)

    Boo, Kyung-On; Kwon, Won-Tae; Oh, Jai-Ho; Baek, Hee-Jeong

    2004-11-01

    This study is to investigate changes in regional surface climate arising from global warming with MM5 downscaling simulation for the period 1971-2100. The main focus is on the drought conditions over Korea. Palmer Drought Severity Index (PDSI) is utilized as a measure of drought severity. The important findings show the increase of surface air temperature by 6°C and precipitation by 25% over Korea at the end of the 21st century. The increasing trend of temperature is associated with an increasing trend of evapotranspiration and precipitation. Climatological precipitation amount appropriate for existing conditions is larger than the precipitation amounts. Hence, it actually produces deficit in precipitation. This exhibits a negative PDSI. As a result droughts are expected to be severe and frequent. Better resolved topography in MM5 induces large changes in local precipitation compared with temperature. Consequently peaks of negative PDSI anomalies appear over southern parts of Korea, where a large reduction in precipitation is noticed in addition to warming.

  10. Evaluating the performance of an integrated CALPUFF-MM5 modeling system for predicting SO{sub 2} emission from a refinery

    Energy Technology Data Exchange (ETDEWEB)

    Abdul-Wahab, Sabah Ahmed [Sultan Qaboos University, Department of Mechanical and Industrial Engineering, College of Engineering, Muscat (Oman); Ali, Sappurd [National Engineering and Scientific Commission (NESCOM), Islamabad (Pakistan); Sardar, Sabir; Irfan, Naseem [Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad (Pakistan); Al-Damkhi, Ali [Public Authority for Applied Education and Training (PAAET), Department of Environmental Sciences College of Health Sciences, Salmiyah (Kuwait)

    2011-12-15

    Oil refineries are one of the proven sources of environmental pollution as they emit more than 100 chemicals into the atmosphere including sulfur dioxide (SO{sub 2}). The dispersion patterns of SO{sub 2} from emissions of Sohar refinery was simulated by employing California Puff (CALPUFF) model integrated with state of the art meteorological Mesoscale Model (MM5). The results of this simulation were used to quantify the ground level concentrations of SO{sub 2} in and around the refinery. The evaluation of the CALPUFF and MM5 modeling system was carried out by comparing the estimated results with that of observed data of the same area. The predicted concentrations of SO{sub 2} agreed well with the observed data, with minor differences in magnitudes. In addition, the ambient air quality of the area was checked by comparing the model results with the regulatory limits for SO{sub 2} set by the Ministry of Environment and Climate Affairs (MECA) in Oman. From the analysis of results, it was found that the concentration of SO{sub 2} in the nearby communities of Sohar refinery is well within the regulatory limits specified by MECA. Based on these results, it was concluded that no health risk, due to SO{sub 2} emissions, is present in areas adjacent to the refinery. (orig.)

  11. Application of TRMM PR and TMI Measurements to Assess Cloud Microphysical Schemes in the MM5 Model for a Winter Storm

    Science.gov (United States)

    Han, Mei; Braun, Scott A.; Olson, William S.; Persson, P. Ola G.; Bao, Jian-Wen

    2009-01-01

    Seen by the human eye, precipitation particles are commonly drops of rain, flakes of snow, or lumps of hail that reach the ground. Remote sensors and numerical models usually deal with information about large collections of rain, snow, and hail (or graupel --also called soft hail ) in a volume of air. Therefore, the size and number of the precipitation particles and how particles interact, evolve, and fall within the volume of air need to be represented using physical laws and mathematical tools, which are often implemented as cloud and precipitation microphysical parameterizations in numerical models. To account for the complexity of the precipitation physical processes, scientists have developed various types of such schemes in models. The accuracy of numerical weather forecasting may vary dramatically when different types of these schemes are employed. Therefore, systematic evaluations of cloud and precipitation schemes are of great importance for improvement of weather forecasts. This study is one such endeavor; it pursues quantitative assessment of all the available cloud and precipitation microphysical schemes in a weather model (MM5) through comparison with the observations obtained by National Aeronautics and Space Administration (NASA) s and Japan Aerospace Exploration Agency (JAXA) s Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI). When satellite sensors (like PR or TMI) detect information from precipitation particles, they cannot directly observe the microphysical quantities (e.g., water species phase, density, size, and amount etc.). Instead, they tell how much radiation is absorbed by rain, reflected away from the sensor by snow or graupel, or reflected back to the satellite. On the other hand, the microphysical quantities in the model are usually well represented in microphysical schemes and can be converted to radiative properties that can be directly compared to the corresponding PR and TMI observations

  12. The Same-Source Parallel MM5

    Directory of Open Access Journals (Sweden)

    John Michalakes

    2000-01-01

    Full Text Available Beginning with the March 1998 release of the Penn State University/NCAR Mesoscale Model (MM5, and continuing through eight subsequent releases up to the present, the official version has run on distributed -memory (DM parallel computers. Source translation and runtime library support minimize the impact of parallelization on the original model source code, with the result that the majority of code is line-for-line identical with the original version. Parallel performance and scaling are equivalent to earlier, hand-parallelized versions; the modifications have no effect when the code is compiled and run without the DM option. Supported computers include the IBM SP, Cray T3E, Fujitsu VPP, Compaq Alpha clusters, and clusters of PCs (so-called Beowulf clusters. The approach also is compatible with shared-memory parallel directives, allowing distributed-memory/shared-memory hybrid parallelization on distributed-memory clusters of symmetric multiprocessors.

  13. Analysis on MM5 predictions at Sriharikota during northeast ...

    Indian Academy of Sciences (India)

    The objective of this study is to analyse the performance of the PSU-NCAR Mesoscale Model Version 5 (MM5), for northeast monsoon 2008 that includes tropical cyclones – Rashmi, Khai-Muk and Nisha and convective events over Sriharikota region, the rocket launch centre. The impact of objective analysis system using ...

  14. Consistent model driven architecture

    Science.gov (United States)

    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.

  15. Model-Driven and Pattern-Based Integration of Process-Driven SOA Models

    OpenAIRE

    Zdun, Uwe; Dustdar, Schahram

    2006-01-01

    Service-oriented architectures (SOA) are increasingly used in the context of business processes. However, the modeling approaches for process-driven SOAs do not yet sufficiently integrate the various kinds of models relevant for a process-driven SOA -- ranging from process models to software architectural models to software design models. We propose to integrate process-driven SOA models via a model-driven software development approach that is based on proven practices do...

  16. Distributed simulation a model driven engineering approach

    CERN Document Server

    Topçu, Okan; Oğuztüzün, Halit; Yilmaz, Levent

    2016-01-01

    Backed by substantive case studies, the novel approach to software engineering for distributed simulation outlined in this text demonstrates the potent synergies between model-driven techniques, simulation, intelligent agents, and computer systems development.

  17. Test-driven modeling of embedded systems

    DEFF Research Database (Denmark)

    Munck, Allan; Madsen, Jan

    2015-01-01

    To benefit maximally from model-based systems engineering (MBSE) trustworthy high quality models are required. From the software disciplines it is known that test-driven development (TDD) can significantly increase the quality of the products. Using a test-driven approach with MBSE may have...... a similar positive effect on the quality of the system models and the resulting products and may therefore be desirable. To define a test-driven model-based systems engineering (TD-MBSE) approach, we must define this approach for numerous sub disciplines such as modeling of requirements, use cases......, scenarios, behavior, architecture, etc. In this paper we present a method that utilizes the formalism of timed automatons with formal and statistical model checking techniques to apply TD-MBSE to the modeling of system architecture and behavior. The results obtained from applying it to an industrial case...

  18. Evaluating Quality in Model-Driven Engineering

    OpenAIRE

    Mohagheghi, Parastoo; Aagedal, Jan

    2007-01-01

    In Model-Driven Engineering (MDE), models are the prime artifacts, and developing high-quality systems depends on developing high-quality models and performing transformations that preserve quality or even improve it. This paper presents quality goals in MDE and states that the quality of models is affected by the quality of modeling languages, tools, modeling processes, the knowledge and experience of modelers, and the quality assurance techniques applied. The paper further presents related ...

  19. Semantic Web and Model-Driven Engineering

    CERN Document Server

    Parreiras, Fernando S

    2012-01-01

    The next enterprise computing era will rely on the synergy between both technologies: semantic web and model-driven software development (MDSD). The semantic web organizes system knowledge in conceptual domains according to its meaning. It addresses various enterprise computing needs by identifying, abstracting and rationalizing commonalities, and checking for inconsistencies across system specifications. On the other side, model-driven software development is closing the gap among business requirements, designs and executables by using domain-specific languages with custom-built syntax and se

  20. Modeling magnetically driven synthetic microcapsules

    Science.gov (United States)

    Masoud, Hassan; Alexeev, Alexander

    2009-11-01

    Using computer simulations and theory, we examine how to design magnetically-responsive synthetic microcapsules that able to move in a steady manner in microfluidic channels. These compliant fluid-filled capsules encompass superparamagnetic nanoparticles in their solid shells and, thereby, can be manipulated by alternating magnetic forces. To model the magnetic capsules propelled in fluid-filled microchannels, we employ a hybrid computational method for fluid-structure interactions. This method integrates the lattice Boltzmann model for the fluid dynamics and the lattice spring model for the micromechanics of solids. We show that in circulating magnetic field the capsules propel along sticky microchannel walls. The direction of capsule motion depends on the relative location of the solid surface, whereas the propulsion speed can be regulated through the wall adhesiveness, amplitude and frequency of magnetic forces, and elasticity of capsule's shell. The results indicate that such mobile fluid-filled containers could find application in lab-on-chip systems for controlled delivery of minute amounts of fluidic samples.

  1. Influences of Asymmetric Heating on Hurricane Evolution in the MM5.

    Science.gov (United States)

    Möller, J. Dominique; Shapiro, Lloyd J.

    2005-11-01

    While previous idealized studies have demonstrated the importance of asymmetric atmospheric features in the intensification of a symmetric tropical cyclone vortex, the role of convectively generated asymmetries in creating changes in the azimuthally averaged cyclone is not well understood. In the present study the full-physics nonhydrostatic fifth-generation Pennsylvania State University National Center for Atmospheric Research (PSU NCAR) Mesoscale Model (MM5) is used to evaluate the influence of such asymmetries. Rather than adding winds and temperatures in balance with a specified potential vorticity (PV) asymmetry, or temperature perturbations themselves, to a symmetric vortex as in previous studies, a diabatic heating asymmetry is imposed on a spunup model hurricane. The impact of short-duration eyewall-scale monochromatic azimuthal wavenumber diabatic heating on the short- and long-term evolution of the azimuthally averaged vortex is evaluated, and a tangential wind budget is made to determine the mechanisms responsible for the short-term impact.It is found that the small eddy kick created by the additional diabatic heating asymmetry leads to a substantially amplified long-term change in the azimuthally averaged vortex, with episodes of strong relative weakening and strengthening following at irregular intervals. This behavior is diabatically controlled. It is also found that the symmetric secondary circulation can be active in creating short-term changes in the vortex, and is not simply a passive response as in previous studies with dry physics. A central conclusion of the study is that the structure of the spunup hurricane vortex, in particular preexisting asymmetric features, can have a substantial influence on the character of the response to an additional diabatic heating asymmetry. The results also imply that a small change in the factors that control convective activity will have a substantial lasting consequence for the intensification of a hurricane.

  2. Quantitative system validation in model driven design

    DEFF Research Database (Denmark)

    Hermanns, Hilger; Larsen, Kim Guldstrand; Raskin, Jean-Francois

    2010-01-01

    The European STREP project Quasimodo1 develops theory, techniques and tool components for handling quantitative constraints in model-driven development of real-time embedded systems, covering in particular real-time, hybrid and stochastic aspects. This tutorial highlights the advances made...

  3. Model Driven Development of Data Sensitive Systems

    DEFF Research Database (Denmark)

    Olsen, Petur

    2014-01-01

    to the values of variables. This theses strives to improve model-driven development of such data-sensitive systems. This is done by addressing three research questions. In the first we combine state-based modeling and abstract interpretation, in order to ease modeling of data-sensitive systems, while allowing...... efficient model-checking and model-based testing. In the second we develop automatic abstraction learning used together with model learning, in order to allow fully automatic learning of data-sensitive systems to allow learning of larger systems. In the third we develop an approach for modeling and model-based...... detection and pushing error detection to earlier stages of development. The complexity of modeling and the size of systems which can be analyzed is severely limited when introducing data variables. The state space grows exponentially in the number of variable and the domain size of the variables...

  4. Modeling of laser-driven hydrodynamics experiments

    Science.gov (United States)

    di Stefano, Carlos; Doss, Forrest; Rasmus, Alex; Flippo, Kirk; Desjardins, Tiffany; Merritt, Elizabeth; Kline, John; Hager, Jon; Bradley, Paul

    2017-10-01

    Correct interpretation of hydrodynamics experiments driven by a laser-produced shock depends strongly on an understanding of the time-dependent effect of the irradiation conditions on the flow. In this talk, we discuss the modeling of such experiments using the RAGE radiation-hydrodynamics code. The focus is an instability experiment consisting of a period of relatively-steady shock conditions in which the Richtmyer-Meshkov process dominates, followed by a period of decaying flow conditions, in which the dominant growth process changes to Rayleigh-Taylor instability. The use of a laser model is essential for capturing the transition. also University of Michigan.

  5. Model-driven software migration a methodology

    CERN Document Server

    Wagner, Christian

    2014-01-01

    Today, reliable software systems are the basis of any business or company. The continuous further development of those systems is the central component in software evolution. It requires a huge amount of time- man power- as well as financial resources. The challenges are size, seniority and heterogeneity of those software systems. Christian Wagner addresses software evolution: the inherent problems and uncertainties in the process. He presents a model-driven method which leads to a synchronization between source code and design. As a result the model layer will be the central part in further e

  6. Model-Driven Development of Safety Architectures

    Science.gov (United States)

    Denney, Ewen; Pai, Ganesh; Whiteside, Iain

    2017-01-01

    We describe the use of model-driven development for safety assurance of a pioneering NASA flight operation involving a fleet of small unmanned aircraft systems (sUAS) flying beyond visual line of sight. The central idea is to develop a safety architecture that provides the basis for risk assessment and visualization within a safety case, the formal justification of acceptable safety required by the aviation regulatory authority. A safety architecture is composed from a collection of bow tie diagrams (BTDs), a practical approach to manage safety risk by linking the identified hazards to the appropriate mitigation measures. The safety justification for a given unmanned aircraft system (UAS) operation can have many related BTDs. In practice, however, each BTD is independently developed, which poses challenges with respect to incremental development, maintaining consistency across different safety artifacts when changes occur, and in extracting and presenting stakeholder specific information relevant for decision making. We show how a safety architecture reconciles the various BTDs of a system, and, collectively, provide an overarching picture of system safety, by considering them as views of a unified model. We also show how it enables model-driven development of BTDs, replete with validations, transformations, and a range of views. Our approach, which we have implemented in our toolset, AdvoCATE, is illustrated with a running example drawn from a real UAS safety case. The models and some of the innovations described here were instrumental in successfully obtaining regulatory flight approval.

  7. Model Driven Software Development for Agricultural Robotics

    DEFF Research Database (Denmark)

    Larsen, Morten

    processing, control engineering, etc. This thesis proposes a Model-Driven Software Develop- ment based approach to model, analyse and partially generate the software implementation of a agricultural robot. Furthermore, Guidelines for mod- elling the architecture of an agricultural robots are provided......The design and development of agricultural robots, consists of both mechan- ical, electrical and software components. All these components must be de- signed and combined such that the overall goal of the robot is fulfilled. The design and development of these systems require collaboration between...... mul- tiple engineering disciplines. To this end, architectural specifications can serve as means for communication between different engineering disciplines. Such specifications aid in establishing the interface between the different com- ponents, belonging to different domains such as image...

  8. Modelling of natural-convection driven heat exchangers

    NARCIS (Netherlands)

    Dirkse, M.H.; Loon, van W.K.P.; Stigter, J.D.; Bot, G.P.A.

    2007-01-01

    Abstract: A lumped model is developed for shell-and-tube heat exchangers driven by natural convection, which is based on a one-dimensional approximation. The heat flux is driven by the logarithmic mean temperature difference. The volumetric air flow rate is driven by the buoyant force. Based on the

  9. Model-Driven Software Evolution : A Research Agenda

    NARCIS (Netherlands)

    Van Deursen, A.; Visser, E.; Warmer, J.

    2007-01-01

    Software systems need to evolve, and systems built using model-driven approaches are no exception. What complicates model-driven engineering is that it requires multiple dimensions of evolution. In regular evolution, the modeling language is used to make the changes. In meta-model evolution, changes

  10. Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China

    Science.gov (United States)

    Deng, T.; Chen, Y.; Wan, Q.

    2017-12-01

    The Community Multiscale Air Quality (CMAQ) model was utilized for forecasting air quality over the Pearl River Delta (PRD) region from December 2013 to January 2014. The pollution forecasting performance of CMAQ coupled with the two different meteorological models, the Global/Regional Assimilation and Prediction System (GRAPES) and the 5th-generation Mesoscale Model (MM5), was assessed by combining observational data. The effect of meteorological factors and physical-chemical processes on forecast results was discussed through process analysis. The results showed that both models have similar good performance with better performance by GRAPES-CMAQ. GRAPES was superior in predicting the overall meteorological element variation tendencies but showed large deviations in atmospheric pressure and wind speed. It contributed to higher correlation coefficients of the pollutants with GRAPES-CMAQ, but with greater deviation. The underestimations of nitrate and ammonium salt contributed to the underestimations of Particle Matter (PM) and extinction coefficients. Surface layer SO2, CO and NO source emissions made the sole positive contribution. O3 originated mainly from horizontal and vertical transport and chemical processes were the main consumption item. On the contrary, NO2 derived mainly from chemical production.

  11. An ontology-driven, diagnostic modeling system.

    Science.gov (United States)

    Haug, Peter J; Ferraro, Jeffrey P; Holmen, John; Wu, Xinzi; Mynam, Kumar; Ebert, Matthew; Dean, Nathan; Jones, Jason

    2013-06-01

    To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia. We developed a system that automates the creation of diagnostic decision-support applications. It relies on a medical ontology to direct the acquisition of clinic data from a clinical data warehouse and uses an automated analytic system to apply a sequence of machine learning algorithms that create applications for diagnostic screening. We refer to this system as the ontology-driven diagnostic modeling system (ODMS). We tested this system using samples of patient data collected in Salt Lake City emergency rooms and stored in Intermountain Healthcare's enterprise data warehouse. The system was used in the preliminary development steps of a tool to identify patients with pneumonia in the emergency department. This tool was compared with a manually created diagnostic tool derived from a curated dataset. The manually created tool is currently in clinical use. The automatically created tool had an area under the receiver operating characteristic curve of 0.920 (95% CI 0.916 to 0.924), compared with 0.944 (95% CI 0.942 to 0.947) for the manually created tool. Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.

  12. The ModelCC Model-Driven Parser Generator

    Directory of Open Access Journals (Sweden)

    Fernando Berzal

    2015-01-01

    Full Text Available Syntax-directed translation tools require the specification of a language by means of a formal grammar. This grammar must conform to the specific requirements of the parser generator to be used. This grammar is then annotated with semantic actions for the resulting system to perform its desired function. In this paper, we introduce ModelCC, a model-based parser generator that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual syntax and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a reference resolution mechanism so that ModelCC is able to instantiate abstract syntax graphs, rather than mere abstract syntax trees.

  13. Model-driven development of service compositions for enterprise interoperability

    NARCIS (Netherlands)

    Khadka, Ravi; Sapkota, Brahmananda; Ferreira Pires, Luis; Jansen, Slinger; van Sinderen, Marten J.; Johnson, Pontus

    2011-01-01

    Service-Oriented Architecture (SOA) has emerged as an architectural style to foster enterprise interoperability, as it claims to facilitate the flexible composition of loosely coupled enterprise applications and thus alleviates the heterogeneity problem among enterprises. Meanwhile, Model-Driven

  14. Data-driven Modelling for decision making under uncertainty

    Science.gov (United States)

    Angria S, Layla; Dwi Sari, Yunita; Zarlis, Muhammad; Tulus

    2018-01-01

    The rise of the issues with the uncertainty of decision making has become a very warm conversation in operation research. Many models have been presented, one of which is with data-driven modelling (DDM). The purpose of this paper is to extract and recognize patterns in data, and find the best model in decision-making problem under uncertainty by using data-driven modeling approach with linear programming, linear and nonlinear differential equation, bayesian approach. Model criteria tested to determine the smallest error, and it will be the best model that can be used.

  15. Mathematical modeling of compression processes in air-driven boosters

    International Nuclear Information System (INIS)

    Li Zeyu; Zhao Yuanyang; Li Liansheng; Shu Pengcheng

    2007-01-01

    The compressed air in normal pressure is used as the source of power of the air-driven booster. The continuous working of air-driven boosters relies on the difference of surface area between driven piston and driving piston, i.e., the different forces acting on the pistons. When the working surface area of the driving piston for providing power is greater than that of the driven piston for compressing gas, the gas in compression chamber will be compressed. On the basis of the first law of thermodynamics, the motion regulation of piston is analyzed and the mathematical model of compression processes is set up. Giving a calculating example, the vary trends of gas pressure and pistons' move in working process of booster have been gotten. The change of parameters at different working conditions is also calculated and compared. And the corresponding results can be referred in the design of air-driven boosters

  16. Test Driven Development of Scientific Models

    Science.gov (United States)

    Clune, Thomas L.

    2014-01-01

    Test-Driven Development (TDD), a software development process that promises many advantages for developer productivity and software reliability, has become widely accepted among professional software engineers. As the name suggests, TDD practitioners alternate between writing short automated tests and producing code that passes those tests. Although this overly simplified description will undoubtedly sound prohibitively burdensome to many uninitiated developers, the advent of powerful unit-testing frameworks greatly reduces the effort required to produce and routinely execute suites of tests. By testimony, many developers find TDD to be addicting after only a few days of exposure, and find it unthinkable to return to previous practices.After a brief overview of the TDD process and my experience in applying the methodology for development activities at Goddard, I will delve more deeply into some of the challenges that are posed by numerical and scientific software as well as tools and implementation approaches that should address those challenges.

  17. Requirements Traceability and Transformation Conformance in Model-Driven Development

    NARCIS (Netherlands)

    Andrade Almeida, João; van Eck, Pascal; Iacob, Maria Eugenia

    2006-01-01

    The variety of design artefacts (models) produced in a model-driven design process results in an intricate rela-tionship between requirements and the various models. This paper proposes a methodological framework that simplifies management of this relationship. This frame-work is a basis for tracing

  18. Survey of Traceability Approaches in Model-Driven Engineering

    NARCIS (Netherlands)

    Galvao, I.; Göknil, Arda

    2007-01-01

    Models have been used in various engineering fields to help managing complexity and represent information in different abstraction levels, according to specific notations and stakeholder's viewpoints. Model-Driven Engineering (MDE) gives the basic principles for the use of models as primary

  19. Towards a sufficiency-driven business model : Experiences and opportunities

    NARCIS (Netherlands)

    Bocken, N.M.P.; Short, SW

    2016-01-01

    Business model innovation is an important lever for change to tackle pressing sustainability issues. In this paper, ‘sufficiency’ is proposed as a driver of business model innovation for sustainability. Sufficiency-driven business models seek to moderate overall resource consumption by curbing

  20. Observational Data-Driven Modeling and Optimization of Manufacturing Processes

    OpenAIRE

    Sadati, Najibesadat; Chinnam, Ratna Babu; Nezhad, Milad Zafar

    2017-01-01

    The dramatic increase of observational data across industries provides unparalleled opportunities for data-driven decision making and management, including the manufacturing industry. In the context of production, data-driven approaches can exploit observational data to model, control and improve the process performance. When supplied by observational data with adequate coverage to inform the true process performance dynamics, they can overcome the cost associated with intrusive controlled de...

  1. MODEL DRIVEN DEVELOPMENT OF ONLINE BANKING SYSTEMS

    Directory of Open Access Journals (Sweden)

    Bresfelean Vasile Paul

    2011-07-01

    Full Text Available In case of online applications the cycle of software development varies from the routine. The online environment, the variety of users, the treatability of the mass of information created by them, the reusability and the accessibility from different devices are all factors of these systems complexity. The use of model drive approach brings several advantages that ease up the development process. Working prototypes that simplify client relationship and serve as the base of model tests can be easily made from models describing the system. These systems make possible for the banks clients to make their desired actions from anywhere. The user has the possibility of accessing information or making transactions.

  2. Menthor Editor: An Ontology-Driven Conceptual Modeling Platform

    NARCIS (Netherlands)

    Moreira, João Luiz; Sales, Tiago Prince; Guerson, John; Braga, Bernardo F.B; Brasileiro, Freddy; Sobral, Vinicius

    2016-01-01

    The lack of well-founded constructs in ontology tools can lead to the construction of non-intended models. In this demonstration we present the Menthor Editor, an ontology-driven conceptual modelling platform which incorporates the theories of the Unified Foundational Ontology (UFO). We illustrate

  3. A Model-Driven Approach to e-Course Management

    Science.gov (United States)

    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…

  4. Conceptual models of the wind-driven and thermohaline circulation

    NARCIS (Netherlands)

    Drijfhout, S.S.; Marshall, D.P.; Dijkstra, H.A.

    2013-01-01

    Conceptual models are a vital tool for understanding the processes that maintain the global ocean circulation, both in nature and in complex numerical ocean models. In this chapter we provide a broad overview of our conceptual understanding of the wind-driven circulation, the thermohaline

  5. Data mining, knowledge discovery and data-driven modelling

    NARCIS (Netherlands)

    Solomatine, D.P.; Velickov, S.; Bhattacharya, B.; Van der Wal, B.

    2003-01-01

    The project was aimed at exploring the possibilities of a new paradigm in modelling - data-driven modelling, often referred as "data mining". Several application areas were considered: sedimentation problems in the Port of Rotterdam, automatic soil classification on the basis of cone penetration

  6. Data-Driven Model Order Reduction for Bayesian Inverse Problems

    KAUST Repository

    Cui, Tiangang

    2014-01-06

    One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.

  7. Six Sigma Driven Enterprise Model Transformation

    Directory of Open Access Journals (Sweden)

    Raymond Vella

    2009-10-01

    Full Text Available Enterprise architecture methods provide a structured system to understand enterprise activities. However, existing enterprise modelling methodologies take static views of the enterprise and do not naturally lead to a path of improvement during enterprise model transformation. This paper discusses the need for a methodology to facilitate changes for improvement in an enterprise. The six sigma methodology is proposed as the tool to facilitate progressive and continual Enterprise Model Transformation to allow businesses to adapt to meet increased customer expectation and global competition. An alignment of six sigma with phases of GERAM life cycle is described with inclusion of Critical-To-Satisfaction (CTS requirements. The synergies of combining the two methodologies are presented in an effort to provide a more culturally embedded framework for Enterprise Model Transformation that builds on the success of six sigma.

  8. A model for information retrieval driven by conceptual spaces

    OpenAIRE

    Tanase, D.

    2015-01-01

    A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ repre...

  9. Data driven mathematical models for policy making

    OpenAIRE

    Nannyonga, Betty

    2011-01-01

    This thesis consists of two papers. 1. Betty Nannyonga, D.J.T. Sumpter, J.Y.T. Mugisha and L.S. Luboobi: The Dynamics,causes and possible prevention of Hepaititis E outbreaks. 2. Betty Nannyonga, D.J.T. Sumpter, andStam Nicolis: A dynamical systems approach tosocial and economic development. The first paper deals with a deterministic approach of modelling a Hepatitis E outbreak whenmalaria is endemic in a population. We design three models based on the epidemiology ofHepatitis E, malaria, and...

  10. Model-driven and software product line engineering

    CERN Document Server

    Royer, Jean-Claude

    2013-01-01

    Many approaches to creating Software Product Lines have emerged that are based on Model-Driven Engineering. This book introduces both Software Product Lines and Model-Driven Engineering, which have separate success stories in industry, and focuses on the practical combination of them. It describes the challenges and benefits of merging these two software development trends and provides the reader with a novel approach and practical mechanisms to improve software development productivity.The book is aimed at engineers and students who wish to understand and apply software product lines

  11. Human driven transitions in complex model ecosystems

    Science.gov (United States)

    Harfoot, Mike; Newbold, Tim; Tittinsor, Derek; Purves, Drew

    2015-04-01

    Human activities have been observed to be impacting ecosystems across the globe, leading to reduced ecosystem functioning, altered trophic and biomass structure and ultimately ecosystem collapse. Previous attempts to understand global human impacts on ecosystems have usually relied on statistical models, which do not explicitly model the processes underlying the functioning of ecosystems, represent only a small proportion of organisms and do not adequately capture complex non-linear and dynamic responses of ecosystems to perturbations. We use a mechanistic ecosystem model (1), which simulates the underlying processes structuring ecosystems and can thus capture complex and dynamic interactions, to investigate boundaries of complex ecosystems to human perturbation. We explore several drivers including human appropriation of net primary production and harvesting of animal biomass. We also present an analysis of the key interactions between biotic, societal and abiotic earth system components, considering why and how we might think about these couplings. References: M. B. J. Harfoot et al., Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model., PLoS Biol. 12, e1001841 (2014).

  12. Feature-driven model-based segmentation

    Science.gov (United States)

    Qazi, Arish A.; Kim, John; Jaffray, David A.; Pekar, Vladimir

    2011-03-01

    The accurate delineation of anatomical structures is required in many medical image analysis applications. One example is radiation therapy planning (RTP), where traditional manual delineation is tedious, labor intensive, and can require hours of clinician's valuable time. Majority of automated segmentation methods in RTP belong to either model-based or atlas-based approaches. One substantial limitation of model-based segmentation is that its accuracy may be restricted by the uncertainties in image content, specifically when segmenting low-contrast anatomical structures, e.g. soft tissue organs in computed tomography images. In this paper, we introduce a non-parametric feature enhancement filter which replaces raw intensity image data by a high level probabilistic map which guides the deformable model to reliably segment low-contrast regions. The method is evaluated by segmenting the submandibular and parotid glands in the head and neck region and comparing the results to manual segmentations in terms of the volume overlap. Quantitative results show that we are in overall good agreement with expert segmentations, achieving volume overlap of up to 80%. Qualitatively, we demonstrate that we are able to segment low-contrast regions, which otherwise are difficult to delineate with deformable models relying on distinct object boundaries from the original image data.

  13. A bulk viscosity driven inflationary model

    International Nuclear Information System (INIS)

    Waga, I.; Falcao, R.C.; Chanda, R.

    1985-01-01

    Bulk viscosity associated with the production of heavy particles during the GUT phase transition can lead to exponential or 'generalized' inflation. The condition of inflation proposed is independent of the details of the phase transition and remains unaltered in presence of a cosmological constant. Such mechanism avoids the extreme supercooling and reheating needed in the usual inflationary models. The standard baryongenesis mechanism can be maintained. (Author) [pt

  14. Floquet prethermalization in the resonantly driven Hubbard model

    Science.gov (United States)

    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.

  15. Traffic-driven model of the World Wide Web graph

    OpenAIRE

    Barrat, Alain; Barthelemy, Marc; Vespignani, Alessandro

    2004-01-01

    We propose a model for the World Wide Web graph that couples the topological growth with the traffic's dynamical evolution. The model is based on a simple traffic-driven dynamics and generates weighted directed graphs exhibiting the statistical properties observed in the Web. In particular, the model yields a non-trivial time evolution of vertices and heavy-tail distributions for the topological and traffic properties. The generated graphs exhibit a complex architecture with a hierarchy of co...

  16. A Model-Driven Development Method for Management Information Systems

    Science.gov (United States)

    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.

  17. Modelling exciton–phonon interactions in optically driven quantum dots

    DEFF Research Database (Denmark)

    Nazir, Ahsan; McCutcheon, Dara

    2016-01-01

    We provide a self-contained review of master equation approaches to modelling phonon effects in optically driven self-assembled quantum dots. Coupling of the (quasi) two-level excitonic system to phonons leads to dissipation and dephasing, the rates of which depend on the excitation conditions...

  18. Traceability for Model Driven, Software Product Line Engineering

    NARCIS (Netherlands)

    Anquetil, N.; Grammel, B.; Galvao, I.; Noppen, J.A.R.; Shakil Khan, S.; Arboleda, H.; Rashid, A.; Garcia, A.

    Traceability is an important challenge for software organizations. This is true for traditional software development and even more so in new approaches that introduce more variety of artefacts such as Model Driven development or Software Product Lines. In this paper we look at some aspect of the

  19. Managing business compliance using model-driven security management

    Science.gov (United States)

    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.

  20. Validation of buoyancy driven spectral tensor model using HATS data

    DEFF Research Database (Denmark)

    Chougule, A.; Mann, Jakob; Kelly, Mark C.

    2016-01-01

    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...

  1. Construction of UML class diagram with Model-Driven Development

    Directory of Open Access Journals (Sweden)

    Tomasz Górski

    2016-03-01

    Full Text Available Model transformations play a key role in software development projects based on Model--Driven Development (MDD principles. Transformations allow for automation of repetitive and well-defined steps, thus shortening design time and reducing a number of errors. In the object-oriented approach, the key elements are use cases. They are described, modelled and later designed until executable application code is obtained. The aim of the paper is to present transformation of a model-to-model type, Communication-2-Class, which automates construction of Unified Modelling Language (UML class diagram in the context of the analysis/design model. An UML class diagram is created based on UML communication diagram within use case realization. As a result, a class diagram shows all of the classes involved in the use case realization and the relationships among them. The plug-in which implements Communication-2-Class transformation was implemented in the IBM Rational Software Architect. The article presents the tests results of developed plug-in, which realizes Communication-2-Class transformation, showing capabilities of shortening use case realization’s design time.[b]Keywords[/b]: Model-Driven Development, transformations, Unified Modelling Language, analysis/design model, UML class diagram, UML communication diagram

  2. Model-driven dependability assessment of software systems

    CERN Document Server

    Bernardi, Simona; Petriu, Dorina C

    2013-01-01

    In this book, the authors present cutting-edge model-driven techniques for modeling and analysis of software dependability. Most of them are based on the use of UML as software specification language. From the software system specification point of view, such techniques exploit the standard extension mechanisms of UML (i.e., UML profiling). UML profiles enable software engineers to add non-functional properties to the software model, in addition to the functional ones. The authors detail the state of the art on UML profile proposals for dependability specification and rigorously describe the t

  3. Semantic Model Driven Architecture Based Method for Enterprise Application Development

    Science.gov (United States)

    Wu, Minghui; Ying, Jing; Yan, Hui

    Enterprise applications have the requirements of meeting dynamic businesses processes and adopting lasted technologies flexibly, with to solve the problems caused by the nature of heterogeneous characteristic. Service-Oriented Architecture (SOA) is becoming a leading paradigm for business process integration. This research work focuses on business process modeling, proposes a semantic model-driven development method named SMDA combined with the Ontology and Model-Driven Architecture (MDA) technologies. The architecture of SMDA is presented in three orthogonal perspectives. (1) Vertical axis is the MDA 4 layers, the focus is UML profiles in M2 (meta-model layer) for ontology modeling, and three abstract levels: CIM, PIM and PSM modeling respectively. (2) Horizontal axis is different concerns involved in the development: Process, Application, Information, Organization, and Technology. (3) Traversal Axis is referred to aspects that have influence on other models of the cross-cutting axis: Architecture, Semantics, Aspect, and Pattern. The paper also introduces the modeling and transformation process in SMDA, and describes dynamic service composition supports briefly.

  4. Tag-Driven Online Novel Recommendation with Collaborative Item Modeling

    Directory of Open Access Journals (Sweden)

    Fenghuan Li

    2018-04-01

    Full Text Available Online novel recommendation recommends attractive novels according to the preferences and characteristics of users or novels and is increasingly touted as an indispensable service of many online stores and websites. The interests of the majority of users remain stable over a certain period. However, there are broad categories in the initial recommendation list achieved by collaborative filtering (CF. That is to say, it is very possible that there are many inappropriately recommended novels. Meanwhile, most algorithms assume that users can provide an explicit preference. However, this assumption does not always hold, especially in online novel reading. To solve these issues, a tag-driven algorithm with collaborative item modeling (TDCIM is proposed for online novel recommendation. Online novel reading is different from traditional book marketing and lacks preference rating. In addition, collaborative filtering frequently suffers from the Matthew effect, leading to ignored personalized recommendations and serious long tail problems. Therefore, item-based CF is improved by latent preference rating with a punishment mechanism based on novel popularity. Consequently, a tag-driven algorithm is constructed by means of collaborative item modeling and tag extension. Experimental results show that online novel recommendation is improved greatly by a tag-driven algorithm with collaborative item modeling.

  5. A model-driven approach to information security compliance

    Science.gov (United States)

    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.

  6. Formal Model-Driven Engineering: Generating Data and Behavioural Components

    Directory of Open Access Journals (Sweden)

    Chen-Wei Wang

    2012-12-01

    Full Text Available Model-driven engineering is the automatic production of software artefacts from abstract models of structure and functionality. By targeting a specific class of system, it is possible to automate aspects of the development process, using model transformations and code generators that encode domain knowledge and implementation strategies. Using this approach, questions of correctness for a complex, software system may be answered through analysis of abstract models of lower complexity, under the assumption that the transformations and generators employed are themselves correct. This paper shows how formal techniques can be used to establish the correctness of model transformations used in the generation of software components from precise object models. The source language is based upon existing, formal techniques; the target language is the widely-used SQL notation for database programming. Correctness is established by giving comparable, relational semantics to both languages, and checking that the transformations are semantics-preserving.

  7. Aspect-Oriented Model-Driven Software Product Line Engineering

    Science.gov (United States)

    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.

  8. Modelling of two-zone accelerator-driven systems

    Directory of Open Access Journals (Sweden)

    V. A. Babenko

    2012-09-01

    Full Text Available Neutron-physical modelings of two-zone subcritical reactor driven by high-intensity neutron generator are considered. The cascade principle in subcritical reactors, the use of which can hypothetically substantially amplify the neutron flux from the external source is discussed in this article. The theoretical preconditions of the cascade principle are discussed, and the directions of practical realization of the cascade subcritical system are considered, namely the possible methods of neutron feedback between reactor sections elimination. The results of Monte Carlo neutron-physical modeling of the cascade subcritical systems are presented and discussed.

  9. Data-driven stochastic modelling of zebrafish locomotion.

    Science.gov (United States)

    Zienkiewicz, Adam; Barton, David A W; Porfiri, Maurizio; di Bernardo, Mario

    2015-11-01

    In this work, we develop a data-driven modelling framework to reproduce the locomotion of fish in a confined environment. Specifically, we highlight the primary characteristics of the motion of individual zebrafish (Danio rerio), and study how these can be suitably encapsulated within a mathematical framework utilising a limited number of calibrated model parameters. Using data captured from individual zebrafish via automated visual tracking, we develop a model using stochastic differential equations and describe fish as a self propelled particle moving in a plane. Based on recent experimental evidence of the importance of speed regulation in social behaviour, we extend stochastic models of fish locomotion by introducing experimentally-derived processes describing dynamic speed regulation. Salient metrics are defined which are then used to calibrate key parameters of coupled stochastic differential equations, describing both speed and angular speed of swimming fish. The effects of external constraints are also included, based on experimentally observed responses. Understanding the spontaneous dynamics of zebrafish using a bottom-up, purely data-driven approach is expected to yield a modelling framework for quantitative investigation of individual behaviour in the presence of various external constraints or biological assays.

  10. Econophysics and Data Driven Modelling of Market Dynamics

    CERN Document Server

    Aoyama, Hideaki; Chakrabarti, Bikas; Chakraborti, Anirban; Ghosh, Asim; Econophysics and Data Driven Modelling of Market Dynamics

    2015-01-01

    This book presents the works and research findings of physicists, economists, mathematicians, statisticians, and financial engineers who have undertaken data-driven modelling of market dynamics and other empirical studies in the field of Econophysics. During recent decades, the financial market landscape has changed dramatically with the deregulation of markets and the growing complexity of products. The ever-increasing speed and decreasing costs of computational power and networks have led to the emergence of huge databases. The availability of these data should permit the development of models that are better founded empirically, and econophysicists have accordingly been advocating that one should rely primarily on the empirical observations in order to construct models and validate them. The recent turmoil in financial markets and the 2008 crash appear to offer a strong rationale for new models and approaches. The Econophysics community accordingly has an important future role to play in market modelling....

  11. A Model-Driven Framework to Develop Personalized Health Monitoring

    Directory of Open Access Journals (Sweden)

    Algimantas Venčkauskas

    2016-07-01

    Full Text Available Both distributed healthcare systems and the Internet of Things (IoT are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient’s data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues.

  12. Model Driven Development of Simulation Models : Defining and Transforming Conceptual Models into Simulation Models by Using Metamodels and Model Transformations

    NARCIS (Netherlands)

    Küçükkeçeci Çetinkaya, D.

    2013-01-01

    Modeling and simulation (M&S) is an effective method for analyzing and designing systems and it is of interest to scientists and engineers from all disciplines. This thesis proposes the application of a model driven software development approach throughout the whole set of M&S activities and it

  13. Optimizing Computing Platforms for Climate-Driven Ecological Forecasting Models

    Science.gov (United States)

    Farley, S. S.; Williams, J. W.

    2016-12-01

    Species distribution models are widely used, climate-driven ecological forecasting tools that use machine-learning techniques to predict species range shifts and ecological responses to 21st century climate change. As high-resolution modern and fossil biodiversity data becomes increasingly available and statistical learning methods become more computationally intensive, choosing the correct computing configuration on which to run these models becomes more important. With a variety of low-cost cloud and desktop computing options available, users of forecasting models must balance performance gains achieved by provisioning more powerful hardware with the cost of using these resources. We present a framework for estimating the optimal computing solution for a given modeling activity. We argue that this framework is capable of identifying the optimal computing solution - the one that maximizes model accuracy while minimizing resource cost and computing time. Our framework is built on constituent models of algorithm execution time, predictive skill, and computing cost. We demonstrate the results of the framework using four leading species distribution models: multivariate adaptive regression splines, generalized additive models, support vector machines, and boosted regression trees. The constituent models themselves are shown to have high predictive accuracy, and can be used independently to estimate the effects of using larger input datasets, such as those that incorporate data from the fossil record. When used together, our framework shows highly significant predictive ability, and is designed to be used by researchers to inform future computing provisioning strategies.

  14. Hybrid models for hydrological forecasting: Integration of data-driven and conceptual modelling techniques

    NARCIS (Netherlands)

    Corzo Perez, G.A.

    2009-01-01

    This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following

  15. Hybrid models for hydrological forecasting : Integration of data-driven and conceptual modelling techniques

    NARCIS (Netherlands)

    Corzo Perez, G.A.

    2009-01-01

    This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following

  16. Model-Driven Approach for Body Area Network Application Development

    Directory of Open Access Journals (Sweden)

    Algimantas Venčkauskas

    2016-05-01

    Full Text Available This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS. We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

  17. A Model-driven Framework for Educational Game Design

    Directory of Open Access Journals (Sweden)

    Bill Roungas

    2016-09-01

    Full Text Available Educational games are a class of serious games whose main purpose is to teach some subject to their players. Despite the many existing design frameworks, these games are too often created in an ad-hoc manner, and typically without the use of a game design document (GDD. We argue that a reason for this phenomenon is that current ways to structure, create and update GDDs do not increase the value of the artifact in the design and development process. As a solution, we propose a model-driven, web-based knowledge management environment that supports game designers in the creation of a GDD that accounts for and relates educational and entertainment game elements. The foundation of our approach is our devised conceptual model for educational games, which also defines the structure of the design environment. We present promising results from an evaluation of our environment with eight experts in serious games.

  18. Damped trophic cascades driven by fishing in model marine ecosystems

    DEFF Research Database (Denmark)

    Andersen, Ken Haste; Pedersen, Martin

    2010-01-01

    The largest perturbation on upper trophic levels of many marine ecosystems stems from fishing. The reaction of the ecosystem goes beyond the trophic levels directly targeted by the fishery. This reaction has been described either as a change in slope of the overall size spectrum or as a trophic...... cascade triggered by the removal of top predators. Here we use a novel size- and trait-based model to explore how marine ecosystems might react to perturbations from different types of fishing pressure. The model explicitly resolves the whole life history of fish, from larvae to adults. The results show...... that fishing does not change the overall slope of the size spectrum, but depletes the largest individuals and induces trophic cascades. A trophic cascade can propagate both up and down in trophic levels driven by a combination of changes in predation mortality and food limitation. The cascade is damped...

  19. Data-driven non-Markovian closure models

    Science.gov (United States)

    Kondrashov, Dmitri; Chekroun, Mickaël D.; Ghil, Michael

    2015-03-01

    This paper has two interrelated foci: (i) obtaining stable and efficient data-driven closure models by using a multivariate time series of partial observations from a large-dimensional system; and (ii) comparing these closure models with the optimal closures predicted by the Mori-Zwanzig (MZ) formalism of statistical physics. Multilayer stochastic models (MSMs) are introduced as both a generalization and a time-continuous limit of existing multilevel, regression-based approaches to closure in a data-driven setting; these approaches include empirical model reduction (EMR), as well as more recent multi-layer modeling. It is shown that the multilayer structure of MSMs can provide a natural Markov approximation to the generalized Langevin equation (GLE) of the MZ formalism. A simple correlation-based stopping criterion for an EMR-MSM model is derived to assess how well it approximates the GLE solution. Sufficient conditions are derived on the structure of the nonlinear cross-interactions between the constitutive layers of a given MSM to guarantee the existence of a global random attractor. This existence ensures that no blow-up can occur for a broad class of MSM applications, a class that includes non-polynomial predictors and nonlinearities that do not necessarily preserve quadratic energy invariants. The EMR-MSM methodology is first applied to a conceptual, nonlinear, stochastic climate model of coupled slow and fast variables, in which only slow variables are observed. It is shown that the resulting closure model with energy-conserving nonlinearities efficiently captures the main statistical features of the slow variables, even when there is no formal scale separation and the fast variables are quite energetic. Second, an MSM is shown to successfully reproduce the statistics of a partially observed, generalized Lotka-Volterra model of population dynamics in its chaotic regime. The challenges here include the rarity of strange attractors in the model's parameter

  20. A dynamic, climate-driven model of Rift Valley fever

    Directory of Open Access Journals (Sweden)

    Joseph Leedale

    2016-03-01

    Full Text Available Outbreaks of Rift Valley fever (RVF in eastern Africa have previously occurred following specific rainfall dynamics and flooding events that appear to support the emergence of large numbers of mosquito vectors. As such, transmission of the virus is considered to be sensitive to environmental conditions and therefore changes in climate can impact the spatiotemporal dynamics of epizootic vulnerability. Epidemiological information describing the methods and parameters of RVF transmission and its dependence on climatic factors are used to develop a new spatio-temporal mathematical model that simulates these dynamics and can predict the impact of changes in climate. The Liverpool RVF (LRVF model is a new dynamic, process-based model driven by climate data that provides a predictive output of geographical changes in RVF outbreak susceptibility as a result of the climate and local livestock immunity. This description of the multi-disciplinary process of model development is accessible to mathematicians, epidemiological modellers and climate scientists, uniting dynamic mathematical modelling, empirical parameterisation and state-of-the-art climate information.

  1. Analysis of Intelligent Transportation Systems Using Model-Driven Simulations

    Directory of Open Access Journals (Sweden)

    Alberto Fernández-Isabel

    2015-06-01

    Full Text Available Intelligent Transportation Systems (ITSs integrate information, sensor, control, and communication technologies to provide transport related services. Their users range from everyday commuters to policy makers and urban planners. Given the complexity of these systems and their environment, their study in real settings is frequently unfeasible. Simulations help to address this problem, but present their own issues: there can be unintended mistakes in the transition from models to code; their platforms frequently bias modeling; and it is difficult to compare works that use different models and tools. In order to overcome these problems, this paper proposes a framework for a model-driven development of these simulations. It is based on a specific modeling language that supports the integrated specification of the multiple facets of an ITS: people, their vehicles, and the external environment; and a network of sensors and actuators conveniently arranged and distributed that operates over them. The framework works with a model editor to generate specifications compliant with that language, and a code generator to produce code from them using platform specifications. There are also guidelines to help researchers in the application of this infrastructure. A case study on advanced management of traffic lights with cameras illustrates its use.

  2. Modeling pressure-driven assembly of polymer coated nanoparticles

    Science.gov (United States)

    Lane, J. Matthew D.; Salerno, K. Michael; Grest, Gary S.; Fan, Hongyou

    2017-06-01

    High-pressure experiments have successfully produced a variety of gold nanostructures by compressing polymer coated spherical nanoparticles. We apply atomistic simulation to understand the role of the soft polymer response in determining the pressure-driven assembly of gold nanostructures. Quasi-isentropic experiments have shown that 1D, 2D and 3D nanostructures can be formed and recovered from dynamic compression of fcc superlattices of alkanethiol-coated gold nanocrystals on Sandia's Veloce pulsed power accelerator. Molecular modeling has shown that the dimensionality of the final structures depends on the orientation of the superlattice and the uniaxial loading. We describe the role of coating ligand length and grafting density, on ligand migration and deformation processes during pressure-driven coalescence of the cores into permanent nanowires, nanosheets and 3D structures. The role of uniaxial vs isotropic pressure and the effects of compression along various superlattice orientations will be discussed. Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

  3. Conformon-driven biopolymer shape changes in cell modeling.

    Science.gov (United States)

    Ji, Sungchul; Ciobanu, Gabriel

    2003-07-01

    Conceptual models of the atom preceded the mathematical model of the hydrogen atom in physics in the second decade of the 20th century. The computer modeling of the living cell in the 21st century may follow a similar course of development. A conceptual model of the cell called the Bhopalator was formulated in the mid-1980s, along with its twin theories known as the conformon theory of molecular machines and the cell language theory of biopolymer interactions [Ann. N.Y. Acad. Sci. 227 (1974) 211; BioSystems 44 (1997) 17; Ann. N.Y. Acad. Sci. 870 (1999a) 411; BioSystems 54 (2000) 107; Semiotica 138 (1-4) (2002a) 15; Fundamenta Informaticae 49 (2002b) 147]. The conformon theory accounts for the reversible actions of individual biopolymers coupled to irreversible chemical reactions, while the cell language theory provides a theoretical framework for understanding the complex networks of dynamic interactions among biopolymers in the cell. These two theories are reviewed and further elaborated for the benefit of both computational biologists and computer scientists who are interested in modeling the living cell and its functions. One of the critical components of the mechanisms of cell communication and cell computing has been postulated to be space- and time-organized teleonomic (i.e. goal-directed) shape changes of biopolymers that are driven by exergonic (free energy-releasing) chemical reactions. The generalized Franck-Condon principle is suggested to be essential in resolving the apparent paradox arising when one attempts to couple endergonic (free energy-requiring) biopolymer shape changes to the exergonic chemical reactions that are catalyzed by biopolymer shape changes themselves. Conformons, defined as sequence-specific mechanical strains of biopolymers first invoked three decades ago to account for energy coupling in mitochondria, have been identified as shape changers, the agents that cause shape changes in biopolymers. Given a set of space- and time

  4. Data-driven approach to dynamic visual attention modelling

    Science.gov (United States)

    Culibrk, Dubravko; Sladojevic, Srdjan; Riche, Nicolas; Mancas, Matei; Crnojevic, Vladimir

    2012-06-01

    Visual attention deployment mechanisms allow the Human Visual System to cope with an overwhelming amount of visual data by dedicating most of the processing power to objects of interest. The ability to automatically detect areas of the visual scene that will be attended to by humans is of interest for a large number of applications, from video coding, video quality assessment to scene understanding. Due to this fact, visual saliency (bottom-up attention) models have generated significant scientific interest in recent years. Most recent work in this area deals with dynamic models of attention that deal with moving stimuli (videos) instead of traditionally used still images. Visual saliency models are usually evaluated against ground-truth eye-tracking data collected from human subjects. However, there are precious few recently published approaches that try to learn saliency from eyetracking data and, to the best of our knowledge, no approaches that try to do so when dynamic saliency is concerned. The paper attempts to fill this gap and describes an approach to data-driven dynamic saliency model learning. A framework is proposed that enables the use of eye-tracking data to train an arbitrary machine learning algorithm, using arbitrary features derived from the scene. We evaluate the methodology using features from a state-of-the art dynamic saliency model and show how simple machine learning algorithms can be trained to distinguish between visually salient and non-salient parts of the scene.

  5. Requirements traceability in model-driven development: Applying model and transformation conformance

    NARCIS (Netherlands)

    Andrade Almeida, João; Iacob, Maria Eugenia; van Eck, Pascal

    The variety of design artifacts (models) produced in a model-driven design process results in an intricate relationship between requirements and the various models. This paper proposes a methodological framework that simplifies management of this relationship, which helps in assessing the quality of

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

    OpenAIRE

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

    2012-01-01

    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 concen...

  7. Polarity-Driven Geometrical Cluster Growth Model of Budding Yeast

    Science.gov (United States)

    Cabral, Reniel B.; Lim, May T.

    We present a polarity-driven activator-inhibitor model of budding yeast in a two-dimensional medium wherein impeding metabolites secretion (or growth inhibitors) and growth directionality are determined by the local nutrient level. We found that colony size and morphological features varied with nutrient concentration. A branched-type morphology is associated with high impeding metabolite concentration together with a high fraction of distal budding, while opposite conditions (low impeding metabolite concentration, high fraction of proximal budding) promote Eden-type patterns. Increasing the anisotropy factor (or polarity) produced other spatial patterns akin to the electrical breakdown under varying electric field. Rapid changes in the colony morphology, which we conjecture to be equivalent to a transition from an inactive quiescent state to an active budding state, appeared when nutrients were limited.

  8. Suitability of Modern Software Development Methodologies for Model Driven Development

    Directory of Open Access Journals (Sweden)

    Ruben Picek

    2009-12-01

    Full Text Available As an answer to today’s growing challenges in software industry, wide spectrum of new approaches of software development has occurred. One prominent direction is currently most promising software development paradigm called Model Driven Development (MDD. Despite a lot of skepticism and problems, MDD paradigm is being used and improved to accomplish many inherent potential benefits. In the methodological approach of software development it is necessary to use some kind of development process. Modern methodologies can be classified into two main categories: formal or heavyweight and agile or lightweight. But when it is a question about MDD and development process for MDD, currently known methodologies are very poor or better said they don't have any explanation of MDD process. As the result of research, in this paper, author examines the possibilities of using existing modern software methodologies in context of MDD paradigm.

  9. Data-Driven Modeling and Prediction of Arctic Sea Ice

    Science.gov (United States)

    Kondrashov, Dmitri; Chekroun, Mickael; Ghil, Michael

    2016-04-01

    We present results of data-driven predictive analyses of sea ice over the main Arctic regions. Our approach relies on the Multilayer Stochastic Modeling (MSM) framework of Kondrashov, Chekroun and Ghil [Physica D, 2015] and it leads to probabilistic prognostic models of sea ice concentration (SIC) anomalies on seasonal time scales. This approach is applied to monthly time series of state-of-the-art data-adaptive decompositions of SIC and selected climate variables over the Arctic. We evaluate the predictive skill of MSM models by performing retrospective forecasts with "no-look ahead" for up to 6-months ahead. It will be shown in particular that the memory effects included intrinsically in the formulation of our non-Markovian MSM models allow for improvements of the prediction skill of large-amplitude SIC anomalies in certain Arctic regions on the one hand, and of September Sea Ice Extent, on the other. Further improvements allowed by the MSM framework will adopt a nonlinear formulation and explore next-generation data-adaptive decompositions, namely modification of Principal Oscillation Patterns (POPs) and rotated Multichannel Singular Spectrum Analysis (M-SSA).

  10. Space Weather Forecasts Driven by the ADAPT Model

    Science.gov (United States)

    Henney, C. J.; Arge, C. N.; Shurkin, K.; Schooley, A. K.; Hock, R. A.; White, S.

    2015-12-01

    In this presentation, we highlight recent progress to forecast key space weather parameters with the ADAPT (Air Force Data Assimilative Photospheric flux Transport) model. Driven by a magnetic flux transport model, ADAPT evolves global solar magnetic maps forward 1 to 7 days in the future to provide realistic estimates of the solar near-side field distribution used to forecast the solar wind, F10.7 (i.e., the solar 10.7 cm radio flux), extreme ultraviolet (EUV) and far ultraviolet (FUV) irradiance. Input to the ADAPT model includes solar near-side estimates of the inferred photospheric magnetic field from space-based (i.e., HMI) and ground-based (e.g., GONG & VSM) instruments. We summarize the recent findings that: 1) the sum of the absolute value of strong magnetic fields, associated with sunspots, is shown to correlate well with the observed daily F10.7 variability (Henney et al. 2012); and 2) the sum of the absolute value of weak magnetic fields, associated with plage regions, is shown to correlate well with EUV and FUV irradiance variability (Henney et al. 2015). In addition, recent progress to utilize the ADAPT global maps as input to the Wang-Sheeley-Arge (WSA) coronal and solar wind model is presented. We also discuss the challenges of observing less than half of the solar surface at any given time and the need for future magnetograph instruments near L1 and L5.

  11. A Module-System Discipline for Model-Driven Software Development

    NARCIS (Netherlands)

    Erdweg, S.T.; Ostermann, Klaus

    2017-01-01

    Model-driven development is a pragmatic approach to software development that embraces domain-specific languages (DSLs), where models correspond to DSL programs. A distinguishing feature of model-driven development is that clients of a model can select from an open set of alternative semantics of

  12. Simulation of Road Traffic Applying Model-Driven Engineering

    Directory of Open Access Journals (Sweden)

    Alberto FERNÁNDEZ-ISABEL

    2016-05-01

    Full Text Available Road traffic is an important phenomenon in modern societies. The study of its different aspects in the multiple scenarios where it happens is relevant for a huge number of problems. At the same time, its scale and complexity make it hard to study. Traffic simulations can alleviate these difficulties, simplifying the scenarios to consider and controlling their variables. However, their development also presents difficulties. The main ones come from the need to integrate the way of working of researchers and developers from multiple fields. Model-Driven Engineering (MDE addresses these problems using Modelling Languages (MLs and semi-automatic transformations to organise and describe the development, from requirements to code. This paper presents a domain-specific MDE framework for simulations of road traffic. It comprises an extensible ML, support tools, and development guidelines. The ML adopts an agent-based approach, which is focused on the roles of individuals in road traffic and their decision-making. A case study shows the process to model a traffic theory with the ML, and how to specialise that specification for an existing target platform and its simulations. The results are the basis for comparison with related work.

  13. Model-driven Privacy Assessment in the Smart Grid

    Energy Technology Data Exchange (ETDEWEB)

    Knirsch, Fabian [Salzburg Univ. (Austria); Engel, Dominik [Salzburg Univ. (Austria); Neureiter, Christian [Salzburg Univ. (Austria); Frincu, Marc [Univ. of Southern California, Los Angeles, CA (United States); Prasanna, Viktor [Univ. of Southern California, Los Angeles, CA (United States)

    2015-02-09

    In a smart grid, data and information are transported, transmitted, stored, and processed with various stakeholders having to cooperate effectively. Furthermore, personal data is the key to many smart grid applications and therefore privacy impacts have to be taken into account. For an effective smart grid, well integrated solutions are crucial and for achieving a high degree of customer acceptance, privacy should already be considered at design time of the system. To assist system engineers in early design phase, frameworks for the automated privacy evaluation of use cases are important. For evaluation, use cases for services and software architectures need to be formally captured in a standardized and commonly understood manner. In order to ensure this common understanding for all kinds of stakeholders, reference models have recently been developed. In this paper we present a model-driven approach for the automated assessment of such services and software architectures in the smart grid that builds on the standardized reference models. The focus of qualitative and quantitative evaluation is on privacy. For evaluation, the framework draws on use cases from the University of Southern California microgrid.

  14. Product Data Model for Performance-driven Design

    Science.gov (United States)

    Hu, Guang-Zhong; Xu, Xin-Jian; Xiao, Shou-Ne; Yang, Guang-Wu; Pu, Fan

    2017-09-01

    When designing large-sized complex machinery products, the design focus is always on the overall performance; however, there exist no design theory and method based on performance driven. In view of the deficiency of the existing design theory, according to the performance features of complex mechanical products, the performance indices are introduced into the traditional design theory of "Requirement-Function-Structure" to construct a new five-domain design theory of "Client Requirement-Function-Performance-Structure-Design Parameter". To support design practice based on this new theory, a product data model is established by using performance indices and the mapping relationship between them and the other four domains. When the product data model is applied to high-speed train design and combining the existing research result and relevant standards, the corresponding data model and its structure involving five domains of high-speed trains are established, which can provide technical support for studying the relationships between typical performance indices and design parameters and the fast achievement of a high-speed train scheme design. The five domains provide a reference for the design specification and evaluation criteria of high speed train and a new idea for the train's parameter design.

  15. Authoring and verification of clinical guidelines: a model driven approach.

    Science.gov (United States)

    Pérez, Beatriz; Porres, Ivan

    2010-08-01

    The goal of this research is to provide a framework to enable authoring and verification of clinical guidelines. The framework is part of a larger research project aimed at improving the representation, quality and application of clinical guidelines in daily clinical practice. The verification process of a guideline is based on (1) model checking techniques to verify guidelines against semantic errors and inconsistencies in their definition, (2) combined with Model Driven Development (MDD) techniques, which enable us to automatically process manually created guideline specifications and temporal-logic statements to be checked and verified regarding these specifications, making the verification process faster and cost-effective. Particularly, we use UML statecharts to represent the dynamics of guidelines and, based on this manually defined guideline specifications, we use a MDD-based tool chain to automatically process them to generate the input model of a model checker. The model checker takes the resulted model together with the specific guideline requirements, and verifies whether the guideline fulfils such properties. The overall framework has been implemented as an Eclipse plug-in named GBDSSGenerator which, particularly, starting from the UML statechart representing a guideline, allows the verification of the guideline against specific requirements. Additionally, we have established a pattern-based approach for defining commonly occurring types of requirements in guidelines. We have successfully validated our overall approach by verifying properties in different clinical guidelines resulting in the detection of some inconsistencies in their definition. The proposed framework allows (1) the authoring and (2) the verification of clinical guidelines against specific requirements defined based on a set of property specification patterns, enabling non-experts to easily write formal specifications and thus easing the verification process. Copyright 2010 Elsevier Inc

  16. Dependencies between models in the model-driven design of distributed applications

    NARCIS (Netherlands)

    Andrade Almeida, João; Bevinoppa, S.; Ferreira Pires, Luis; van Sinderen, Marten J.; Hammoudi, S.

    2005-01-01

    In our previous work, we have defined a model-driven design approach based on the organization of models of a distributed application according to different levels of platform-independence. In our approach, the design process is structured into a preparation and an execution phase. In the

  17. EXPLORING DATA-DRIVEN SPECTRAL MODELS FOR APOGEE M DWARFS

    Science.gov (United States)

    Lua Birky, Jessica; Hogg, David; Burgasser, Adam J.; Jessica Birky

    2018-01-01

    The Cannon (Ness et al. 2015; Casey et al. 2016) is a flexible, data-driven spectral modeling and parameter inference framework, demonstrated on high-resolution Apache Point Galactic Evolution Experiment (APOGEE; λ/Δλ~22,500, 1.5-1.7µm) spectra of giant stars to estimate stellar labels (Teff, logg, [Fe/H], and chemical abundances) to precisions higher than the model-grid pipeline. The lack of reliable stellar parameters reported by the APOGEE pipeline for temperatures less than ~3550K, motivates extension of this approach to M dwarf stars. Using a training set of 51 M dwarfs with spectral types ranging M0-M9 obtained from SDSS optical spectra, we demonstrate that the Cannon can infer spectral types to a precision of +/-0.6 types, making it an effective tool for classifying high-resolution near-infrared spectra. We discuss the potential for extending this work to determine the physical stellar labels Teff, logg, and [Fe/H].This work is supported by the SDSS Faculty and Student (FAST) initiative.

  18. Forecasting wind-driven wildfires using an inverse modelling approach

    Directory of Open Access Journals (Sweden)

    O. Rios

    2014-06-01

    Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

  19. A question driven socio-hydrological modeling process

    Science.gov (United States)

    Garcia, M.; Portney, K.; Islam, S.

    2016-01-01

    Human and hydrological systems are coupled: human activity impacts the hydrological cycle and hydrological conditions can, but do not always, trigger changes in human systems. Traditional modeling approaches with no feedback between hydrological and human systems typically cannot offer insight into how different patterns of natural variability or human-induced changes may propagate through this coupled system. Modeling of coupled human-hydrological systems, also called socio-hydrological systems, recognizes the potential for humans to transform hydrological systems and for hydrological conditions to influence human behavior. However, this coupling introduces new challenges and existing literature does not offer clear guidance regarding model conceptualization. There are no universally accepted laws of human behavior as there are for the physical systems; furthermore, a shared understanding of important processes within the field is often used to develop hydrological models, but there is no such consensus on the relevant processes in socio-hydrological systems. Here we present a question driven process to address these challenges. Such an approach allows modeling structure, scope and detail to remain contingent on and adaptive to the question context. We demonstrate the utility of this process by revisiting a classic question in water resources engineering on reservoir operation rules: what is the impact of reservoir operation policy on the reliability of water supply for a growing city? Our example model couples hydrological and human systems by linking the rate of demand decreases to the past reliability to compare standard operating policy (SOP) with hedging policy (HP). The model shows that reservoir storage acts both as a buffer for variability and as a delay triggering oscillations around a sustainable level of demand. HP reduces the threshold for action thereby decreasing the delay and the oscillation effect. As a result, per capita demand decreases during

  20. Modelling and simulation of surface morphology driven by ion bombardment

    Energy Technology Data Exchange (ETDEWEB)

    Yewande, E.O.

    2006-05-02

    Non-equilibrium surfaces, at nanometer length scales, externally driven via bombardment with energetic particles are known to exhibit well ordered patterns with a variety of applications in nano-technology. These patterns emerge at time scales on the order of minutes. Continuum theory has been quite successful in giving a general picture of the processes that interplay to give the observed patterns, as well as how such competition might determine the properties of the nanostructures. However, continuum theoretical descriptions are ideal only in the asymptotic limit. The only other theoretical alternative, which happens to be more suitable for the characteristic length-and time-scales of pattern formation, is Monte Carlo simulation. In this thesis, surface morphology is studied using discrete solid-on-solid Monte Carlo models of sputtering and surface diffusion. The simulations are performed in the context of the continuum theories and experiments. In agreement with the experiments, the ripples coarsen with time and the ripple velocity exhibits a power-law behaviour with the ripple wavelength, in addition, the exponent was found to depend on the simulation temperature, which suggests future experimental studies of flux dependence. Moreover, a detailed exploration of possible topographies, for different sputtering conditions, corresponding to different materials, was performed. And different surface topographies e.g. holes, ripples, and dots, were found at oblique incidence, without sample rotation. With sample rotation no new topography was found, its only role being to destroy any inherent anisotropy in the system. (orig.)

  1. Modelling and simulation of surface morphology driven by ion bombardment

    International Nuclear Information System (INIS)

    Yewande, E.O.

    2006-01-01

    Non-equilibrium surfaces, at nanometer length scales, externally driven via bombardment with energetic particles are known to exhibit well ordered patterns with a variety of applications in nano-technology. These patterns emerge at time scales on the order of minutes. Continuum theory has been quite successful in giving a general picture of the processes that interplay to give the observed patterns, as well as how such competition might determine the properties of the nanostructures. However, continuum theoretical descriptions are ideal only in the asymptotic limit. The only other theoretical alternative, which happens to be more suitable for the characteristic length-and time-scales of pattern formation, is Monte Carlo simulation. In this thesis, surface morphology is studied using discrete solid-on-solid Monte Carlo models of sputtering and surface diffusion. The simulations are performed in the context of the continuum theories and experiments. In agreement with the experiments, the ripples coarsen with time and the ripple velocity exhibits a power-law behaviour with the ripple wavelength, in addition, the exponent was found to depend on the simulation temperature, which suggests future experimental studies of flux dependence. Moreover, a detailed exploration of possible topographies, for different sputtering conditions, corresponding to different materials, was performed. And different surface topographies e.g. holes, ripples, and dots, were found at oblique incidence, without sample rotation. With sample rotation no new topography was found, its only role being to destroy any inherent anisotropy in the system. (orig.)

  2. Kinetic Modeling of Temperature Driven Flows in Short Microchannels

    National Research Council Canada - National Science Library

    Alexeenko, Alina A; Muntz, E. P; Gimelshein, Sergey F; Ketsdever, Andrew D

    2005-01-01

    The temperature driven gas flow in a two-dimensional finite length microchannel and a cylindrical tube are studied numerically with the goal of performance optimization of a nanomembrane-based Knudsen compressor...

  3. Nonspherical Radiation Driven Wind Models Applied to Be Stars

    Science.gov (United States)

    Arauxo, F. X.

    1990-11-01

    ABSTRACT. In this work we present a model for the structure of a radiatively driven wind in the meridional plane of a hot star. Rotation effects and simulation of viscous forces were included in the motion equations. The line radiation force is considered with the inclusion of the finite disk correction in self-consistent computations which also contain gravity darkening as well as distortion of the star by rotation. An application to a typical BlV star leads to mass-flux ratios between equator and pole of the order of 10 and mass loss rates in the range 5.l0 to Mo/yr. Our envelope models are flattened towards the equator and the wind terminal velocities in that region are rather high (1000 Km/s). However, in the region near the star the equatorial velocity field is dominated by rotation. RESUMEN. Se presenta un modelo de la estructura de un viento empujado radiativamente en el plano meridional de una estrella caliente. Se incluyeron en las ecuaciones de movimiento los efectos de rotaci6n y la simulaci6n de fuerzas viscosas. Se consider6 la fuerza de las lineas de radiaci6n incluyendo la correcci6n de disco finito en calculos autoconsistentes los cuales incluyen oscurecimiento gravitacional asi como distorsi6n de la estrella por rotaci6n. La aplicaci6n a una estrella tipica BlV lleva a cocientes de flujo de masa entre el ecuador y el polo del orden de 10 de perdida de masa en el intervalo 5.l0 a 10 Mo/ano. Nuestros modelos de envolvente estan achatados hacia el ecuador y las velocidads terminales del viento en esa regi6n son bastante altas (1000 Km/s). Sin embargo, en la regi6n cercana a la estrella el campo de velocidad ecuatorial esta dominado por la rotaci6n. Key words: STARS-BE -- STARS-WINDS

  4. Beginning SQL Server Modeling Model-driven Application Development in SQL Server

    CERN Document Server

    Weller, Bart

    2010-01-01

    Get ready for model-driven application development with SQL Server Modeling! This book covers Microsoft's SQL Server Modeling (formerly known under the code name "Oslo") in detail and contains the information you need to be successful with designing and implementing workflow modeling. Beginning SQL Server Modeling will help you gain a comprehensive understanding of how to apply DSLs and other modeling components in the development of SQL Server implementations. Most importantly, after reading the book and working through the examples, you will have considerable experience using SQL M

  5. Comparing Transformation Possibilities of Topological Functioning Model and BPMN in the Context of Model Driven Architecture

    Directory of Open Access Journals (Sweden)

    Solomencevs Artūrs

    2016-05-01

    Full Text Available The approach called “Topological Functioning Model for Software Engineering” (TFM4SE applies the Topological Functioning Model (TFM for modelling the business system in the context of Model Driven Architecture. TFM is a mathematically formal computation independent model (CIM. TFM4SE is compared to an approach that uses BPMN as a CIM. The comparison focuses on CIM modelling and on transformation to UML Sequence diagram on the platform independent (PIM level. The results show the advantages and drawbacks the formalism of TFM brings into the development.

  6. EMG-driven models of human-machine interaction in individuals wearing the H2 exoskeleton

    NARCIS (Netherlands)

    Durandau, Guillaume; Sartori, Massimo; Bortole, Magdo; Moreno, Juan C.; Pons, José L.; Farina, Dario

    2016-01-01

    EMG-driven modeling has been mostly used offline and on powerful desktop computers, limiting the application of this technique to neurorehabilitation settings. In this paper, we demonstrate the use of EMG-driven modeling in online (i.e. in real-time) running on a fully portable embedded system and

  7. Efficient and Accurate Log-Levy Approximations of Levy-Driven LIBOR Models

    DEFF Research Database (Denmark)

    Papapantoleon, Antonis; Schoenmakers, John; Skovmand, David

    2012-01-01

    The LIBOR market model is very popular for pricing interest rate derivatives but is known to have several pitfalls. In addition, if the model is driven by a jump process, then the complexity of the drift term grows exponentially fast (as a function of the tenor length). We consider a Lévy-driven ...

  8. Hydrography-driven coarsening of grid digital elevation models

    Science.gov (United States)

    Moretti, G.; Orlandini, S.

    2017-12-01

    A new grid coarsening strategy, denoted as hydrography-driven (HD) coarsening, is developed in the present study. The HD coarsening strategy is designed to retain the essential hydrographic features of surface flow paths observed in high-resolution digital elevation models (DEMs): (1) depressions are filled in the considered high-resolution DEM, (2) the obtained topographic data are used to extract a reference grid network composed of all surface flow paths, (3) the Horton order is assigned to each link of the reference grid network, and (4) within each coarse grid cell, the elevation of the point lying along the highest-order path of the reference grid network and displaying the minimum distance to the cell center is assigned to this coarse grid cell center. The capabilities of the HD coarsening strategy to provide consistent surface flow paths with respect to those observed in high-resolution DEMs are evaluated over a synthetic valley and two real drainage basins located in the Italian Alps and in the Italian Apennines. The HD coarsening is found to yield significantly more accurate surface flow path profiles than the standard nearest neighbor (NN) coarsening. In addition, the proposed strategy is found to reduce drastically the impact of depression-filling procedures in coarsened topographic data. The HD coarsening strategy is therefore advocated for all those cases in which the relief of the extracted drainage network is an important hydrographic feature. The figure below reports DEMs of a synthetic valley and extracted surface flow paths. (a) 10-m grid DEM displaying no depressions and extracted surface flow path (gray line). (b) 1-km grid DEM obtained from NN coarsening. (c) 1-km grid DEM obtained from NN coarsening plus depression-filling and extracted surface flow path (light blue line). (d) 1-km grid DEM obtained from HD coarsening and extracted surface flow path (magenta line).

  9. Proceedings of the Workshop on Models and Model-driven Methods for Enterprise Computing (3M4EC 2008)

    NARCIS (Netherlands)

    van Sinderen, Marten J.; Andrade Almeida, João; Ferreira Pires, Luis; Steen, M.

    2008-01-01

    Recent developments in metamodeling and model transformation techniques have led to increasing adoption of model-driven engineering practices. The increase in interest and significance of the model-driven approach has also accelerated its application in the development of large (distributed) IT

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

    Science.gov (United States)

    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

  11. Data–driven modeling of nano-nose gas sensor arrays

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård

    2010-01-01

    We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence...... the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state...

  12. Data-driven modeling of nano-nose gas sensor arrays

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård

    2010-01-01

    We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence...... the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state...

  13. A new solar wind-driven global dynamic plasmapause model: 2. Model and validation

    Science.gov (United States)

    He, Fei; Zhang, Xiao-Xin; Lin, Rui-Lin; Fok, Mei-Ching; Katus, Roxanne M.; Liemohn, Mike W.; Gallagher, Dennis L.; Nakano, Shinya

    2017-07-01

    A new solar wind-driven global dynamic plasmapause (NSW-GDP) model has been constructed based on the largest currently available database containing 49,119 plasmapause crossing locations and 3957 plasmapause profiles (corresponding to 48,899 plasmapause locations), from 18 satellites during 1977-2015 covering four solar cycles. This model is compiled by the Levenberg-Marquardt method for nonlinear multiparameter fitting and parameterized by VSW, BZ, SYM-H, and AE. Continuous and smooth magnetic local time dependence controlled mainly by the solar wind-driven convection electric field ESW is also embedded in this model. Compared with previous empirical models based on our database, this new model improves the forecasting accuracy and capability for the global plasmapause. The diurnal, seasonal, and solar cycle variations of the plasmapause can be captured by the new model. The NSW-GDP model can potentially be used to forecast the global plasmapause shape with upstream solar wind and interplanetary magnetic field parameters and corresponding predicted values of SYM-H and AE and can also be used as input parameters for other inner magnetospheric coupling models, such as dynamic radiation belt and ring current models and even MHD models.

  14. The Arizona Universities Library Consortium patron-driven e-book model

    Directory of Open Access Journals (Sweden)

    Jeanne Richardson

    2013-03-01

    Full Text Available Building on Arizona State University's patron-driven acquisitions (PDA initiative in 2009, the Arizona Universities Library Consortium, in partnership with the Ingram Content Group, created a cooperative patron-driven model to acquire electronic books (e-books. The model provides the opportunity for faculty and students at the universities governed by the Arizona Board of Regents (ABOR to access a core of e-books made accessible through resource discovery services and online catalogs. These books are available for significantly less than a single ABOR university would expend for the same materials. The patron-driven model described is one of many evolving models in digital scholarship, and, although the Arizona Universities Library Consortium reports a successful experience, patron-driven models pose questions to stakeholders in the academic publishing industry.

  15. Employer-driven consumerism: integrating health into the business model.

    Science.gov (United States)

    Thompson, Michael; Checkley, Joseph

    2006-01-01

    Consumer-driven health care is a misnomer. Notwithstanding the enormous role the individual consumer has to play in reshaping the U.S. health care delivery system, this article will focus on the employer as the key driver of change and innovation in the consumerism revolution. American Standard provides a case study of how one major employer has evaluated health care in the context of its business and aggressively integrated consumerism and health into the core of its business. Other companies will appropriately execute consumerism strategies in a fashion consistent with their own needs, culture, resources and populations. However, the principles supporting those strategies will be very much consistent.

  16. Parameter optimization method for the water quality dynamic model based on data-driven theory.

    Science.gov (United States)

    Liang, Shuxiu; Han, Songlin; Sun, Zhaochen

    2015-09-15

    Parameter optimization is important for developing a water quality dynamic model. In this study, we applied data-driven method to select and optimize parameters for a complex three-dimensional water quality model. First, a data-driven model was developed to train the response relationship between phytoplankton and environmental factors based on the measured data. Second, an eight-variable water quality dynamic model was established and coupled to a physical model. Parameter sensitivity analysis was investigated by changing parameter values individually in an assigned range. The above results served as guidelines for the control parameter selection and the simulated result verification. Finally, using the data-driven model to approximate the computational water quality model, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the control parameters. The optimization routines and results were analyzed and discussed based on the establishment of the water quality model in Xiangshan Bay (XSB). Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Business process modelling in demand-driven agri-food supply chains : a reference framework

    OpenAIRE

    Verdouw, C.N.

    2010-01-01

    Keywords: Business process models; Supply chain management; Information systems; Reference information models; Market orientation; Mass customisation; Configuration; Coordination; Control; SCOR; Pot plants; Fruit industry Abstract The increasing volatility and diversity of demand urge agri-food supply chains to become more demand driven, i.e. sensitive and responsive to demand information of the ultimate consumer. Companies that participate in demand-driven supply chains must manage a high va...

  18. Comparison of data-driven and model-driven approaches to brightness temperature diurnal cycle interpolation

    CSIR Research Space (South Africa)

    Van den Bergh, F

    2006-01-01

    Full Text Available of the sequence no clouds were present, i.e. the cycle was complete without missing data. For each subsequent cycle this reference RKHS model was kept fixed and then just scaled and translated vertically to obtain the best least squares overlay. The 380 390 400... interpola- tion problem is novel. Results obtained by means of computer experiments are presented. 1. Introduction Remote sensing data obtained from earth observing satellites is frequently affected by cloud contamination, with about 50% of the globe...

  19. Model-Driven Enterprise Information Systems. Proceedings of the 3rd International Workshop on Model-Driven Enterprise Information Systems (MDEIS 2007)

    NARCIS (Netherlands)

    Ferreira Pires, Luis; Hammoudi, S.; Unknown, [Unknown

    This volume contains the proceedings of the Third International Workshop on Model-Driven Enterprise Information Systems (MDEIS) held in conjunction with the 9th International Conference on Enterprise Information Systems (ICEIS) in Madeira, Portugal. The main aim of this workshop is to serve as a

  20. Model-driven design of simulation support for the TERRA robot software tool suite

    NARCIS (Netherlands)

    Lu, Zhou; Bezemer, M.M.; Broenink, Johannes F.

    2015-01-01

    Model-Driven Development (MDD) – based on the concepts of model, meta-model and model transformation – is an approach to develop predictable and re- liable software for Cyber-Physical Systems (CPS). The work presented here concerns a methodology to design simulation software based on MDD techniques,

  1. LHC-GCS a model-driven approach for automatic PLC and SCADA code generation

    CERN Document Server

    Thomas, Geraldine; Barillère, Renaud; Cabaret, Sebastien; Kulman, Nikolay; Pons, Xavier; Rochez, Jacques

    2005-01-01

    The LHC experiments’ Gas Control System (LHC GCS) project [1] aims to provide the four LHC experiments (ALICE, ATLAS, CMS and LHCb) with control for their 23 gas systems. To ease the production and maintenance of 23 control systems, a model-driven approach has been adopted to generate automatically the code for the Programmable Logic Controllers (PLCs) and for the Supervision Control And Data Acquisition (SCADA) systems. The first milestones of the project have been achieved. The LHC GCS framework [4] and the generation tools have been produced. A first control application has actually been generated and is in production, and a second is in preparation. This paper describes the principle and the architecture of the model-driven solution. It will in particular detail how the model-driven solution fits with the LHC GCS framework and with the UNICOS [5] data-driven tools.

  2. Modeling and evaluation of HE driven shock effects in copper with the MTS model

    International Nuclear Information System (INIS)

    Murphy, M.J.; Lassila, D.F.

    1997-01-01

    Many experimental studies have investigated the effect of shock pressure on the post-shock mechanical properties of OFHC copper. These studies have shown that significant hardening occurs during shock loading due to dislocation processes and twinning. It has been demonstrated that when an appropriate initial value of the Mechanical Threshold Stress (MTS) is specified, the post-shock flow stress of OFE copper is well described by relationships derived independently for unshocked materials. In this study we consider the evolution of the MTS during HE driven shock loading processes and the effect on the subsequent flow stress of the copper. An increased post shock flow stress results in a higher material temperature due to an increase in the plastic work. An increase in temperature leads to thermal softening which reduces the flow stress. These coupled effects will determine if there is melting in a shaped charge jet or a necking instability in an EFP Ww. 'Me critical factor is the evolution path followed combined with the 'current' temperature, plastic strain, and strain rate. Preliminary studies indicate that in simulations of HE driven shock with very high resolution zoning, the MTS saturates because of the rate dependence in the evolution law. On going studies are addressing this and other issues with the goal of developing a version of the MT'S model that treats HE driven, shock loading, temperature, strain, and rate effects apriori

  3. A solvable two-species catalysis-driven aggregation model

    CERN Document Server

    Ke Jian Hong

    2003-01-01

    We study the kinetics of a two-species catalysis-driven aggregation system, in which an irreversible aggregation between any two clusters of one species occurs only with the catalytic action of another species. By means of a generalized mean-field rate equation, we obtain the asymptotic solutions of the cluster mass distributions in a simple process with a constant rate kernel. For the case without any consumption of the catalyst, the cluster mass distribution of either species always approaches a conventional scaling law. However, the evolution behaviour of the system in the case with catalyst consumption is complicated and depends crucially on the relative data of the initial concentrations of the two species.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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…

  6. Model-driven design, simulation and implementation of service compositions in COSMO

    NARCIS (Netherlands)

    Quartel, Dick; Dirgahayu, T.; van Sinderen, Marten J.

    2009-01-01

    The success of software development projects to a large extent depends on the quality of the models that are produced in the development process, which in turn depends on the conceptual and practical support that is available for modelling, design and analysis. This paper focuses on model-driven

  7. Modeling net ecosystem carbon exchange of alpine grasslands with a satellite-driven model.

    Directory of Open Access Journals (Sweden)

    Wei Yan

    Full Text Available Estimate of net ecosystem carbon exchange (NEE between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP and ecosystem respiration (Reco has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model.

  8. A Data-Driven Air Transportation Delay Propagation Model Using Epidemic Process Models

    Directory of Open Access Journals (Sweden)

    B. Baspinar

    2016-01-01

    Full Text Available In air transport network management, in addition to defining the performance behavior of the system’s components, identification of their interaction dynamics is a delicate issue in both strategic and tactical decision-making process so as to decide which elements of the system are “controlled” and how. This paper introduces a novel delay propagation model utilizing epidemic spreading process, which enables the definition of novel performance indicators and interaction rates of the elements of the air transportation network. In order to understand the behavior of the delay propagation over the network at different levels, we have constructed two different data-driven epidemic models approximating the dynamics of the system: (a flight-based epidemic model and (b airport-based epidemic model. The flight-based epidemic model utilizing SIS epidemic model focuses on the individual flights where each flight can be in susceptible or infected states. The airport-centric epidemic model, in addition to the flight-to-flight interactions, allows us to define the collective behavior of the airports, which are modeled as metapopulations. In network model construction, we have utilized historical flight-track data of Europe and performed analysis for certain days involving certain disturbances. Through this effort, we have validated the proposed delay propagation models under disruptive events.

  9. Preliminary Results from a Model-Driven Architecture Methodology for Development of an Event-Driven Space Communications Service Concept

    Science.gov (United States)

    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.

  10. Unified Model, and Novel Reverse Recovery Nonlinearities, of the Driven Diode Resonator

    OpenAIRE

    de Moraes, Renato Mariz; Anlage, Steven M.

    2003-01-01

    We study the origins of period doubling and chaos in the driven series resistor-inductor-varactor diode (RLD) nonlinear resonant circuit. We find that resonators driven at frequencies much higher than the diode reverse recovery rate do not show period doubling, and that models of chaos based on the nonlinear capacitance of the varactor diode display a reverse-recovery-like effect, and this effect strongly resembles reverse recovery of real diodes. We find for the first time that in addition t...

  11. Positioning performance of the NTCM model driven by GPS Klobuchar model parameters

    Science.gov (United States)

    Hoque, Mohammed Mainul; Jakowski, Norbert; Berdermann, Jens

    2018-03-01

    Users of the Global Positioning System (GPS) utilize the Ionospheric Correction Algorithm (ICA) also known as Klobuchar model for correcting ionospheric signal delay or range error. Recently, we developed an ionosphere correction algorithm called NTCM-Klobpar model for single frequency GNSS applications. The model is driven by a parameter computed from GPS Klobuchar model and consecutively can be used instead of the GPS Klobuchar model for ionospheric corrections. In the presented work we compare the positioning solutions obtained using NTCM-Klobpar with those using the Klobuchar model. Our investigation using worldwide ground GPS data from a quiet and a perturbed ionospheric and geomagnetic activity period of 17 days each shows that the 24-hour prediction performance of the NTCM-Klobpar is better than the GPS Klobuchar model in global average. The root mean squared deviation of the 3D position errors are found to be about 0.24 and 0.45 m less for the NTCM-Klobpar compared to the GPS Klobuchar model during quiet and perturbed condition, respectively. The presented algorithm has the potential to continuously improve the accuracy of GPS single frequency mass market devices with only little software modification.

  12. Recommendations for Model Driven Paradigms for Integrated Approaches to Cyber Defense

    Science.gov (United States)

    2017-03-06

    Simulation models that we call here “ Business Impact Simulation ” are particularly important for NATO, but simulation models we call here “Attack...Recommendation: The types of simulation models that we call here Business Impact Simulation are particularly important for NATO mission planning, mission...principle-based modeling and simulation are more likely to produce long-term, reusable approaches. A model-driven paradigm is predicated on mechanisms

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Frankovský P.

    2017-08-01

    Full Text Available 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.

  15. Bending of Euler-Bernoulli nanobeams based on the strain-driven and stress-driven nonlocal integral models: a numerical approach

    Science.gov (United States)

    Oskouie, M. Faraji; Ansari, R.; Rouhi, H.

    2018-04-01

    Eringen's nonlocal elasticity theory is extensively employed for the analysis of nanostructures because it is able to capture nanoscale effects. Previous studies have revealed that using the differential form of the strain-driven version of this theory leads to paradoxical results in some cases, such as bending analysis of cantilevers, and recourse must be made to the integral version. In this article, a novel numerical approach is developed for the bending analysis of Euler-Bernoulli nanobeams in the context of strain- and stress-driven integral nonlocal models. This numerical approach is proposed for the direct solution to bypass the difficulties related to converting the integral governing equation into a differential equation. First, the governing equation is derived based on both strain-driven and stress-driven nonlocal models by means of the minimum total potential energy. Also, in each case, the governing equation is obtained in both strong and weak forms. To solve numerically the derived equations, matrix differential and integral operators are constructed based upon the finite difference technique and trapezoidal integration rule. It is shown that the proposed numerical approach can be efficiently applied to the strain-driven nonlocal model with the aim of resolving the mentioned paradoxes. Also, it is able to solve the problem based on the strain-driven model without inconsistencies of the application of this model that are reported in the literature.

  16. Models of plastic depinning of driven disordered systems

    Indian Academy of Sciences (India)

    class allows for proliferation of topological defects due to the interplay of strong disorder and drive. In mean field theory both models exhibit a tricritical point as a function of dis- .... driving force, fp is the pinning force, and σα represents the stress due to interactions ..... In this model the hysteresis may indeed be an artifact.

  17. Efficient Parallel Execution of Event-Driven Electromagnetic Hybrid Models

    Energy Technology Data Exchange (ETDEWEB)

    Perumalla, Kalyan S [ORNL; Karimabadi, Dr. Homa [SciberQuest Inc.; Fujimoto, Richard [ORNL

    2007-01-01

    New discrete-event formulations of physics simulation models are emerging that can outperform traditional time-stepped models, especially in simulations containing multiple timescales. Detailed simulation of the Earth's magnetosphere, for example, requires execution of sub-models that operate at timescales that differ by orders of magnitude. In contrast to time-stepped simulation which requires tightly coupled updates to almost the entire system state at regular time intervals, the new discrete event simulation (DES) approaches help evolve the states of sub-models on relatively independent timescales. However, in contrast to relative ease of parallelization of time-stepped codes, the parallelization of DES-based models raises challenges with respect to their scalability and performance. One of the key challenges is to improve the computation granularity to offset synchronization and communication overheads within and across processors. Our previous work on parallelization was limited in scalability and runtime performance due to such challenges. Here we report on optimizations we performed on DES-based plasma simulation models to improve parallel execution performance. The mapping of the model to simulation processes is optimized via aggregation techniques, and the parallel runtime engine is optimized for communication and memory efficiency. The net result is the capability to simulate hybrid particle-in-cell (PIC) models with over 2 billion ion particles using 512 processors on supercomputing platforms.

  18. Profile-driven regression for modeling and runtime optimization of mobile networks

    DEFF Research Database (Denmark)

    McClary, Dan; Syrotiuk, Violet; Kulahci, Murat

    2010-01-01

    of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...

  19. Efficient and Accurate Log-Levy Approximations of Levy-Driven LIBOR Models

    DEFF Research Database (Denmark)

    Papapantoleon, Antonis; Schoenmakers, John; Skovmand, David

    2012-01-01

    The LIBOR market model is very popular for pricing interest rate derivatives but is known to have several pitfalls. In addition, if the model is driven by a jump process, then the complexity of the drift term grows exponentially fast (as a function of the tenor length). We consider a Lévy...

  20. Efficient and accurate log-Lévy approximations to Lévy driven LIBOR models

    DEFF Research Database (Denmark)

    Papapantoleon, Antonis; Schoenmakers, John; Skovmand, David

    2011-01-01

    The LIBOR market model is very popular for pricing interest rate derivatives, but is known to have several pitfalls. In addition, if the model is driven by a jump process, then the complexity of the drift term is growing exponentially fast (as a function of the tenor length). In this work, we...

  1. Stochastic regime switching SIR model driven by Lévy noise

    Science.gov (United States)

    Guo, Yingjia

    2017-08-01

    We propose a new stochastic regime switching SIR model driven by Lévy noise. A unique global positive solution is obtained under some appropriate conditions. Moreover, we investigate the asymptotic behavior of the stochastic SIR model with jumps under regime switching.

  2. Editorial - Special Issue on Model-driven Service-oriented architectures

    NARCIS (Netherlands)

    Andrade Almeida, João; Ferreira Pires, Luis; van Sinderen, Marten J.; Steen, M.W.A.

    2009-01-01

    Model-driven approaches to software development have proliferated in recent years owing to the availability of techniques based on metamodelling and model transformations, such as the meta-object facility (MOF) and the query view transformation (QVT) standards. During the same period,

  3. Uncertainty Driven Action (UDA) model: A foundation for unifying perspectives on design activity

    DEFF Research Database (Denmark)

    Cash, Philip; Kreye, Melanie

    2017-01-01

    This paper proposes the Uncertainty Driven Action (UDA) model, which unifies the fragmented literature on design activity. The UDA model conceptualises design activity as a process consisting of three core actions: information action, knowledge-sharing action, and representation action, which are...

  4. Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models

    DEFF Research Database (Denmark)

    Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin

    2017-01-01

    In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21......, 28 and 34 from all preceding days and provide our interpretation of the results. Results indicate that the dynamic interconnection between environmental conditions and broiler growth can be captured by the model. Furthermore, we found that a comparable forecast can be obtained by using input data...

  5. Modelling of vegetation-driven morphodynamics in braided rivers.

    Science.gov (United States)

    Stecca, Guglielmo; Fedrizzi, Davide; Hicks, Murray; Measures, Richard; Zolezzi, Guido; Bertoldi, Walter; Tal, Michal

    2017-04-01

    River planform results from the complex interaction between flow, sediment transport and vegetation, and can evolve following a change in these controls. The braided planform of New Zealand's Lower Waitaki River, for instance, is endangered by the action of artificially-introduced alien vegetation, which spread across the braidplain following the reduction in magnitude of floods by hydropower dam construction. This vegetation, by encouraging flow concentration into the main channel, would likely promote a shift towards a single-thread morphology if it was not artificially removed within a central fairway. The purpose of this work is to study the evolution of braided rivers such as the Waitaki under different management scenarios through two-dimensional numerical modelling. The construction of a suitable model represents a task in itself, since a modelling framework coupling all the relevant processes is not yet readily available. Our starting point is the physics-based GIAMT2D numerical model, which solves two-dimensional flow and bedload transport in wet/dry domains, and recently modified by the inclusion of a rule-based bank erosion model. We have further developed this model by adding a vegetation module, which accounts in a simplified manner for time-evolving biomass density, adjusting local flow roughness, critical shear stress for sediment transport, and bank erodibility accordingly. Our goal is to use the model to study decadal-scale evolution of a reach on the Waitaki River and predict planform characteristics under different vegetation management scenarios. Here we present the results of a preliminary application of the model to reproduce the morphodynamic evolution of a braided channel in a set of flume experiments that used alfalfa as vegetation. The experiments began with a braided morphology that spontaneoulsy formed at constant flow over a bed of bare uniform sand. The planform transitioned towards single-thread when this discharge was repeatedly

  6. Dataflow-Driven Crowdsourcing: Relational Models and Algorithms

    OpenAIRE

    D. A. Ustalov

    2016-01-01

    Recently, microtask crowdsourcing has become a popular approach for addressing various data mining problems. Crowdsourcing workflows for approaching such problems are composed of several data processing stages which require consistent representation for making the work reproducible. This paper is devoted to the problem of reproducibility and formalization of the microtask crowdsourcing process. A computational model for microtask crowdsourcing based on an extended relational model and a dataf...

  7. Studies on modelling of bubble driven flows in chemical reactors

    Energy Technology Data Exchange (ETDEWEB)

    Grevskott, Sverre

    1997-12-31

    Multiphase reactors are widely used in the process industry, especially in the petrochemical industry. They very often are characterized by very good thermal control and high heat transfer coefficients against heating and cooling surfaces. This thesis first reviews recent advances in bubble column modelling, focusing on the fundamental flow equations, drag forces, transversal forces and added mass forces. The mathematical equations for the bubble column reactor are developed, using an Eulerian description for the continuous and dispersed phase in tensor notation. Conservation equations for mass, momentum, energy and chemical species are given, and the k-{epsilon} and Rice-Geary models for turbulence are described. The different algebraic solvers used in the model are described, as are relaxation procedures. Simulation results are presented and compared with experimental values. Attention is focused on the modelling of void fractions and gas velocities in the column. The energy conservation equation has been included in the bubble column model in order to model temperature distributions in a heated reactor. The conservation equation of chemical species has been included to simulate absorption of CO{sub 2}. Simulated axial and radial mass fraction profiles for CO{sub 2} in the gas phase are compared with measured values. Simulations of the dynamic behaviour of the column are also presented. 189 refs., 124 figs., 1 tab.

  8. Data-driven modelling of structured populations a practical guide to the integral projection model

    CERN Document Server

    Ellner, Stephen P; Rees, Mark

    2016-01-01

    This book is a “How To” guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includes code in the R language (in the text and online) to carry out all computations. The intended audience are ecologists, evolutionary biologists, and mathematical biologists interested in developing data-driven models for animal and plant populations. IPMs may seem hard as they involve integrals. The aim of this book is to demystify IPMs, so they become the model of choice for populations structured by size or other continuously varying traits. The book uses real examples of increasing complexity to show how the life-cycle of the study organism naturally leads to the appropriate statistical analysis, which leads directly to the IPM itself. A wide range of model types and analyses are presented, including model construction, computational methods, and the underlying theory, with the more technical material in B...

  9. Dataflow-Driven Crowdsourcing: Relational Models and Algorithms

    Directory of Open Access Journals (Sweden)

    D. A. Ustalov

    2016-01-01

    Full Text Available Recently, microtask crowdsourcing has become a popular approach for addressing various data mining problems. Crowdsourcing workflows for approaching such problems are composed of several data processing stages which require consistent representation for making the work reproducible. This paper is devoted to the problem of reproducibility and formalization of the microtask crowdsourcing process. A computational model for microtask crowdsourcing based on an extended relational model and a dataflow computational model has been proposed. The proposed collaborative dataflow computational model is designed for processing the input data sources by executing annotation stages and automatic synchronization stages simultaneously. Data processing stages and connections between them are expressed by using collaborative computation workflows represented as loosely connected directed acyclic graphs. A synchronous algorithm for executing such workflows has been described. The computational model has been evaluated by applying it to two tasks from the computational linguistics field: concept lexicalization refining in electronic thesauri and establishing hierarchical relations between such concepts. The “Add–Remove–Confirm” procedure is designed for adding the missing lexemes to the concepts while removing the odd ones. The “Genus–Species–Match” procedure is designed for establishing “is-a” relations between the concepts provided with the corresponding word pairs. The experiments involving both volunteers from popular online social networks and paid workers from crowdsourcing marketplaces confirm applicability of these procedures for enhancing lexical resources. 

  10. A Micro Simulated and Demand Driven Supply Chain Model To Calculate Regional Production and Consumption Matrices

    OpenAIRE

    Abed, Omar; Bellemans, Tom; Janssens, Gerrit; Patil, Bharat; Yasar, Ansar; Janssens, Davy; Wets, Geert

    2013-01-01

    Detailed data on regional goods production and consumption are traditionally the starting point to model freight transport on a nationwide scale. The conversation of those goods afterwards into various vehicle load types and the different logistics operations needed to deliver the requested goods type and quantity, follow from that starting point in the modeling process. In this paper, a demand driven microsimulated supply chain model is presented. The model shall be a first step towards calc...

  11. An induction Linac driven heavy-ion fusion systems model

    International Nuclear Information System (INIS)

    Zuckerman, D.S.; Driemeyer, D.E.; Waganer, L.M.; Dudziak, D.J.

    1988-01-01

    A computerized systems model of a heavy-ion fusion (HIF) reactor power plant is presented. The model can be used to analyze the behavior and projected costs of a commercial power plant using an induction linear accelerator (Linac) as a driver. Each major component of the model (targets, reactor cavity, Linac, beam transport, power flow, balance of plant, and costing) is discussed. Various target, reactor cavity, Linac, and beam transport schemes are examined and compared. The preferred operating regime for such a power plant is also examined. The results show that HIF power plants can compete with other advanced energy concepts at the 1000-MW (electric) power level [cost of electricity (COE) -- 50 mill/kW . h] provided that the cost savings predicted for Linacs using higher charge-state ions (+3) can be realized

  12. Test-Driven, Model-Based Systems Engineering

    DEFF Research Database (Denmark)

    Munck, Allan

    Hearing systems have evolved over many years from simple mechanical devices (horns) to electronic units consisting of microphones, amplifiers, analog filters, loudspeakers, batteries, etc. Digital signal processors replaced analog filters to provide better performance end new features. Central....... This thesis concerns methods for identifying, selecting and implementing tools for various aspects of model-based systems engineering. A comprehensive method was proposed that include several novel steps such as techniques for analyzing the gap between requirements and tool capabilities. The method...... was verified with good results in two case studies for selection of a traceability tool (single-tool scenario) and a set of modeling tools (multi-tool scenarios). Models must be subjected to testing to allow engineers to predict functionality and performance of systems. Test-first strategies are known...

  13. Experimentally driven atomistic model of 1,2 polybutadiene

    Energy Technology Data Exchange (ETDEWEB)

    Gkourmpis, Thomas, E-mail: thomas.gkourmpis@borealisgroup.com [Polymer Science Centre, J. J. Thomson Physical Laboratory, Department of Physics, University of Reading, Reading RG6 6AF (United Kingdom); Mitchell, Geoffrey R. [Polymer Science Centre, J. J. Thomson Physical Laboratory, Department of Physics, University of Reading, Reading RG6 6AF (United Kingdom); Centre for Rapid and Sustainable Product Development, Institute Polytechnic Leiria, Marinha Grande (Portugal)

    2014-02-07

    We present an efficient method of combining wide angle neutron scattering data with detailed atomistic models, allowing us to perform a quantitative and qualitative mapping of the organisation of the chain conformation in both glass and liquid phases. The structural refinement method presented in this work is based on the exploitation of the intrachain features of the diffraction pattern and its intimate linkage with atomistic models by the use of internal coordinates for bond lengths, valence angles, and torsion rotations. Atomic connectivity is defined through these coordinates that are in turn assigned by pre-defined probability distributions, thus allowing for the models in question to be built stochastically. Incremental variation of these coordinates allows for the construction of models that minimise the differences between the observed and calculated structure factors. We present a series of neutron scattering data of 1,2 polybutadiene at the region 120–400 K. Analysis of the experimental data yields bond lengths for Cî—¸C and C î—» C of 1.54 Å and 1.35 Å, respectively. Valence angles of the backbone were found to be at 112° and the torsion distributions are characterised by five rotational states, a three-fold trans-skew± for the backbone and gauche± for the vinyl group. Rotational states of the vinyl group were found to be equally populated, indicating a largely atactic chan. The two backbone torsion angles exhibit different behaviour with respect to temperature of their trans population, with one of them adopting an almost all trans sequence. Consequently, the resulting configuration leads to a rather persistent chain, something indicated by the value of the characteristic ratio extrapolated from the model. We compare our results with theoretical predictions, computer simulations, RIS models and previously reported experimental results.

  14. Ultrasound-driven Viscous Streaming, Modelled via Momentum Injection

    Directory of Open Access Journals (Sweden)

    James PACKER

    2008-12-01

    Full Text Available Microfluidic devices can use steady streaming caused by the ultrasonic oscillation of one or many gas bubbles in a liquid to drive small scale flow. Such streaming flows are difficult to evaluate, as analytic solutions are not available for any but the simplest cases, and direct computational fluid dynamics models are unsatisfactory due to the large difference in flow velocity between the steady streaming and the leading order oscillatory motion. We develop a numerical technique which uses a two-stage multiscale computational fluid dynamics approach to find the streaming flow as a steady problem, and validate this model against experimental results.

  15. A Model-Driven, Science Data Product Registration Service

    Science.gov (United States)

    Hardman, S.; Ramirez, P.; Hughes, J. S.; Joyner, R.; Cayanan, M.; Lee, H.; Crichton, D. J.

    2011-12-01

    The Planetary Data System (PDS) has undertaken an effort to overhaul the PDS data architecture (including the data model, data structures, data dictionary, etc.) and to deploy an upgraded software system (including data services, distributed data catalog, etc.) that fully embraces the PDS federation as an integrated system while taking advantage of modern innovations in information technology (including networking capabilities, processing speeds, and software breakthroughs). A core component of this new system is the Registry Service that will provide functionality for tracking, auditing, locating, and maintaining artifacts within the system. These artifacts can range from data files and label files, schemas, dictionary definitions for objects and elements, documents, services, etc. This service offers a single reference implementation of the registry capabilities detailed in the Consultative Committee for Space Data Systems (CCSDS) Registry Reference Model White Book. The CCSDS Reference Model in turn relies heavily on the Electronic Business using eXtensible Markup Language (ebXML) standards for registry services and the registry information model, managed by the OASIS consortium. Registries are pervasive components in most information systems. For example, data dictionaries, service registries, LDAP directory services, and even databases provide registry-like services. These all include an account of informational items that are used in large-scale information systems ranging from data values such as names and codes, to vocabularies, services and software components. The problem is that many of these registry-like services were designed with their own data models associated with the specific type of artifact they track. Additionally these services each have their own specific interface for interacting with the service. This Registry Service implements the data model specified in the ebXML Registry Information Model (RIM) specification that supports the various

  16. A Transition Towards a Data-Driven Business Model (DDBM)

    DEFF Research Database (Denmark)

    Zaki, Mohamed; Bøe-Lillegraven, Tor; Neely, Andy

    2016-01-01

    Nettavisen is a Norwegian online start-up that experienced a boost after the financial crisis of 2009. Since then, the firm has been able to increase its market share and profitability through the use of highly disruptive business models, allowing the relatively small staff to outcompete powerhouse...... legacy-publishing companies and new media players such as Facebook and Google. These disruptive business models have been successful, as Nettavisen captured a large market share in Norway early on, and was consistently one of the top-three online news sites in Norway. Capitalising on media data explosion...

  17. Modeling Quasi-Static and Fatigue-Driven Delamination Migration

    Science.gov (United States)

    De Carvalho, N. V.; Ratcliffe, J. G.; Chen, B. Y.; Pinho, S. T.; Baiz, P. M.; Tay, T. E.

    2014-01-01

    An approach was proposed and assessed for the high-fidelity modeling of progressive damage and failure in composite materials. It combines the Floating Node Method (FNM) and the Virtual Crack Closure Technique (VCCT) to represent multiple interacting failure mechanisms in a mesh-independent fashion. Delamination, matrix cracking, and migration were captured failure and migration criteria based on fracture mechanics. Quasi-static and fatigue loading were modeled within the same overall framework. The methodology proposed was illustrated by simulating the delamination migration test, showing good agreement with the available experimental data.

  18. Observer and data-driven model based fault detection in Power Plant Coal Mills

    DEFF Research Database (Denmark)

    Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

    2008-01-01

    model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection...

  19. Model-driven design of geo-information services

    NARCIS (Netherlands)

    Morales Guarin, J.M.; Morales Guarin, Javier Marcelino

    2004-01-01

    This thesis presents a method for the development of distributed geo-information systems. The method is organised around the design principles of modularity, reuse and replaceability. The method enables the modelling of both behavioural and informational aspects of geo-information systems in an

  20. Unraveling Supply-Driven Business Models of Architectural Firms

    NARCIS (Netherlands)

    Bos-De Vos, M.; Volker, L.; Wamelink, J.W.F.; Kaminsky, Jessica; Zerjav, Vedran

    2016-01-01

    Architectural firms deliver services for various, unique projects that are all characterized by a high level of uncertainty. To successfully propose, create and capture value, they need business models that are able to deal with this variety and uncertainty. So far, little is known about the

  1. 25 Years of Model-Driven Web Engineering: What we achieved, What is missing

    Directory of Open Access Journals (Sweden)

    Gustavo Rossi

    2016-12-01

    Full Text Available Model-Driven Web Engineering (MDWE approaches aim to improve the Web applications development process by focusing on modeling instead of coding, and deriving the running application by transformations from conceptual models to code. The emergence of the Interaction Flow Modeling Language (IFML has been an important milestone in the evolution of Web modeling languages, indicating not only the maturity of the field but also a final convergence of languages. In this paper we explain the evolution of modeling and design approaches since the early years (in the 90’s detailing the forces which drove that evolution and discussing the strengths and weaknesses of some of those approaches. A brief presentation of the IFML is accompanied with a thorough analysis of the most important achievements of the MDWE community as well as the problems and obstacles that hinder the dissemination of model-driven techniques in the Web engineering field.

  2. Comparing the data-driven and the model-dependent strategies for improving filtered GRACE signal

    Science.gov (United States)

    Dutt Vishwakarma, Bramha; Sneeuw, Nico

    2017-04-01

    The noisy level 02 GRACE products from various groups need to be filtered in order to obtain meaningful information about water mass transport within the Earth system. Filtering affects signal, which increases the uncertainty in the filtered GRACE observed total water storage time series. The signal loss is counter acted using a correction strategy that typically makes use of models. The accuracy of model-dependent methods is dependent on the accuracy of the model, which raises doubts on accuracy of corrected GRACE products over poorly modeled regions. This led to the development of data-driven methods. Although research contributions using a model-dependent method or a data-driven method claim that the corrected GRACE products are superior to filtered products, a comparison of model dependent methods and the data-driven methods is essential to choose the best one. In this contribution, we compare the three most popular model-dependent approaches: additive approach, multiplicative approach, scaling approach, and two data-driven methods proposed recently. In order to be comprehensive, we analyze the performance of these correction strategies over 32 catchments of different sizes located in different climate zones. In a realistic closed-loop simulation, we find that the data-driven methods are consistently superior to the model-dependent approaches. At last we analyze the desiccation of Aral Sea and lake Urmia with the GRACE products, and compare the corrected total water storage change with reports and contributions from different groups. We find that the model-dependent approaches have a tendency to overestimate the rate of water mass loss recorded by GRACE satellites.

  3. Quantum dynamics of the driven and dissipative Rabi model

    Science.gov (United States)

    Henriet, Loïc; Ristivojevic, Zoran; Orth, Peter P.; Le Hur, Karyn

    2014-08-01

    The Rabi model considers a two-level system (or spin 1/2) coupled to a quantized harmonic oscillator and describes the simplest interaction between matter and light. The recent experimental progress in solid-state circuit quantum electrodynamics has engendered theoretical efforts to quantitatively describe the mathematical and physical aspects of the light-matter interaction beyond the rotating-wave approximation. We develop a stochastic Schrödinger equation approach which enables us to access the strong-coupling limit of the Rabi model and study the effects of dissipation and ac drive in an exact manner. We include the effect of Ohmic noise on the non-Markovian spin dynamics, resulting in Kondo-type correlations, as well as cavity losses. We compute the time evolution of spin variables in various conditions. As a consideration for future work, we discuss the possibility of reaching a steady state with one polariton in realistic experimental conditions.

  4. Data-driven forward model inference for EEG brain imaging

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai

    2016-01-01

    . Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging......Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain...... imaging device. The quality of the source reconstruction depends on the forward model which details head geometry and conductivities of different head compartments. These person-specific factors are complex to determine, requiring detailed knowledge of the subject’s anatomy and physiology. In this proof...

  5. Model-Driven Test Generation of Distributed Systems

    Science.gov (United States)

    Easwaran, Arvind; Hall, Brendan; Schweiker, Kevin

    2012-01-01

    This report describes a novel test generation technique for distributed systems. Utilizing formal models and formal verification tools, spe cifically the Symbolic Analysis Laboratory (SAL) tool-suite from SRI, we present techniques to generate concurrent test vectors for distrib uted systems. These are initially explored within an informal test validation context and later extended to achieve full MC/DC coverage of the TTEthernet protocol operating within a system-centric context.

  6. Weather Driven Renewable Energy Analysis, Modeling New Technologies

    Science.gov (United States)

    Paine, J.; Clack, C.; Picciano, P.; Terry, L.

    2015-12-01

    Carbon emission reduction is essential to hampering anthropogenic climate change. While there are several methods to broach carbon reductions, the National Energy with Weather System (NEWS) model focuses on limiting electrical generation emissions by way of a national high-voltage direct-current transmission that takes advantage of the strengths of different regions in terms of variable sources of energy. Specifically, we focus upon modeling concentrating solar power (CSP) as another source to contribute to the electric grid. Power tower solar fields are optimized taking into account high spatial and temporal resolution, 13km and hourly, numerical weather prediction model data gathered by NOAA from the years of 2006-2008. Importantly, the optimization of these CSP power plants takes into consideration factors that decrease the optical efficiency of the heliostats reflecting solar irradiance. For example, cosine efficiency, atmospheric attenuation, and shadowing are shown here; however, it should be noted that they are not the only limiting factors. While solar photovoltaic plants can be combined for similar efficiency to the power tower and currently at a lower cost, they do not have a cost-effective capability to provide electricity when there are interruptions in solar irradiance. Power towers rely on a heat transfer fluid, which can be used for thermal storage changing the cost efficiency of this energy source. Thermal storage increases the electric stability that many other renewable energy sources lack, and thus, the ability to choose between direct electric conversion and thermal storage is discussed. The figure shown is a test model of a CSP plant made up of heliostats. The colors show the optical efficiency of each heliostat at a single time of the day.

  7. Modeling Water Quality Parameters Using Data-driven Methods

    Directory of Open Access Journals (Sweden)

    Shima Soleimani

    2017-02-01

    Full Text Available Introduction: Surface water bodies are the most easily available water resources. Increase use and waste water withdrawal of surface water causes drastic changes in surface water quality. Water quality, importance as the most vulnerable and important water supply resources is absolutely clear. Unfortunately, in the recent years because of city population increase, economical improvement, and industrial product increase, entry of pollutants to water bodies has been increased. According to that water quality parameters express physical, chemical, and biological water features. So the importance of water quality monitoring is necessary more than before. Each of various uses of water, such as agriculture, drinking, industry, and aquaculture needs the water with a special quality. In the other hand, the exact estimation of concentration of water quality parameter is significant. Material and Methods: In this research, first two input variable models as selection methods (namely, correlation coefficient and principal component analysis were applied to select the model inputs. Data processing is consisting of three steps, (1 data considering, (2 identification of input data which have efficient on output data, and (3 selecting the training and testing data. Genetic Algorithm-Least Square Support Vector Regression (GA-LSSVR algorithm were developed to model the water quality parameters. In the LSSVR method is assumed that the relationship between input and output variables is nonlinear, but by using a nonlinear mapping relation can create a space which is named feature space in which relationship between input and output variables is defined linear. The developed algorithm is able to gain maximize the accuracy of the LSSVR method with auto LSSVR parameters. Genetic algorithm (GA is one of evolutionary algorithm which automatically can find the optimum coefficient of Least Square Support Vector Regression (LSSVR. The GA-LSSVR algorithm was employed to

  8. A porcine model system of BRCA1 driven breast cancer

    Directory of Open Access Journals (Sweden)

    Geoff eClark

    2015-08-01

    Full Text Available BRCA1 is a breast and ovarian tumor suppressor. Hereditary mutations in BRCA1 result in a predisposition to breast cancer, and BRCA1 expression is down-regulated in ~30% of sporadic cases. The function of BRCA1 remains poorly understood, but it appears to play an important role in DNA repair and the maintenance of genetic stability. Mouse models of BRCA1 deficiency have been developed in an attempt to understand the role of the gene in vivo. However, the subtle nature of BRCA1 function and the well-known discrepancies between human and murine breast cancer biology and genetics may limit the utility of mouse systems in defining the function of BRCA1 in cancer and validating the development of novel therapeutics for breast cancer. In contrast to mice, pig biological systems and cancer genetics appear to more closely resemble their human counterparts. To determine if BRCA1 inactivation in pig cells promotes their transformation and may serve as a model for the human disease, we developed an immortalized porcine breast cell line and stably inactivated BRCA1 using miRNA. The cell line developed characteristics of breast cancer stem cells and exhibited a transformed phenotype. These results validate the concept of using pigs as a model to study BRCA1 defects in breast cancer and establish the first porcine breast tumor cell line.

  9. Data-driven remaining useful life prognosis techniques stochastic models, methods and applications

    CERN Document Server

    Si, Xiao-Sheng; Hu, Chang-Hua

    2017-01-01

    This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based pro...

  10. Cosmic ray driven outflows in global galaxy disc models

    Science.gov (United States)

    Salem, Munier; Bryan, Greg L.

    2014-02-01

    Galactic-scale winds are a generic feature of massive galaxies with high star formation rates across a broad range of redshifts. Despite their importance, a detailed physical understanding of what drives these mass loaded global flows has remained elusive. In this paper, we explore the dynamical impact of cosmic rays (CRs) by performing the first three-dimensional, adaptive mesh refinement simulations of an isolated starbursting galaxy that includes a basic model for the production, dynamics and diffusion of galactic CRs. We find that including CRs naturally leads to robust, massive, bipolar outflows from our 1012 M⊙ halo, with a mass loading factor dot{M}/SFR = 0.3 for our fiducial run. Other reasonable parameter choices led to mass loading factors above unity. The wind is multiphase and is accelerated to velocities well in excess of the escape velocity. We employ a two-fluid model for the thermal gas and relativistic CR plasma and model a range of physics relevant to galaxy formation, including radiative cooling, shocks, self-gravity, star formation, supernovae feedback into both the thermal and CR gas and isotropic CR diffusion. Injecting CRs into star-forming regions can provide significant pressure support for the interstellar medium (ISM), suppressing star formation and thickening the disc. We find that CR diffusion plays a central role in driving superwinds, rapidly transferring long-lived CRs from the highest density regions of the disc to the ISM at large, where their pressure gradient can smoothly accelerate the gas out of the disc.

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

    Science.gov (United States)

    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.

  12. Data-driven modeling of solar-powered urban microgrids.

    Science.gov (United States)

    Halu, Arda; Scala, Antonio; Khiyami, Abdulaziz; González, Marta C

    2016-01-01

    Distributed generation takes center stage in today's rapidly changing energy landscape. Particularly, locally matching demand and generation in the form of microgrids is becoming a promising alternative to the central distribution paradigm. Infrastructure networks have long been a major focus of complex networks research with their spatial considerations. We present a systemic study of solar-powered microgrids in the urban context, obeying real hourly consumption patterns and spatial constraints of the city. We propose a microgrid model and study its citywide implementation, identifying the self-sufficiency and temporal properties of microgrids. Using a simple optimization scheme, we find microgrid configurations that result in increased resilience under cost constraints. We characterize load-related failures solving power flows in the networks, and we show the robustness behavior of urban microgrids with respect to optimization using percolation methods. Our findings hint at the existence of an optimal balance between cost and robustness in urban microgrids.

  13. Facilitating Data Driven Business Model Innovation - A Case study

    DEFF Research Database (Denmark)

    Bjerrum, Torben Cæsar Bisgaard; Andersen, Troels Christian; Aagaard, Annabeth

    2016-01-01

    , that gathers knowledge is of great importance. The SMEs have little, if no experience, within data handling, data analytics, and working with structured Business Model Innovation (BMI), that relates to both new and conventional products, processes and services. This new frontier of data and BMI will have...... ability to adapt these new DDBM depends on the ability to pick up, share and develop knowledge between customers, partners and the network. This knowledge can be embedded into core BMs and constitutes a strategic opportunity enabling businesses to extract value from data into BMI, resulting in DDBMs...... core BMs, through the downloading, seeing and sensing phase of the BMI. This fact inhibits the sharing- and quality of knowledge. This in turn is limiting knowledge to narrow view and not other persons-, technical-, organisational-, and user/customer knowledge [2]. The outcome does not release the full...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  16. A RECONNECTION-DRIVEN RAREFACTION WAVE MODEL FOR CORONAL OUTFLOWS

    International Nuclear Information System (INIS)

    Bradshaw, S. J.; Aulanier, G.; Del Zanna, G.

    2011-01-01

    We conduct numerical experiments to determine whether interchange reconnection at high altitude coronal null points can explain the outflows observed as blueshifts in coronal emission lines at the boundaries between open and closed magnetic field regions. In this scenario, a strong, post-reconnection pressure gradient forms in the field-aligned direction when dense and hot, active region core loops reconnect with neighboring tenuous and cool, open field lines. We find that the pressure gradient drives a supersonic outflow and a rarefaction wave develops in both the open and closed post-reconnection magnetic field regions. We forward-model the spectral line profiles for a selection of coronal emission lines to predict the spectral signatures of the rarefaction wave. We find that the properties of the rarefaction wave are consistent with the observed velocity versus temperature structure of the corona in the outflow regions, where the velocity increases with the formation temperature of the emission lines. In particular, we find excellent agreement between the predicted and observed Fe XII 195.119 Å spectral line profiles in terms of the blueshift (10 km s –1 ), full width at half-maximum (83 mÅ) and symmetry. Finally, we find that T i e in the open field region, which indicates that the interchange reconnection scenario may provide a viable mechanism and source region for the slow solar wind.

  17. Extended MHD modeling of tearing-driven magnetic relaxation

    Science.gov (United States)

    Sauppe, J. P.; Sovinec, C. R.

    2017-05-01

    Discrete relaxation events in reversed-field pinch relevant configurations are investigated numerically with nonlinear extended magnetohydrodynamic (MHD) modeling, including the Hall term in Ohm's law and first-order ion finite Larmor radius effects. Results show variability among relaxation events, where the Hall dynamo effect may help or impede the MHD dynamo effect in relaxing the parallel current density profile. The competitive behavior arises from multi-helicity conditions where the dominant magnetic fluctuation is relatively small. The resulting changes in parallel current density and parallel flow are aligned in the core, consistent with experimental observations. The analysis of simulation results also confirms that the force density from fluctuation-induced Reynolds stress arises subsequent to the drive from the fluctuation-induced Lorentz force density. Transport of the momentum density is found to be dominated by the fluctuation-induced Maxwell stress over most of the cross section with viscous and gyroviscous contributions being large in the edge region. The findings resolve a discrepancy with respect to the relative orientation of current density and flow relaxation, which had not been realized or investigated in King et al. [Phys. Plasmas 19, 055905 (2012)], where only the magnitude of flow relaxation is actually consistent with experimental results.

  18. A unified model of supernova driven by magnetic monopoles

    Science.gov (United States)

    Peng, Qiu-He; Liu, Jing-Jing; Chou, Chih-Kang

    2017-12-01

    In this paper, we first discuss a series of important but puzzling physical mechanisms concerning the energy source, various kinds of core collapsed supernovae explosion mechanisms during central gravitational collapse in astrophysics. We also discuss the puzzle of possible association of γ -ray burst with gravitational wave perturbation, the heat source for the molten interior of the core of the Earth and finally the puzzling problem of the cooling of white dwarfs. We then make use of the estimations for the space flux of magnetic monopoles (hereafter MMs) and nucleon decay induced by MMs (called the Rubakov-Callen (RC) effect) to obtain the luminosity due to the RC effect. In terms of the formula for this RC luminosity, we present a unified treatment for the heat source of the Earth's core, the energy source for the white dwarf interior, various kinds of core collapsed supernovae (Type II Supernova (SNII), Type Ib Supernova (SNIb), Type Ic Supernova (SNIc), Super luminous supernova (SLSN)), and the production mechanism for γ -ray burst. This unified model can also be used to reasonably explain the possible association of the short γ -ray burst detected by the Fermi γ -ray Burst Monitoring Satellite (GBM) with the LIGO gravitational wave event GW150914 in September 2015.

  19. Data Driven Modelling of the Dynamic Wake Between Two Wind Turbines

    DEFF Research Database (Denmark)

    Knudsen, Torben; Bak, Thomas

    2012-01-01

    turbine. This paper establishes flow models relating the wind speeds at turbines in a farm. So far, research in this area has been mainly based on first principles static models and the data driven modelling done has not included the loading of the upwind turbine and its impact on the wind speed downwind......Wind turbines in a wind farm, influence each other through the wind flow. Downwind turbines are in the wake of upwind turbines and the wind speed experienced at downwind turbines is hence a function of the wind speeds at upwind turbines but also the momentum extracted from the wind by the upwind....... This paper is the first where modern commercial mega watt turbines are used for data driven modelling including the upwind turbine loading by changing power reference. Obtaining the necessary data is difficult and data is therefore limited. A simple dynamic extension to the Jensen wake model is tested...

  20. Service and Data Driven Multi Business Model Platform in a World of Persuasive Technologies

    DEFF Research Database (Denmark)

    Andersen, Troels Christian; Bjerrum, Torben Cæsar Bisgaard

    2016-01-01

    companies in establishing a service organization that delivers, creates and captures value through service and data driven business models by utilizing their network, resources and customers and/or users. Furthermore, based on literature and collaboration with the case company, the suggestion of a new...

  1. On model-driven design of robot software using co-simulation

    NARCIS (Netherlands)

    Broenink, Johannes F.; Ni, Yunyun; Groothuis, M.A.; Menegatti, E.

    2010-01-01

    In this paper we show that using co-simulation for robot software design will be more efficient than without co-simulation. We will show an example of the plotter how the co-simulation is helping with the design process. We believe that a collaborative methodology based on model-driven design will

  2. Magnetic-Field-Driven Artificial Muscle based on the H. Huxley Model: Fundamental Experiments

    Science.gov (United States)

    Hosoda, Makoto; Nishimoto, Yoshiko; Nashima, Shigeki

    2007-03-01

    We experimentally demonstrate an artificial muscle driven by an externally applied magnetic field. The drive mechanism simulated a model proposed by H. Huxley for bionic muscle contraction. Small magnetic needles swing with a time-varying magnetic field and enable linear motion of an object on the needles. In the future, this fundamental mechanism could prove useful for realizing linear actuators for robots.

  3. Way of Working for Embedded Control Software using Model-Driven Development Techniques

    NARCIS (Netherlands)

    Bezemer, M.M.; Groothuis, M.A.; Brugali, D.; Schlegel, C.; Broenink, Johannes F.

    2011-01-01

    Embedded targets normally do not have much resources to aid developing and debugging the software. So model-driven development (MDD) is used for designing embedded software with a `first time right' approach. For such an approach, a good way of working (WoW) is required for embedded software

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

    Science.gov (United States)

    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…

  5. Towards a multi-stakeholder-driven model for excellence in higher ...

    African Journals Online (AJOL)

    Additionally, the principles of learner empowerment, employability, transparency and world-class quality form the foundation of this strategic-driven model for curriculum development. Six phases are postulated with stakeholder engagement during all phases. Three broad areas of quality planning, quality management ...

  6. Business Process Modelling in Demand-Driven Agri-Food Supply Chains

    NARCIS (Netherlands)

    Verdouw, C.N.; Beulens, A.J.M.; Trienekens, J.H.; Wolfert, J.

    2010-01-01

    Agri-food companies increasingly participate in demand-driven supply chains that are able to adapt flexibly to changes in the marketplace. The objective of this presentation is to discuss a process modelling framework, which enhances the interoperability and agility of information systems as

  7. Handling non-functional requirements in model-driven development: an ongoing industrial survey

    NARCIS (Netherlands)

    Ameller, David; Franch, Xavier; Gómez, Cristina; Araújo, João; Berntsson Svensson, Richard; Biffle, Stefan; Cabot, Jordi; Cortelessa, Vittorio; Daneva, Maia; Méndez Fernández, Daniel; Moreira, Ana; Muccini, Henry; Vallecillo, Antonio; Wimmer, Manuel; Amaral, Vasco; Brunelière, Hugo; Burgueño, Loli; Goulão, Miguel; Schätz, Bernard; Teufl, Sabine

    2015-01-01

    Model-Driven Development (MDD) is no longer a novel development paradigm. It has become mature from a research perspective and recent studies show its adoption in industry. Still, some issues remain a challenge. Among them, we are interested in the treatment of non-functional requirements (NFRs) in

  8. Designing enterprise applications using model-driven service-oriented architectures

    NARCIS (Netherlands)

    van Sinderen, Marten J.; Andrade Almeida, João; Ferreira Pires, Luis; Quartel, Dick; Qui, R.G.

    2006-01-01

    This chapter aims at characterizing the concepts that underlie a model-driven service-oriented approach to the design of enterprise applications. Enterprise applications are subject to continuous change and adaptation since they are meant to support the dynamic arrangement of the business processes

  9. A Local Model for Angular Momentum Transport in Accretion Disks Driven by the Magnetorotational Instability

    DEFF Research Database (Denmark)

    Pessah, Martin Elias; Chan, Chi-kwan; Psaltis, Dimitrios

    2006-01-01

    We develop a local model for the exponential growth and saturation of the Reynolds and Maxwell stresses in turbulent flows driven by the magnetorotational instability. We first derive equations that describe the effects of the instability on the growth and pumping of the stresses. We highlight th...

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

    Science.gov (United States)

    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…

  11. DTI-based response-driven modeling of mTLE laterality

    Directory of Open Access Journals (Sweden)

    Mohammad-Reza Nazem-Zadeh

    2016-01-01

    Conclusion: The proposed response-driven DTI biomarker is intended to lessen diagnostic ambiguity of laterality in cases of mTLE and help optimize selection of surgical candidates. Application of this model shows promise in reducing the need for invasive icEEG in prospective cases.

  12. Nonperturbative stochastic method for driven spin-boson model

    Science.gov (United States)

    Orth, Peter P.; Imambekov, Adilet; Le Hur, Karyn

    2013-01-01

    We introduce and apply a numerically exact method for investigating the real-time dissipative dynamics of quantum impurities embedded in a macroscopic environment beyond the weak-coupling limit. We focus on the spin-boson Hamiltonian that describes a two-level system interacting with a bosonic bath of harmonic oscillators. This model is archetypal for investigating dissipation in quantum systems, and tunable experimental realizations exist in mesoscopic and cold-atom systems. It finds abundant applications in physics ranging from the study of decoherence in quantum computing and quantum optics to extended dynamical mean-field theory. Starting from the real-time Feynman-Vernon path integral, we derive an exact stochastic Schrödinger equation that allows us to compute the full spin density matrix and spin-spin correlation functions beyond weak coupling. We greatly extend our earlier work [P. P. Orth, A. Imambekov, and K. Le Hur, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.82.032118 82, 032118 (2010)] by fleshing out the core concepts of the method and by presenting a number of interesting applications. Methodologically, we present an analogy between the dissipative dynamics of a quantum spin and that of a classical spin in a random magnetic field. This analogy is used to recover the well-known noninteracting-blip approximation in the weak-coupling limit. We explain in detail how to compute spin-spin autocorrelation functions. As interesting applications of our method, we explore the non-Markovian effects of the initial spin-bath preparation on the dynamics of the coherence σx(t) and of σz(t) under a Landau-Zener sweep of the bias field. We also compute to a high precision the asymptotic long-time dynamics of σz(t) without bias and demonstrate the wide applicability of our approach by calculating the spin dynamics at nonzero bias and different temperatures.

  13. Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

    KAUST Repository

    Dreano, Denis

    2017-05-31

    Phytoplankton is at the basis of the marine food chain and therefore play a fundamental role in the ocean ecosystem. However, the large-scale phytoplankton dynamics of the Red Sea are not well understood yet, mainly due to the lack of historical in situ measurements. As a result, our knowledge in this area relies mostly on remotely-sensed observations and large-scale numerical marine ecosystem models. Models are very useful to identify the mechanisms driving the variations in chlorophyll concentration and have practical applications for fisheries operation and harmful algae blooms monitoring. Modelling approaches can be divided between physics- driven (dynamical) approaches, and data-driven (statistical) approaches. Dynamical models are based on a set of differential equations representing the transfer of energy and matter between different subsets of the biota, whereas statistical models identify relationships between variables based on statistical relations within the available data. The goal of this thesis is to develop, implement and test novel dynamical and statistical modelling approaches for studying and forecasting the variability of chlorophyll concentration in the Red Sea. These new models are evaluated in term of their ability to efficiently forecast and explain the regional chlorophyll variability. We also propose innovative synergistic strategies to combine data- and physics-driven approaches to further enhance chlorophyll forecasting capabilities and efficiency.

  14. Model continuity in discrete event simulation: A framework for model-driven development of simulation models

    NARCIS (Netherlands)

    Cetinkaya, D; Verbraeck, A.; Seck, MD

    2015-01-01

    Most of the well-known modeling and simulation (M&S) methodologies state the importance of conceptual modeling in simulation studies, and they suggest the use of conceptual models during the simulation model development process. However, only a limited number of methodologies refers to how to

  15. Low-dimensional modeling of a driven cavity flow with two free parameters

    DEFF Research Database (Denmark)

    Jørgensen, Bo Hoffmann; Sørensen, Jens Nørkær; Brøns, Morten

    2003-01-01

    -dimensional models. SPOD is capable of transforming data organized in different sets separately while still producing orthogonal modes. A low-dimensional model is constructed and used for analyzing bifurcations occurring in the flow in the lid-driven cavity with a rotating rod. The model allows one of the free...... parameters to appear in the inhomogeneous boundary conditions without the addition of any constraints. This is necessary because both the driving lid and the rotating rod are controlled simultaneously. Apparently, the results reported for this model are the first to be obtained for a low-dimensional model...... based on projections on POD modes for more than one free parameter....

  16. Model-driven development of smart grid services using SoaML

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena; Gehrke, Oliver

    2014-01-01

    This paper presents a model-driven software devel- opment process which can be applied to the design of smart grid services. The Service Oriented Architecture Modelling Language (SoaML) is used to describe the architecture as well as the roles and interactions between service participants....... The individual modelling steps and an example design of a SoaML model for a voltage control service are presented and explained. Finally, the paper discusses a proof-of-concept implementation of the modelled service in a smart grid testing laboratory....

  17. From requirements to Java in a snap model-driven requirements engineering in practice

    CERN Document Server

    Smialek, Michal

    2015-01-01

    This book provides a coherent methodology for Model-Driven Requirements Engineering which stresses the systematic treatment of requirements within the realm of modelling and model transformations. The underlying basic assumption is that detailed requirements models are used as first-class artefacts playing a direct role in constructing software. To this end, the book presents the Requirements Specification Language (RSL) that allows precision and formality, which eventually permits automation of the process of turning requirements into a working system by applying model transformations and co

  18. Business Process Modelling in Demand‐Driven Agri‐Food Supply Chains

    OpenAIRE

    Verdouw, Cor N.; Beulens, Adriaan J.M.; Trienekens, Jacques H.; Wolfert, Sjaak

    2010-01-01

    Agri‐food companies increasingly participate in demand‐driven supply chains that are able to adapt flexibly to changes in the marketplace. The objective of this presentation is to discuss a process modelling framework, which enhances the interoperability and agility of information systems as required in such dynamic supply chains. The designed framework consists of two parts: an object system definition and a modelling toolbox. The object system definition provides a conceptual definition of ...

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

    Science.gov (United States)

    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.

  20. The Community Water Model (CWATM) / Development of a community driven global water model

    Science.gov (United States)

    Burek, Peter; Satoh, Yusuke; Greve, Peter; Kahil, Taher; Wada, Yoshihide

    2017-04-01

    With a growing population and economic development, it is expected that water demands will increase significantly in the future, especially in developing regions. At the same time, climate change is expected to alter spatial patterns of hydrological cycle and will have global, regional and local impacts on water availability. Thus, it is important to assess water supply, water demand and environmental needs over time to identify the populations and locations that will be most affected by these changes linked to water scarcity, droughts and floods. The Community Water Model (CWATM) will be designed for this purpose in that it includes an accounting of how future water demands will evolve in response to socioeconomic change and how water availability will change in response to climate. CWATM represents one of the new key elements of IIASA's Water program. It has been developed to work flexibly at both global and regional level at different spatial resolutions. The model is open source and community-driven to promote our work amongst the wider water community worldwide and is flexible enough linking to further planned developments such as water quality and hydro-economic modules. CWATM will be a basis to develop a next-generation global hydro-economic modeling framework that represents the economic trade-offs among different water management options over a basin looking at water supply infrastructure and demand managements. The integrated modeling framework will consider water demand from agriculture, domestic, energy, industry and environment, investment needs to alleviate future water scarcity, and will provide a portfolio of economically optimal solutions for achieving future water management options under the Sustainable Development Goals (SDG) for example. In addition, it will be able to track the energy requirements associated with the water supply system e.g., pumping, desalination and interbasin transfer to realize the linkage with the water-energy economy. In

  1. Test-driven modeling and development of cloud-enabled cyber-physical smart systems

    DEFF Research Database (Denmark)

    Munck, Allan; Madsen, Jan

    2017-01-01

    . Using test-driven modeling (TDM) is likely to be the best way to design smart systems such that these qualities are ensured. However, the TDM methods that are applied to development of simpler systems do not scale to smart systems because the modeling technologies cannot handle the complexity and size...... of the systems. In this paper, we present a method for test-driven modeling that scales to very large and complex systems. The method uses a combination of formal verification of basic interactions, simulations of complex scenarios, and mathematical forecasting to predict system behavior and performance. We...... utilized the method to analyze, design and develop various scenarios for a cloud-enabled medical system. Our approach provides a versatile method that may be adapted and improved for future development of very large and complex smart systems in various domains....

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

    Science.gov (United States)

    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.

  3. Nuclear models, experiments and data libraries needed for numerical simulation of accelerator-driven system

    International Nuclear Information System (INIS)

    Bauge, E.; Bersillon, O.

    2000-01-01

    This paper presents the transparencies of the speech concerning the nuclear models, experiments and data libraries needed for numerical simulation of Accelerator-Driven Systems. The first part concerning the nuclear models defines the spallation process, the corresponding models (intra-nuclear cascade, statistical model, Fermi breakup, fission, transport, decay and macroscopic aspects) and the code systems. The second part devoted to the experiments presents the angular measurements, the integral measurements, the residual nuclei and the energy deposition. In the last part, dealing with the data libraries, the author details the fundamental quantities as the reaction cross-section, the low energy transport databases and the decay libraries. (A.L.B.)

  4. Refinement and verification in component-based model-driven design

    DEFF Research Database (Denmark)

    Chen, Zhenbang; Liu, Zhiming; Ravn, Anders Peter

    2009-01-01

    developed, all models constructed in each phase are verifiable. This requires that the modelling notations are formally defined and related in order to have tool support developed for the integration of sophisticated checkers, generators and transformations. This paper summarises our research on the method...... of Refinement of Component and Object Systems (rCOS) and illustrates it with experiences from the work on the Common Component Modelling Example (CoCoME). This gives evidence that the formal techniques developed in rCOS can be integrated into a model-driven development process and shows where it may...

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  7. An automotive supply chain model for a demand-driven environment

    Directory of Open Access Journals (Sweden)

    Intaher M. Ambe

    2011-11-01

    Full Text Available The purpose of this article is to demonstrate the development of a supply chain model for the automotive industry that would respond to changing consumer demand. Now more than ever, businesses need to improve the efficiency of their supply chains in order to maintain a competitive advantage. The principles of lean manufacturing and just-intime (JIT inventory control that were renowned for helping companies like Toyota, Dell and Walmart to rise to the top of their respective industries are no longer adequate. Leading companies are applying new technologies and sophisticated analytics to make their supply chains more responsive to customer demand. This challenge is driven by fierce competition, fluctuating market demand and rising customer requirements that have led to customers becoming more demanding with increased preferences. The article is based on theoretical reviews and suggests guidelines for the implementation of an automotive supply chain model for a demand-driven environment.

  8. Single-particle model of a strongly driven, dense, nanoscale quantum ensemble

    Science.gov (United States)

    DiLoreto, C. S.; Rangan, C.

    2018-01-01

    We study the effects of interatomic interactions on the quantum dynamics of a dense, nanoscale, atomic ensemble driven by a strong electromagnetic field. We use a self-consistent, mean-field technique based on the pseudospectral time-domain method and a full, three-directional basis to solve the coupled Maxwell-Liouville equations. We find that interatomic interactions generate a decoherence in the state of an ensemble on a much faster time scale than the excited-state lifetime of individual atoms. We present a single-particle model of the driven, dense ensemble by incorporating interactions into a dephasing rate. This single-particle model reproduces the essential physics of the full simulation and is an efficient way of rapidly estimating the collective dynamics of a dense ensemble.

  9. Model driven development of clinical information sytems using openEHR.

    Science.gov (United States)

    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.

  10. A Model Driven Approach to domain standard specifications examplified by Finance Accounts receivable/ Accounts payable

    OpenAIRE

    Khan, Bahadar

    2005-01-01

    This thesis was written as a part of a master degree at the University of Oslo. The thesis work was conducted at SINTEF. The work has been carried out in the period November 2002 and April 2005. This thesis might be interesting to anyone interested in Domain Standard Specification Language developed by using the MDA approach to software development. The Model Driven Architecture (MDA) allows to separate the system functionality specification from its implementation on any specific technolo...

  11. Modeling and Control of Direct Driven PMSG for Ultra Large Wind Turbines

    OpenAIRE

    Ahmed M. Hemeida; Wael A. Farag; Osama A. Mahgoub

    2011-01-01

    This paper focuses on developing an integrated reliable and sophisticated model for ultra large wind turbines And to study the performance and analysis of vector control on large wind turbines. With the advance of power electronics technology, direct driven multi-pole radial flux PMSG (Permanent Magnet Synchronous Generator) has proven to be a good choice for wind turbines manufacturers. To study the wind energy conversion systems, it is important to develop a wind turbin...

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

    Directory of Open Access Journals (Sweden)

    M. Braun

    2017-10-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  14. Scenario Driven Data Modelling: A Method for Integrating Diverse Sources of Data and Data Streams

    Energy Technology Data Exchange (ETDEWEB)

    Griffith, Shelton D [ORNL; Quest, Daniel J [ORNL; Brettin, Thomas S [ORNL; Cottingham, Robert W [ORNL

    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

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Natalie Jane de Vries

    Full Text Available 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.

  17. A model for an acoustically driven microbubble inside a rigid tube

    KAUST Repository

    Qamar, Adnan

    2014-09-10

    A theoretical framework to model the dynamics of acoustically driven microbubble inside a rigid tube is presented. The proposed model is not a variant of the conventional Rayleigh-Plesset category of models. It is derived from the reduced Navier-Stokes equation and is coupled with the evolving flow field solution inside the tube by a similarity transformation approach. The results are computed, and compared with experiments available in literature, for the initial bubble radius of Ro=1.5μm and 2μm for the tube diameter of D=12μm and 200μm with the acoustic parameters as utilized in the experiments. Results compare quite well with the existing experimental data. When compared to our earlier basic model, better agreement on a larger tube diameter is obtained with the proposed coupled model. The model also predicts, accurately, bubble fragmentation in terms of acoustic and geometric parameters.

  18. Modeling feedback control of unstable separatrix location in beam-driven field-reversed configurations

    Science.gov (United States)

    Rath, N.; Onofri, M.; Dettrick, S. A.; Barnes, D. C.; Romero, J.

    2017-04-01

    We present a linear, one-parameter model for rigid displacement of a toroidally symmetric plasma. When the feedback control is feasible, plasma inertia can be neglected, and the instability growth rate is proportional to wall resistivity. We benchmark the linear model against non-linear, hybrid simulations of an axially unstable, beam-driven field-reversed configuration to fix the free parameter of the model. The resulting parameter-free model is validated using linear and non-linear closed-loop simulations with active feedback control by voltage-controlled coils. In closed loop simulations, the predictions of the parameter-free linear model agree satisfactory with the non-linear results. Implications for the feedback control of the positional instability in experiments are discussed. The presented model has been used to guide the design of the feedback control hardware in the C-2W experiment.

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

    Science.gov (United States)

    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.

  20. Quality-Driven Model-Based Design of MultiProcessor Embedded Systems for Highlydemanding Applications

    DEFF Research Database (Denmark)

    Jozwiak, Lech; Madsen, Jan

    2013-01-01

    opportunities have been created. The traditional applications can be served much better and numerous new sorts of embedded systems became technologically feasible and economically justified. Various monitoring, control, communication or multi-media systems that can be put on or embedded in (mobile, poorly......C optimization, adequate resolution of numerous complex design tradeoffs, reduction of the design productivity gap for the increasingly complex and sophisticated systems, reduction of the time-to market and development costs without compromising the system quality, etc. These challenges cannot be well addressed...... of contemporary and future embedded systems and introduction of the quality-driven model-based design methodology based on the paradigms of life-inspired systems and quality-driven design earlier proposed by the first presenter of this tutorial. Subsequently, the actual industrial Intel's ASIP-based MPSo...

  1. Towards the final BSA modeling for the accelerator-driven BNCT facility at INFN LNL

    Energy Technology Data Exchange (ETDEWEB)

    Ceballos, C. [Centro de Aplicaciones Tecnlogicas y Desarrollo Nuclear, 5ta y30, Miramar, Playa, Ciudad Habana (Cuba); Esposito, J., E-mail: juan.esposito@lnl.infn.it [INFN, Laboratori Nazionali di Legnaro (LNL), via dell' Universita, 2, I-35020 Legnaro (PD) (Italy); Agosteo, S. [Politecnico di Milano, Dipartimento di Energia, Piazza Leonardo da Vinci 32, 20133 Milano (Italy)] [INFN, Sezione di Milano, via Celoria 16, 20133 Milano (Italy); Colautti, P.; Conte, V.; Moro, D. [INFN, Laboratori Nazionali di Legnaro (LNL), via dell' Universita, 2, I-35020 Legnaro (PD) (Italy); Pola, A. [Politecnico di Milano, Dipartimento di Energia, Piazza Leonardo da Vinci 32, 20133 Milano (Italy)] [INFN, Sezione di Milano, via Celoria 16, 20133 Milano (Italy)

    2011-12-15

    Some remarkable advances have been made in the last years on the SPES-BNCT project of the Istituto Nazionale di Fisica Nucleare (INFN) towards the development of the accelerator-driven thermal neutron beam facility at the Legnaro National Laboratories (LNL), aimed at the BNCT experimental treatment of extended skin melanoma. The compact neutron source will be produced via the {sup 9}Be(p,xn) reactions using the 5 MeV, 30 mA beam driven by the RFQ accelerator, whose modules construction has been recently completed, into a thick beryllium target prototype already available. The Beam Shaping Assembly (BSA) final modeling, using both neutron converter and the new, detailed, Be(p,xn) neutron yield spectra at 5 MeV energy recently measured at the CN Van de Graaff accelerator at LNL, is summarized here.

  2. A unified modeling approach for physical experiment design and optimization in laser driven inertial confinement fusion

    International Nuclear Information System (INIS)

    Li, Haiyan; Huang, Yunbao; Jiang, Shaoen; Jing, Longfei; Tianxuan, Huang; Ding, Yongkun

    2015-01-01

    Highlights: • A unified modeling approach for physical experiment design is presented. • Any laser facility can be flexibly defined and included with two scripts. • Complex targets and laser beams can be parametrically modeled for optimization. • Automatically mapping of laser beam energy facilitates targets shape optimization. - Abstract: Physical experiment design and optimization is very essential for laser driven inertial confinement fusion due to the high cost of each shot. However, only limited experiments with simple structure or shape on several laser facilities can be designed and evaluated in available codes, and targets are usually defined by programming, which may lead to it difficult for complex shape target design and optimization on arbitrary laser facilities. A unified modeling approach for physical experiment design and optimization on any laser facilities is presented in this paper. Its core idea includes: (1) any laser facility can be flexibly defined and included with two scripts, (2) complex shape targets and laser beams can be parametrically modeled based on features, (3) an automatically mapping scheme of laser beam energy onto discrete mesh elements of targets enable targets or laser beams be optimized without any additional interactive modeling or programming, and (4) significant computation algorithms are additionally presented to efficiently evaluate radiation symmetry on the target. Finally, examples are demonstrated to validate the significance of such unified modeling approach for physical experiments design and optimization in laser driven inertial confinement fusion.

  3. A Model-driven and Service-oriented framework for the business process improvement

    Directory of Open Access Journals (Sweden)

    Andrea Delgado

    2010-07-01

    Full Text Available Business Process Management (BPM importance and benefits for organizations to focus on their business processes is nowadays broadly recognized, as business and technology areas are embracing and adopting the paradigm. The Service Oriented Computing (SOC paradigm bases software development on services to realize business processes. The implementation of business processes as services helps in reducing the gap between these two areas, easing the communication and understanding of business needs. The Model Driven Development (MDD paradigm bases software development in models, metamodels and languages that allow transformation between them. The automatic generation of service models from business process models is a key issue to support the separation of its definition from its technical implementation. In this article, we present MINERVA framework which applies Model Driven Development (MDD and Service Oriented Computing (SOC paradigms to business processes for the continuous business process improvement in organizations, giving support to the stages defined in the business process lifecycle from modeling to evaluation of its execution.

  4. Model-driven engineering of information systems principles, techniques, and practice

    CERN Document Server

    Cretu, Liviu Gabriel

    2015-01-01

    Model-driven engineering (MDE) is the automatic production of software from simplified models of structure and functionality. It mainly involves the automation of the routine and technologically complex programming tasks, thus allowing developers to focus on the true value-adding functionality that the system needs to deliver. This book serves an overview of some of the core topics in MDE. The volume is broken into two sections offering a selection of papers that helps the reader not only understand the MDE principles and techniques, but also learn from practical examples. Also covered are the

  5. The Model-Driven openETCS Paradigm for Secure, Safe and Certifiable Train Control Systems

    DEFF Research Database (Denmark)

    Peleska, Jan; Feuser, Johannes; Haxthausen, Anne Elisabeth

    2012-01-01

    A novel approach to managing development, verification, and validation artifacts for the European Train Control System as open, publicly available items is analyzed and discussed with respect to its implications on system safety, security, and certifiability. After introducing this so-called model......-driven openETCS approach, a threat analysis is performed, identifying both safety and security hazards that may be common to all model-based development paradigms for safety-critical railway control systems, or specific to the openETCS approach. In the subsequent sections state-of-the-art methods suitable...... of security hazards....

  6. Wind-driven rain as a boundary condition for HAM simulations: analysis of simplified modelling approaches

    DEFF Research Database (Denmark)

    Janssen, Hans; Blocken, Bert; Roels, Staf

    2007-01-01

    While the numerical simulation of moisture transfer inside building components is currently undergoing standardisation, the modelling of the atmospheric boundary conditions has received far less attention. This article analyses the modelling of the wind-driven-rain load on building facades...... though: the full variability with the perpendicular wind speed and horizontal rain intensity should be preserved, where feasible, for improved estimations of the moisture transfer in building components. In the concluding section, it is moreover shown that the dependence of the surface moisture transfer...

  7. Hysteresis-controlled instability waves in a scale-free driven current sheet model

    Directory of Open Access Journals (Sweden)

    V. M. Uritsky

    2005-01-01

    Full Text Available Magnetospheric dynamics is a complex multiscale process whose statistical features can be successfully reproduced using high-dimensional numerical transport models exhibiting the phenomenon of self-organized criticality (SOC. Along this line of research, a 2-dimensional driven current sheet (DCS model has recently been developed that incorporates an idealized current-driven instability with a resistive MHD plasma system (Klimas et al., 2004a, b. The dynamics of the DCS model is dominated by the scale-free diffusive energy transport characterized by a set of broadband power-law distribution functions similar to those governing the evolution of multiscale precipitation regions of energetic particles in the nighttime sector of aurora (Uritsky et al., 2002b. The scale-free DCS behavior is supported by localized current-driven instabilities that can communicate in an avalanche fashion over arbitrarily long distances thus producing current sheet waves (CSW. In this paper, we derive the analytical expression for CSW speed as a function of plasma parameters controlling local anomalous resistivity dynamics. The obtained relation indicates that the CSW propagation requires sufficiently high initial current densities, and predicts a deceleration of CSWs moving from inner plasma sheet regions toward its northern and southern boundaries. We also show that the shape of time-averaged current density profile in the DCS model is in agreement with steady-state spatial configuration of critical avalanching models as described by the singular diffusion theory of the SOC. Over shorter time scales, SOC dynamics is associated with rather complex spatial patterns and, in particular, can produce bifurcated current sheets often seen in multi-satellite observations.

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

    Directory of Open Access Journals (Sweden)

    Shaoming Pan

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  10. Process modelling in demand-driven supply chains: A reference model for the fruit industry

    NARCIS (Netherlands)

    Verdouw, C.N.; Beulens, A.J.M.; Trienekens, J.H.; Wolfert, J.

    2010-01-01

    The growing importance of health in consumption is expected to result in a significant increase of European fruit demand. However, the current fruit supply does not yet sufficiently meet demand requirements. This urges fruit supply chains to become more demand-driven, that is, able to continuously

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

    Science.gov (United States)

    Imam, Saheed; Schäuble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

    2015-01-01

    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 more 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. 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. PMID:25999934

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

    Directory of Open Access Journals (Sweden)

    Saheed eImam

    2015-05-01

    Full Text Available 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 more 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. 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.

  13. Modeling Cable and Guide Channel Interaction in a High-Strength Cable-Driven Continuum Manipulator.

    Science.gov (United States)

    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.

  14. Combining engineering and data-driven approaches: Development of a generic fire risk model facilitating calibration

    DEFF Research Database (Denmark)

    De Sanctis, G.; Fischer, K.; Kohler, J.

    2014-01-01

    Fire risk models support decision making for engineering problems under the consistent consideration of the associated uncertainties. Empirical approaches can be used for cost-benefit studies when enough data about the decision problem are available. But often the empirical approaches...... are not detailed enough. Engineering risk models, on the other hand, may be detailed but typically involve assumptions that may result in a biased risk assessment and make a cost-benefit study problematic. In two related papers it is shown how engineering and data-driven modeling can be combined by developing...... a generic risk model that is calibrated to observed fire loss data. Generic risk models assess the risk of buildings based on specific risk indicators and support risk assessment at a portfolio level. After an introduction to the principles of generic risk assessment, the focus of the present paper...

  15. Towards a framework for deriving platform-independent model-driven software product lines

    Directory of Open Access Journals (Sweden)

    Andrés Paz

    2013-05-01

    Full Text Available Model-driven software product lines (MD-SPLs are created from domain models which are transformed, merged and composed with reusable core assets, until software products are produced. Model transformation chains (MTCs must be specified to generate such MD-SPLs. This paper presents a framework for creating platform-independent MD-SPLs; such framework includes a domain specific language (DSL for platform-independent MTC specification and facilities platform-specific MTC generation of several of the most used model transformation frameworks. The DSL also allows product line architects to compose generation taking the need for model transformation strategy and technology interoperability into account and specifying several types of variability involved in such generation.

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

    Directory of Open Access Journals (Sweden)

    B. B. B. Booth

    2013-04-01

    Full Text Available 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

  17. Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

    Directory of Open Access Journals (Sweden)

    Xianming Dou

    2017-12-01

    Full Text Available Accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originating from global environmental change, and providing helpful information about carbon and water content for analyzing and diagnosing past and future climate change. The main focus of the current work was to investigate the feasibility of four comparatively new methods, including generalized regression neural network, group method of data handling (GMDH, extreme learning machine and adaptive neuro-fuzzy inference system (ANFIS, for elucidating the carbon and water fluxes in a forest ecosystem. A comparison was made between these models and two widely used data-driven models, artificial neural network (ANN and support vector machine (SVM. All the models were evaluated based on the following statistical indices: coefficient of determination, Nash-Sutcliffe efficiency, root mean square error and mean absolute error. Results indicated that the data-driven models are capable of accounting for most variance in each flux with the limited meteorological variables. The ANN model provided the best estimates for gross primary productivity (GPP and net ecosystem exchange (NEE, while the ANFIS model achieved the best for ecosystem respiration (R, indicating that no single model was consistently superior to others for the carbon flux prediction. In addition, the GMDH model consistently produced somewhat worse results for all the carbon flux and evapotranspiration (ET estimations. On the whole, among the carbon and water fluxes, all the models produced similar highly satisfactory accuracy for GPP, R and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. Based on these findings, it was concluded that these advanced models are promising alternatives to ANN and SVM for estimating the terrestrial carbon and water fluxes.

  18. Modeling Diffusion and Buoyancy-Driven Convection with Application to Geological CO2 Storage

    KAUST Repository

    Allen, Rebecca

    2015-04-01

    ABSTRACT Modeling Diffusion and Buoyancy-Driven Convection with Application to Geological CO2 Storage Rebecca Allen Geological CO2 storage is an engineering feat that has been undertaken around the world for more than two decades, thus accurate modeling of flow and transport behavior is of practical importance. Diffusive and convective transport are relevant processes for buoyancy-driven convection of CO2 into underlying fluid, a scenario that has received the attention of numerous modeling studies. While most studies focus on Darcy-scale modeling of this scenario, relatively little work exists at the pore-scale. In this work, properties evaluated at the pore-scale are used to investigate the transport behavior modeled at the Darcy-scale. We compute permeability and two different forms of tortuosity, namely hydraulic and diffusive. By generating various pore ge- ometries, we find hydraulic and diffusive tortuosity can be quantitatively different in the same pore geometry by up to a factor of ten. As such, we emphasize that these tortuosities should not be used interchangeably. We find pore geometries that are characterized by anisotropic permeability can also exhibit anisotropic diffusive tortuosity. This finding has important implications for buoyancy-driven convection modeling; when representing the geological formation with an anisotropic permeabil- ity, it is more realistic to also account for an anisotropic diffusivity. By implementing a non-dimensional model that includes both a vertically and horizontally orientated 5 Rayleigh number, we interpret our findings according to the combined effect of the anisotropy from permeability and diffusive tortuosity. In particular, we observe the Rayleigh ratio may either dampen or enhance the diffusing front, and our simulation data is used to express the time of convective onset as a function of the Rayleigh ratio. Also, we implement a lattice Boltzmann model for thermal convective flows, which we treat as an analog for

  19. Unified model and reverse recovery nonlinearities of the driven diode resonator.

    Science.gov (United States)

    de Moraes, Renato Mariz; Anlage, Steven M

    2003-08-01

    We study the origins of period doubling and chaos in the driven series resistor-inductor-varactor diode (RLD) nonlinear resonant circuit. We find that resonators driven at frequencies much higher than the diode reverse recovery rate do not show period doubling. Models of chaos based on the nonlinear capacitance of the varactor diode display a reverse-recovery-like effect, and this effect strongly resembles reverse recovery of real diodes. We find for the first time that in addition to the known dependence of the reverse recovery time on past current maxima, there are also important nonlinear dependencies on pulse frequency, duty cycle, and dc voltage bias. Similar nonlinearities are present in the nonlinear capacitance models of these diodes. We conclude that a history-dependent and nonlinear reverse-recovery time is an essential ingredient for chaotic behavior of this circuit, and demonstrate for the first time that all major competing models have this effect, either explicitly or implicitly. Besides unifying the two major models of RLD chaos, our work reveals that the nonlinearities of the reverse-recovery time must be included for a complete understanding of period doubling and chaos in this circuit.

  20. Time-driven activity-based costing: A dynamic value assessment model in pediatric appendicitis.

    Science.gov (United States)

    Yu, Yangyang R; Abbas, Paulette I; Smith, Carolyn M; Carberry, Kathleen E; Ren, Hui; Patel, Binita; Nuchtern, Jed G; Lopez, Monica E

    2017-06-01

    Healthcare reform policies are emphasizing value-based healthcare delivery. We hypothesize that time-driven activity-based costing (TDABC) can be used to appraise healthcare interventions in pediatric appendicitis. Triage-based standing delegation orders, surgical advanced practice providers, and a same-day discharge protocol were implemented to target deficiencies identified in our initial TDABC model. Post-intervention process maps for a hospital episode were created using electronic time stamp data for simple appendicitis cases during February to March 2016. Total personnel and consumable costs were determined using TDABC methodology. The post-intervention TDABC model featured 6 phases of care, 33 processes, and 19 personnel types. Our interventions reduced duration and costs in the emergency department (-41min, -$23) and pre-operative floor (-57min, -$18). While post-anesthesia care unit duration and costs increased (+224min, +$41), the same-day discharge protocol eliminated post-operative floor costs (-$306). Our model incorporating all three interventions reduced total direct costs by 11% ($2753.39 to $2447.68) and duration of hospitalization by 51% (1984min to 966min). Time-driven activity-based costing can dynamically model changes in our healthcare delivery as a result of process improvement interventions. It is an effective tool to continuously assess the impact of these interventions on the value of appendicitis care. II, Type of study: Economic Analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Thermodynamic modelling of a two-stage absorption chiller driven at two-temperature levels

    International Nuclear Information System (INIS)

    Figueredo, Gustavo R.; Bourouis, Mahmoud; Coronas, Alberto

    2008-01-01

    The thermodynamic model we develop in this paper considers (i) the external irreversibilities of the endoreversible models; (ii) the irreversibilities due to heat losses; and (iii) the generation of internal entropy due to pressure drops and the temperature and concentration gradients. We considered: (i) external heat losses between the generators of high and intermediate pressures and the ambient and between the ambient and the evaporator; and (ii) internal heat losses from the generators towards the condensers and from the absorber towards the evaporator. This simple but precise model faithfully represents the trend towards efficiency variation at partial loads. We have used the model to analyse the behaviour of a water-LiBr double-stage absorption chiller with 200 kW of cooling power. This machine can operate in summer as a double-stage chiller driven by heat at 170 o C from natural gas, as a single-stage chiller driven by heat at 90 o C from solar energy, or simultaneously in combined mode at both temperatures. It can also operate in winter in 'double-lift' mode for heating with a driving heat at 170 o C from natural gas. We studied the efficiency of the machine at partial loads for several solar fractions and the distribution of the heat transfer areas between the various components of the chiller

  2. Model-Checking Driven Design of QoS-Based Routing Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhi Chen

    2015-01-01

    Full Text Available Accurate and reliable routing protocols with Quality of Service (QoS support determine the mission-critical application efficiency in WSNs. This paper proposes a model-checking design driven framework for designing the QoS-based routing protocols of WSNs, which involves the light-weight design process, the timed automata model, and the alternative QoS verification properties. The accurate feedback of continually model checking in the iterative design process effectively stimulates the parameter tuning of the protocols. We demonstrate the straightforward and modular characteristics of the proposed framework in designing a prototype QoS-based routing protocol. The prototype study shows that the model-checking design framework may complement other design methods and ensure the QoS implementation of the QoS-based routing protocol design for WSNs.

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

    Science.gov (United States)

    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.

  4. Data-Driven Residential Load Modeling and Validation in GridLAB-D

    Energy Technology Data Exchange (ETDEWEB)

    Gotseff, Peter; Lundstrom, Blake

    2017-05-11

    Accurately characterizing the impacts of high penetrations of distributed energy resources (DER) on the electric distribution system has driven modeling methods from traditional static snap shots, often representing a critical point in time (e.g., summer peak load), to quasi-static time series (QSTS) simulations capturing all the effects of variable DER, associated controls and hence, impacts on the distribution system over a given time period. Unfortunately, the high time resolution DER source and load data required for model inputs is often scarce or non-existent. This paper presents work performed within the GridLAB-D model environment to synthesize, calibrate, and validate 1-second residential load models based on measured transformer loads and physics-based models suitable for QSTS electric distribution system modeling. The modeling and validation approach taken was to create a typical GridLAB-D model home that, when replicated to represent multiple diverse houses on a single transformer, creates a statistically similar load to a measured load for a given weather input. The model homes are constructed to represent the range of actual homes on an instrumented transformer: square footage, thermal integrity, heating and cooling system definition as well as realistic occupancy schedules. House model calibration and validation was performed using the distribution transformer load data and corresponding weather. The modeled loads were found to be similar to the measured loads for four evaluation metrics: 1) daily average energy, 2) daily average and standard deviation of power, 3) power spectral density, and 4) load shape.

  5. Data-driven modeling, control and tools for cyber-physical energy systems

    Science.gov (United States)

    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

  6. Modelling and Control of the Multi-Stage Cable Pulley-Driven Flexible-Joint Robot

    Directory of Open Access Journals (Sweden)

    Phongsaen Pitakwatchara

    2014-07-01

    Full Text Available This work is concerned with the task space impedance control of a robot driven through a multi-stage nonlinear flexible transmission system. Specifically, a two degrees-of-freedom cable pulley-driven flexible-joint robot is considered. Realistic modelling of the system is developed within the bond graph modelling framework. The model captures the nonlinear compliance behaviour of the multi-stage cable pulley transmission system, the spring effect of the augmented counterbalancing mechanism, the major loss throughout the system elements, and the typical inertial dynamics of the robot. Next, a task space impedance controller based on limited information about the angle and the current of the motors is designed. The motor current is used to infer the transmitted torque, by which the motor inertia may be modulated. The motor angle is employed to estimate the stationary distal robot link angle and the robot joint velocity. They are used in the controller to generate the desired damping force and to shape the potential energy of the flexible joint robot system to the desired configuration. Simulation and experimental results of the controlled system signify the competency of the proposed control law.

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

    Science.gov (United States)

    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.

  8. A topography-driven hydrological model in the Heihe River, China

    Science.gov (United States)

    Gao, Hongkai; Savenije, Hubert H. G.; Hrachowitz, Markus; Fenicia, Fabrizio; Gharari, Shervan

    2013-04-01

    A new topography-driven hydrological model was developed and tested in the upper Heihe River Basin and validated in two nested sub-basins using independent remote sensing sources. Topography is closely related to geomorphology, land use, ecosystems, and, as a result, it reflects the dominant hydrological processes. However, existing models use topography in a rather basic way. In this study, we classified the river basin into four landscapes by using two topographic indicators: the elevation above sea level, and the Height Above the Nearest Drainage (HAND). On the basis of this classification each landscape class was described by a different conceptual model. During this translation process, we used soft data and expert knowledge to constrain the model structure and parameter ranges. After calibration, additional data was used for validation, including hydrograph data in different periods and in nested gauge stations. In addition we compared modeled evaporation with evaporation maps obtained from remote sensing. The novelty of this study is threefold: (1) we used a new method for topography-driven landscape classification and successfully translated this classification into model structures describing the dominant hydrological processes in the different landscapes; (2) the two nested catchments have quite distinct landscapes which made the nested validation process more stringent; (3) independent evaporation data was used to further validate the model. Several interesting conclusions are drawn: (1) the classification method which combined HAND and elevation is powerful to separate different landscapes; (2) the wetland and the summit area covered by bare soil/rock are the main peak flow producing region in the Heihe River Basin. The hillslopes with grassland and the summit area are mostly responsible for deep percolation and generate the largest proportion of the base flow ; (3) almost all the rainfall in the forested area of the upper Heihe River Basin is

  9. Kinematics Modelling of Tendon-Driven Continuum Manipulator with Crossed Notches

    Science.gov (United States)

    Yang, Z. X.; Yang, W. L.; Du, Z. J.

    2018-03-01

    Single port surgical robot (SPSR) is a giant leap in the development of minimally invasive surgical robot. An innovative manipulator with high control accuracy and good kinematic dexterity can reduce wound, expedite recovery, and improve the success rate. This paper presents a tendon-driven continuum manipulator with crossed notches. This manipulator has two degrees of freedom (DOF), which possesses good flexibility and high capacity. Then based on cantilever beam theory, a mechanics model is proposed, which connects external force and deformation of a single flexible ring (SFR). By calculating the deformation of each SFR, the manipulator is considered as a series robot whose joint numbers is equal to SFR numbers, and the kinematics model is established through Denavit-Hartenberg (D-H) procedure. In this paper, the total manipulator is described as a curve tube whose curvature is increased from tip to base. Experiments were conducted and the comparison between theoretical and actual results proved the rationality of the models.

  10. Modeling of dielectric barrier discharge plasma actuators driven by repetitive nanosecond pulses

    International Nuclear Information System (INIS)

    Likhanskii, Alexandre V.; Shneider, Mikhail N.; Macheret, Sergey O.; Miles, Richard B.

    2007-01-01

    A detailed physical model for an asymmetric dielectric barrier discharge (DBD) in air driven by repetitive nanosecond voltage pulses is developed. In particular, modeling of DBD with high voltage repetitive negative and positive nanosecond pulses combined with positive dc bias is carried out. Operation at high voltage is compared with operation at low voltage, highlighting the advantage of high voltages, however the effect of backward-directed breakdown in the case of negative pulses results in a decrease of the integral momentum transferred to the gas. The use of positive repetitive pulses with dc bias is demonstrated to be promising for DBD performance improvement. The effects of the voltage waveform not only on force magnitude, but also on the spatial profile of the force, are shown. The crucial role of background photoionization in numerical modeling of ionization waves (streamers) in DBD plasmas is demonstrated

  11. Adjusting weather radar data to rain gauge measurements with data-driven models

    Science.gov (United States)

    Teschl, Reinhard; Randeu, Walter; Teschl, Franz

    2010-05-01

    Weather radar networks provide data with good spatial coverage and temporal resolution. Hence they are able to describe the variability of precipitation. Typical radar stations determine the rain rate for every square kilometre and make a full volume scan within about 5 minutes. A weakness however, is their often poor metering precision limiting the applicability of the radar for hydrological purposes. In contrast to rain gauges, which measure precipitation directly on the ground, the radar determines the reflectivity aloft and remote. Due to this principle, several sources of possible errors occur. Therefore improving the radar estimates of rainfall is still a vital topic in radar meteorology and hydrology. This paper presents data-driven approaches to improve radar estimates of rainfall by mapping radar reflectivity measurements Z to rain gauge data R. The analysis encompasses several input configurations and data-driven models. Reflectivity measurements at a constant altitude and the vertical profiles of reflectivity above a rain gauge are used as input parameters. The applied models are Artificial Neural Network (ANN), Model Tree (MT), and IBk a k-nearest-neighbour classifier. The relationship found between the data of a rain gauge and the reflectivity measurements is subsequently applied to another site with comparable terrain. Based on this independent dataset the performance of the data-driven models in the various input configurations is evaluated. For this study, rain gauge and radar data from the province of Styria, Austria, were available. The data sets extend over a two-year period (2001 and 2002). The available rain gauges use the tipping bucket principle with a resolution of 0.1 mm. Reflectivity measurements are obtained from the Doppler weather radar station on Mt. Zirbitzkogel (by courtesy of AustroControl GmbH). The designated radar is a high-resolution C-band weather-radar situated at an altitude of 2372 m above mean sea level. The data-driven

  12. Dynamic model reduction using data-driven Loewner-framework applied to thermally morphing structures

    Science.gov (United States)

    Phoenix, Austin A.; Tarazaga, Pablo A.

    2017-05-01

    The work herein proposes the use of the data-driven Loewner-framework for reduced order modeling as applied to dynamic Finite Element Models (FEM) of thermally morphing structures. The Loewner-based modeling approach is computationally efficient and accurately constructs reduced models using analytical output data from a FEM. This paper details the two-step process proposed in the Loewner approach. First, a random vibration FEM simulation is used as the input for the development of a Single Input Single Output (SISO) data-based dynamic Loewner state space model. Second, an SVD-based truncation is used on the Loewner state space model, such that the minimal, dynamically representative, state space model is achieved. For this second part, varying levels of reduction are generated and compared. The work herein can be extended to model generation using experimental measurements by replacing the FEM output data in the first step and following the same procedure. This method will be demonstrated on two thermally morphing structures, a rigidly fixed hexapod in multiple geometric configurations and a low mass anisotropic morphing boom. This paper is working to detail the method and identify the benefits of the reduced model methodology.

  13. A data-driven, mathematical model of mammalian cell cycle regulation.

    Directory of Open Access Journals (Sweden)

    Michael C Weis

    Full Text Available Few of >150 published cell cycle modeling efforts use significant levels of data for tuning and validation. This reflects the difficultly to generate correlated quantitative data, and it points out a critical uncertainty in modeling efforts. To develop a data-driven model of cell cycle regulation, we used contiguous, dynamic measurements over two time scales (minutes and hours calculated from static multiparametric cytometry data. The approach provided expression profiles of cyclin A2, cyclin B1, and phospho-S10-histone H3. The model was built by integrating and modifying two previously published models such that the model outputs for cyclins A and B fit cyclin expression measurements and the activation of B cyclin/Cdk1 coincided with phosphorylation of histone H3. The model depends on Cdh1-regulated cyclin degradation during G1, regulation of B cyclin/Cdk1 activity by cyclin A/Cdk via Wee1, and transcriptional control of the mitotic cyclins that reflects some of the current literature. We introduced autocatalytic transcription of E2F, E2F regulated transcription of cyclin B, Cdc20/Cdh1 mediated E2F degradation, enhanced transcription of mitotic cyclins during late S/early G2 phase, and the sustained synthesis of cyclin B during mitosis. These features produced a model with good correlation between state variable output and real measurements. Since the method of data generation is extensible, this model can be continually modified based on new correlated, quantitative data.

  14. Preprocessing and Optimization of Smooth Data-driven Model for Emergency Conditions Against Air Pollution

    Directory of Open Access Journals (Sweden)

    Ali Ardalan

    2016-10-01

    Full Text Available Magnitudes of the air pollution depend on various variables. Preprocessing and optimisation processes are necessary to discover the complexity of the relationship of the data for more accurate and efficient predictions. These techniques help to clean the datasets and to find the best structure of the smooth data model. The Gamma test (GT and Genetic Algorithm (GA are practical tools which can be applied for preprocessing and optimising data models. Regarding building a smooth data model, the developed artificial neural networks are functional optimisation strategies which are suitable for ANN training. Moreover, local linear regression (LLR and dynamic local linear regression (DLLR models are effective due to the high density of our normalised dataset. In this regard, we developed a process to construct a smooth data model to support environmental decision making in air pollution emergency conditions. The main objective of this work was to set an appropriate algorithm by preprocessing and optimising a set of the data model for developing smooth data-driven models which could play a significant role in early warning systems in regard to the human health. The data sets included the meteorological and air pollutant variables as inputs/predictors and emergency medical service clients as outputs. The GT and GA were applied to analyse and optimise the input variables. Three types of ANNS (ANN1, ANN2, and ANN3, (LLR, and (DLLR techniques were used to establish the models. Finally, a smooth data model was constructed and evaluated.

  15. An analytical model for gas overpressure in slug-driven explosions: Insights into Strombolian volcanic eruptions

    Science.gov (United States)

    Del Bello, Elisabetta; Llewellin, Edward W.; Taddeucci, Jacopo; Scarlato, Piergiorgio; Lane, Steve J.

    2012-02-01

    Strombolian eruptions, common at basaltic volcanoes, are mildly explosive events that are driven by a large bubble of magmatic gas (a slug) rising up the conduit and bursting at the surface. Gas overpressure within the bursting slug governs explosion dynamics and vigor and is the main factor controlling associated acoustic and seismic signals. We present a theoretical investigation of slug overpressure based on magma-static and geometric considerations and develop a set of equations that can be used to calculate the overpressure in a slug when it bursts, slug length at burst, and the depth at which the burst process begins. We find that burst overpressure is controlled by two dimensionless parameters: V', which represents the amount of gas in the slug, and A', which represents the thickness of the film of magma that falls around the rising slug. Burst overpressure increases nonlinearly as V' and A' increase. We consider two eruptive scenarios: (1) the "standard model," in which magma remains confined to the vent during slug expansion, and (2) the "overflow model," in which slug expansion is associated with lava effusion, as occasionally observed in the field. We find that slug overpressure is higher for the overflow model by a factor of 1.2-2.4. Applying our model to typical Strombolian eruptions at Stromboli, we find that the transition from passive degassing to explosive bursting occurs for slugs with volume >24-230 m3, depending on magma viscosity and conduit diameter, and that at burst, a typical Strombolian slug (with a volume of 100-1000 m3) has an internal gas pressure of 1-5 bars and a length of 13-120 m. We compare model predictions with field data from Stromboli for low-energy "puffers," mildly explosive Strombolian eruptions, and the violently explosive 5 April 2003 paroxysm. We find that model predictions are consistent with field observations across this broad spectrum of eruptive styles, suggesting a common slug-driven mechanism; we propose that

  16. Model-Driven Methodology for Rapid Deployment of Smart Spaces Based on Resource-Oriented Architectures

    Directory of Open Access Journals (Sweden)

    José R. Casar

    2012-07-01

    Full Text Available 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.

  17. Model-Driven Methodology for Rapid Deployment of Smart Spaces Based on Resource-Oriented Architectures

    Science.gov (United States)

    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. PMID:23012544

  18. Fast parallel algorithm for three-dimensional distance-driven model in iterative computed tomography reconstruction

    International Nuclear Information System (INIS)

    Chen Jian-Lin; Li Lei; Wang Lin-Yuan; Cai Ai-Long; Xi Xiao-Qi; Zhang Han-Ming; Li Jian-Xin; Yan Bin

    2015-01-01

    The projection matrix model is used to describe the physical relationship between reconstructed object and projection. Such a model has a strong influence on projection and backprojection, two vital operations in iterative computed tomographic reconstruction. The distance-driven model (DDM) is a state-of-the-art technology that simulates forward and back projections. This model has a low computational complexity and a relatively high spatial resolution; however, it includes only a few methods in a parallel operation with a matched model scheme. This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations. Our proposed model has been implemented on a GPU (graphic processing unit) platform and has achieved satisfactory computational efficiency with no approximation. The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop, respectively, with an image size of 256×256×256 and 360 projections with a size of 512×512. We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation. The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction. (paper)

  19. Development of Bubble Driven Flow CFD Model Applied for Aluminium Smelting Cells

    Directory of Open Access Journals (Sweden)

    Y.Q. Feng

    2010-09-01

    Full Text Available This paper presents the development of a computational fluid dynamics (CFD model for the study of bubble driven bath flow in aluminium reduction cells. For validation purposes, the model development was conducted using a full scale air -water model of part of an aluminium reduction cell as a test-bed. The bubble induced turbulence has been modelled by either modifying bubble induced turbulence viscosity directly or by modifying bubble induced turbulence kinetic energy in a standard k- ε turbulence model. The relative performance of the two modelling approaches has been examined through comparison with experimental data taken under similar conditions using Particle Image Velocimetry (PIV. Detailed comparison has been conducted by point-wise comparison of liquid velocities to quantify the level of agreement between CFD simulation and PIV measurement. Both models can capture the key flow patterns determined by PIV measurement, while the modified turbulence kinetic energy model gives better agreement with flow patterns in the gap between anode and cathode.

  20. Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends.

    Science.gov (United States)

    Geerts, Hugo; Hofmann-Apitius, Martin; Anastasio, Thomas J

    2017-11-01

    Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to

  1. Deriving albedo maps for HAPEX-Sahel from ASAS data using kernel-driven BRDF models

    Directory of Open Access Journals (Sweden)

    P. Lewis

    1999-01-01

    Full Text Available This paper describes the application and testing of a method for deriving spatial estimates of albedo from multi-angle remote sensing data. Linear kernel-driven models of surface bi-directional reflectance have been inverted against high spatial resolution multi-angular, multi- spectral airborne data of the principal cover types within the HAPEX-Sahel study site in Niger, West Africa. The airborne data are obtained from the NASA Airborne Solid-state Imaging Spectrometer (ASAS instrument, flown in Niger in September and October 1992. The maps of model parameters produced are used to estimate integrated reflectance properties related to spectral albedo. Broadband albedo has been estimated from this by weighting the spectral albedo for each pixel within the map as a function of the appropriate spectral solar irradiance and proportion of direct and diffuse illumination. Partial validation of the results was performed by comparing ASAS reflectance and derived directional-hemispherical reflectance with simulations of a millet canopy made with a complex geometric canopy reflectance model, the Botanical Plant Modelling System (BPMS. Both were found to agree well in magnitude. Broadband albedo values derived from the ASAS data were compared with ground-based (point sample albedo measurements and found to agree extremely well. These results indicate that the linear kernel-driven modelling approach, which is to be used operationally to produce global 16 day, 1 km albedo maps from forthcoming NASA Earth Observing System spaceborne data, is both sound and practical for the estimation of angle-integrated spectral reflectance quantities related to albedo. Results for broadband albedo are dependent on spectral sampling and on obtaining the correct spectral weigthings.

  2. A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model

    Science.gov (United States)

    Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

    2017-11-01

    In this study, a data-driven method for predicting CO2 leaks and associated concentrations from geological CO2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems.

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

    Science.gov (United States)

    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.

  4. Analytical method of CIM to PIM transformation in Model Driven Architecture (MDA

    Directory of Open Access Journals (Sweden)

    Martin Kardos

    2010-06-01

    Full Text Available Information system’s models on higher level of abstraction have become a daily routine in many software companies. The concept of Model Driven Architecture (MDA published by standardization body OMG1 since 2001 has become a concept for creation of software applications and information systems. MDA specifies four levels of abstraction: top three levels are created as graphical models and the last one as implementation code model. Many research works of MDA are focusing on the lower levels and transformations between each other. The top level of abstraction, called Computation Independent Model (CIM and its transformation to the lower level called Platform Independent Model (PIM is not so extensive research topic. Considering to a great importance and usability of this level in practice of IS2Keywords: transformation, MDA, CIM, PIM, UML, DFD. development now our research activity is focused to this highest level of abstraction – CIM and its possible transformation to the lower PIM level. In this article we are presenting a possible solution of CIM modeling and its analytic method of transformation to PIM.

  5. Comparison of data-driven Takagi Sugeno models of rainfall discharge dynamics

    Science.gov (United States)

    Vernieuwe, Hilde; Georgieva, Olga; De Baets, Bernard; Pauwels, Valentijn R. N.; Verhoest, Niko E. C.; De Troch, François P.

    2005-02-01

    Over the last decades, several data-driven techniques have been applied to model the rainfall-discharge dynamics of catchments. Among these techniques are fuzzy rule-based models, which attempt to describe the catchment response to rainfall input through fuzzy relationships. In this paper, we demonstrate three different methods for constructing fuzzy rule-based models of the Takagi-Sugeno type relating rainfall to catchment discharge. They correspond to the grid partitioning, subtractive clustering, and Gustafson-Kessel (GK) clustering identification methods. The data set used to parametrize and validate the models consists of hourly precipitation and discharge records. The models are parametrized using a 1-year identification data set and are then applied to a 4-year data set. Although the models show a similar performance, the best results are obtained for the GK method. A real-time flood forecasting algorithm is then developed, in which discharge measurements are assimilated into the model at either an hourly or a daily time step. The results suggest that the GK method can potentially be used as an operational flood forecasting tool with a low computational cost.

  6. Line-driven disc wind model for ultrafast outflows in active galactic nuclei - scaling with luminosity

    Science.gov (United States)

    Nomura, M.; Ohsuga, K.

    2017-03-01

    In order to reveal the origin of the ultrafast outflows (UFOs) that are frequently observed in active galactic nuclei (AGNs), we perform two-dimensional radiation hydrodynamics simulations of the line-driven disc winds, which are accelerated by the radiation force due to the spectral lines. The line-driven winds are successfully launched for the range of MBH = 106-9 M⊙ and ε = 0.1-0.5, and the resulting mass outflow rate (dot{M_w}), momentum flux (dot{p_w}), and kinetic luminosity (dot{E_w}) are in the region containing 90 per cent of the posterior probability distribution in the dot{M}_w-Lbol plane, dot{p}_w-Lbol plane, and dot{E}_w-Lbol plane shown in Gofford et al., where MBH is the black hole mass, ε is the Eddington ratio, and Lbol is the bolometric luminosity. The best-fitting relations in Gofford et al., d log dot{M_w}/d log {L_bol}˜ 0.9, d log dot{p_w}/d log {L_bol}˜ 1.2, and d log dot{E_w}/d log {L_bol}˜ 1.5, are roughly consistent with our results, d log dot{M_w}/d log {L_bol}˜ 9/8, d log dot{p_w}/d log {L_bol}˜ 10/8, and d log dot{E_w}/d log {L_bol}˜ 11/8. In addition, our model predicts that no UFO features are detected for the AGNs with ε ≲ 0.01, since the winds do not appear. Also, only AGNs with MBH ≲ 108 M⊙ exhibit the UFOs when ε ∼ 0.025. These predictions nicely agree with the X-ray observations. These results support that the line-driven disc wind is the origin of the UFOs.

  7. Maximizing hysteretic losses in magnetic ferrite nanoparticles via model-driven synthesis and materials optimization.

    Science.gov (United States)

    Chen, Ritchie; Christiansen, Michael G; Anikeeva, Polina

    2013-10-22

    This article develops a set of design guidelines for maximizing heat dissipation characteristics of magnetic ferrite MFe2O4 (M = Mn, Fe, Co) nanoparticles in alternating magnetic fields. Using magnetic and structural nanoparticle characterization, we identify key synthetic parameters in the thermal decomposition of organometallic precursors that yield optimized magnetic nanoparticles over a wide range of sizes and compositions. The developed synthetic procedures allow for gram-scale production of magnetic nanoparticles stable in physiological buffer for several months. Our magnetic nanoparticles display some of the highest heat dissipation rates, which are in qualitative agreement with the trends predicted by a dynamic hysteresis model of coherent magnetization reversal in single domain magnetic particles. By combining physical simulations with robust scalable synthesis and materials characterization techniques, this work provides a pathway to a model-driven design of magnetic nanoparticles tailored to a variety of biomedical applications ranging from cancer hyperthermia to remote control of gene expression.

  8. Modelling perceptions of criminality and remorse from faces using a data-driven computational approach.

    Science.gov (United States)

    Funk, Friederike; Walker, Mirella; Todorov, Alexander

    2017-11-01

    Perceptions of criminality and remorse are critical for legal decision-making. While faces perceived as criminal are more likely to be selected in police lineups and to receive guilty verdicts, faces perceived as remorseful are more likely to receive less severe punishment recommendations. To identify the information that makes a face appear criminal and/or remorseful, we successfully used two different data-driven computational approaches that led to convergent findings: one relying on the use of computer-generated faces, and the other on photographs of people. In addition to visualising and validating the perceived looks of criminality and remorse, we report correlations with earlier face models of dominance, threat, trustworthiness, masculinity/femininity, and sadness. The new face models of criminal and remorseful appearance contribute to our understanding of perceived criminality and remorse. They can be used to study the effects of perceived criminality and remorse on decision-making; research that can ultimately inform legal policies.

  9. A self-consistent LTE model of a microwave-driven, high-pressure sulfur lamp

    Energy Technology Data Exchange (ETDEWEB)

    Johnston, C.W.; Mullen, J.J.A.M. van der [Department of Applied Physics, Eindhoven University of Technology (Netherlands)]. E-mails: C.W.Johnston@tue.nl; J.J.A.M.v.d.Mullen@tue.nl; Heijden, H.W.P. van der; Janssen, G.M.; Dijk, J. van [Department of Applied Physics, Eindhoven University of Technology (Netherlands)

    2002-02-21

    A one-dimensional LTE model of a microwave-driven sulfur lamp is presented to aid our understanding of the discharge. The energy balance of the lamp is determined by Ohmic input on one hand and transport due to conductive heat transfer and molecular radiation on the other. We discuss the origin of operational trends in the spectrum, present the model and discuss how the material properties of the plasma are determined. Not only are temperature profiles and electric field strengths simulated but also the spectrum of the lamp from 300 to 900 nm under various conditions of input power and lamp filling pressure. We show that simulated spectra demonstrate observed trends and that radiated power increases linearly with input power as is also found from experiment. (author)

  10. Mathematical Model of Growth Factor Driven Haptotaxis and Proliferation in a Tissue Engineering Scaffold

    KAUST Repository

    Pohlmeyer, J. V.

    2013-01-29

    Motivated by experimental work (Miller et al. in Biomaterials 27(10):2213-2221, 2006, 32(11):2775-2785, 2011) we investigate the effect of growth factor driven haptotaxis and proliferation in a perfusion tissue engineering bioreactor, in which nutrient-rich culture medium is perfused through a 2D porous scaffold impregnated with growth factor and seeded with cells. We model these processes on the timescale of cell proliferation, which typically is of the order of days. While a quantitative representation of these phenomena requires more experimental data than is yet available, qualitative agreement with preliminary experimental studies (Miller et al. in Biomaterials 27(10):2213-2221, 2006) is obtained, and appears promising. The ultimate goal of such modeling is to ascertain initial conditions (growth factor distribution, initial cell seeding, etc.) that will lead to a final desired outcome. © 2013 Society for Mathematical Biology.

  11. Discrete Event Modeling and Simulation-Driven Engineering for the ATLAS Data Acquisition Network

    CERN Document Server

    Bonaventura, Matias Alejandro; The ATLAS collaboration; Castro, Rodrigo Daniel

    2016-01-01

    We present an iterative and incremental development methodology for simulation models in network engineering projects. Driven by the DEVS (Discrete Event Systems Specification) formal framework for modeling and simulation we assist network design, test, analysis and optimization processes. A practical application of the methodology is presented for a case study in the ATLAS particle physics detector, the largest scientific experiment built by man where scientists around the globe search for answers about the origins of the universe. The ATLAS data network convey real-time information produced by physics detectors as beams of particles collide. The produced sub-atomic evidences must be filtered and recorded for further offline scrutiny. Due to the criticality of the transported data, networks and applications undergo careful engineering processes with stringent quality of service requirements. A tight project schedule imposes time pressure on design decisions, while rapid technology evolution widens the palett...

  12. Optical and atomic stochastic resonances in the driven dissipative Jaynes-Cummings model

    Science.gov (United States)

    Qiu, Qingyang; Tao, Shengdan; Liu, Cunjin; Guan, Shengguo; Xie, Min; Fan, Bixuan

    2017-12-01

    In this paper, we study the stochastic resonance (SR) effect in a driven dissipative Jaynes-Cummings model. The SR effect is systematically investigated in the semiclassical and full quantum frameworks, and in both cases we find that SRs simultaneously occur for optical and atomic degrees of freedom. In particular, at zero temperature, quantum SR can be induced merely by vacuum fluctuations. Although the qualitative features of semiclassical SR and quantum SR are similar, their mechanisms are completely different: semiclassical SR is induced by thermal activation while quantum SR is induced by quantum-tunneling-assisted transitions. Our results provide a theoretical basis for experimentally observing and studying the SR phenomenon of the Jaynes-Cummings model in the quantum regime.

  13. Constitutive modeling of stress-driven grain growth in nanocrystalline metals

    KAUST Repository

    Gürses, Ercan

    2013-02-08

    In this work, we present a variational multiscale model for grain growth in face-centered cubic nanocrystalline (nc) metals. In particular, grain-growth-induced stress softening and the resulting relaxation phenomena are addressed. The behavior of the polycrystal is described by a conventional Taylor-type averaging scheme in which the grains are treated as two-phase composites consisting of a grain interior phase and a grain boundary-affected zone. Furthermore, a grain-growth law that captures the experimentally observed characteristics of the grain coarsening phenomena is proposed. To this end, the grain size is not taken as constant and varies according to the proposed stress-driven growth law. Several parametric studies are conducted to emphasize the influence of the grain-growth rule on the overall macroscopic response. Finally, the model is shown to provide a good description of the experimentally observed grain-growth-induced relaxation in nc-copper. © 2013 IOP Publishing Ltd.

  14. The OSMOSIS Model of the Wind-Driven Ocean Surface Boundary Layer.

    Science.gov (United States)

    Grant, A. L.; Belcher, S. E.; Pearson, B.; Polton, J.

    2016-02-01

    In the wind-driven ocean surface boundary layer (OSBL) the vertical velocity variance is observed to be larger than in shear driven turbulence. The observed variances are consistent with the results from large-eddy simulations (LES) which parametrize the interaction between the Stokes drift of the surface waves and vorticity. The resulting flow is known as Langmuir turbulence and the close connection between winds and waves suggests that Langmuir turbulence is common in the OSBL. This poster describes a model of the OSBL, developed as part of the OSMOSIS project, in which mixing is by Langmuir turbulence. The transports of momentum, heat and salinity are represented by a first-order closure scheme with flux-gradient relationships that include non-gradient contributions. In this the model is similar to the KPP scheme which uses flux-gradient relationships with non-gradient contributions to represent scalar transports. The flux-gradient relationships are derived from an analysis of the turbulent flux budgets of momentum and scalars (heat) obtained from LES. The non-gradient terms represent the contributions to the turbulent flux by the terms in the turbulent flux budget that represent the effects of the Stokes shear, buoyancy and turbulent transport. The eddy viscosity, diffusivities and non-gradient components are represented by similarity profiles. The depth of the boundary layer is determined by a prognostic equation, which represents the time variation of the boundary layer depth in both unstable and stable conditions. It is based on the equation for the depth integrated potential energy combined with a parametrization of the turbulent kinetic energy budget. The use of the prognostic equation allows the effects of Langmuir turbulence on boundary layer depth to be explicitly represented in the model. Comparison with the results from LES of the diurnal cycle of the OSBL are presented as a test for the model.

  15. Simulation of shallow groundwater levels: Comparison of a data-driven and a conceptual model

    Science.gov (United States)

    Fahle, Marcus; Dietrich, Ottfried; Lischeid, Gunnar

    2015-04-01

    Despite an abundance of models aimed at simulating shallow groundwater levels, application of such models is often hampered by a lack of appropriate input data. Difficulties especially arise with regard to soil data, which are typically hard to obtain and prone to spatial variability, eventually leading to uncertainties in the model results. Modelling approaches relying entirely on easily measured quantities are therefore an alternative to encourage the applicability of models. We present and compare two models for calculating 1-day-ahead predictions of the groundwater level that are only based on measurements of potential evapotranspiration, precipitation and groundwater levels. The first model is a newly developed conceptual model that is parametrized using the White method (which estimates the actual evapotranspiration on basis of diurnal groundwater fluctuations) and a rainfall-response ratio. Inverted versions of the two latter approaches are then used to calculate the predictions of the groundwater level. Furthermore, as a completely data-driven alternative, a simple feed-forward multilayer perceptron neural network was trained based on the same inputs and outputs. Data of 4 growing periods (April to October) from a study site situated in the Spreewald wetland in North-east Germany were taken to set-up the models and compare their performance. In addition, response surfaces that relate model outputs to combinations of different input variables are used to reveal those aspects in which the two approaches coincide and those in which they differ. Finally, it will be evaluated whether the conceptual approach can be enhanced by extracting knowledge of the neural network. This is done by replacing in the conceptual model the default function that relates groundwater recharge and groundwater level, which is assumed to be linear, by the non-linear function extracted from the neural network.

  16. Loss Modeling with a Data-Driven Approach in Event-Based Rainfall-Runoff Analysis

    Science.gov (United States)

    Chua, L. H. C.

    2012-04-01

    Mathematical models require the estimation of rainfall abstractions for accurate predictions of runoff. Although loss models such as the constant loss and exponential loss models are commonly used, these methods are based on simplified assumptions of the physical process. A new approach based on the data driven paradigm to estimate rainfall abstractions is proposed in this paper. The proposed data driven model, based on the artificial neural network (ANN) does not make any assumptions on the loss behavior. The estimated discharge from a physically-based model, obtained from the kinematic wave (KW) model assuming zero losses, was used as the only input to the ANN. The output is the measured discharge. Thus, the ANN functions as a black-box loss model. Two sets of data were analyzed for this study. The first dataset consists of rainfall and runoff data, measured from an artificial catchment (area = 25 m2) comprising two overland planes (slope = 11%), 25m long, transversely inclined towards a rectangular channel (slope = 2%) which conveyed the flow, recorded using calibrated weigh tanks, to the outlet. Two rain gauges, each placed 6.25 m from either ends of the channel, were used to record rainfall. Data for six storm events over the period between October 2002 and December 2002 were analyzed. The second dataset was obtained from the Upper Bukit Timah catchment (area = 6.4 km2) instrumented with two rain gauges and a flow measuring station. A total of six events recorded between November 1987 and July 1988 were selected for this study. The runoff predicted by the ANN was compared with the measured runoff. In addition, results from KW models developed for both the catchments were used as a benchmark. The KW models were calibrated assuming the loss rate for an average event for each of the datasets. The results from both the ANN and KW models agreed well with the runoff measured from the artificial catchment. The KW model is expected to perform well since the catchment

  17. A data-driven framework for identifying nonlinear dynamic models of genetic parts.

    Science.gov (United States)

    Krishnanathan, Kirubhakaran; Anderson, Sean R; Billings, Stephen A; Kadirkamanathan, Visakan

    2012-08-17

    A key challenge in synthetic biology is the development of effective methodologies for characterization of component genetic parts in a form suitable for dynamic analysis and design. In this investigation we propose the use of a nonlinear dynamic modeling framework that is popular in the field of control engineering but is novel to the field of synthetic biology: Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX). The framework is applied to the identification of a genetic part BBa_T9002 as a case study. A concise model is developed that exhibits accurate representation of the system dynamics and a structure that is compact and consistent across cell populations. A comparison is made with a biochemical model, derived from a simple enzymatic reaction scheme. The NARMAX model is shown to be comparably simple but exhibits much greater prediction accuracy on the experimental data. These results indicate that the data-driven NARMAX framework is an attractive technique for dynamic modeling of genetic parts.

  18. A beamline systems model for Accelerator-Driven Transmutation Technology (ADTT) facilities

    Energy Technology Data Exchange (ETDEWEB)

    Todd, A.M.M.; Paulson, C.C.; Peacock, M.A. [Grumman Research and Development Center, Princeton, NJ (United States)] [and others

    1995-10-01

    A beamline systems code, that is being developed for Accelerator-Driven Transmutation Technology (ADTT) facility trade studies, is described. The overall program is a joint Grumman, G.H. Gillespie Associates (GHGA) and Los Alamos National Laboratory effort. The GHGA Accelerator Systems Model (ASM) has been adopted as the framework on which this effort is based. Relevant accelerator and beam transport models from earlier Grumman systems codes are being adapted to this framework. Preliminary physics and engineering models for each ADTT beamline component have been constructed. Examples noted include a Bridge Coupled Drift Tube Linac (BCDTL) and the accelerator thermal system. A decision has been made to confine the ASM framework principally to beamline modeling, while detailed target/blanket, balance-of-plant and facility costing analysis will be performed externally. An interfacing external balance-of-plant and facility costing model, which will permit the performance of iterative facility trade studies, is under separate development. An ABC (Accelerator Based Conversion) example is used to highlight the present models and capabilities.

  19. Rule-Driven Object Tracking in Clutter and Partial Occlusion with Model-Based Snakes

    Directory of Open Access Journals (Sweden)

    Rapantzikos Konstantinos

    2004-01-01

    Full Text Available In the last few years it has been made clear to the research community that further improvements in classic approaches for solving low-level computer vision and image/video understanding tasks are difficult to obtain. New approaches started evolving, employing knowledge-based processing, though transforming a priori knowledge to low-level models and rules are far from being straightforward. In this paper, we examine one of the most popular active contour models, snakes, and propose a snake model, modifying terms and introducing a model-based one that eliminates basic problems through the usage of prior shape knowledge in the model. A probabilistic rule-driven utilization of the proposed model follows, being able to handle (or cope with objects of different shapes, contour complexities and motions; different environments, indoor and outdoor; cluttered sequences; and cases where background is complex (not smooth and when moving objects get partially occluded. The proposed method has been tested in a variety of sequences and the experimental results verify its efficiency.

  20. Driven Bose-Hubbard model with a parametrically modulated harmonic trap

    Science.gov (United States)

    Mann, N.; Bakhtiari, M. Reza; Massel, F.; Pelster, A.; Thorwart, M.

    2017-04-01

    We investigate a one-dimensional Bose-Hubbard model in a parametrically driven global harmonic trap. The delicate interplay of both the local interaction of the atoms in the lattice and the driving of the global trap allows us to control the dynamical stability of the trapped quantum many-body state. The impact of the atomic interaction on the dynamical stability of the driven quantum many-body state is revealed in the regime of weak interaction by analyzing a discretized Gross-Pitaevskii equation within a Gaussian variational ansatz, yielding a Mathieu equation for the condensate width. The parametric resonance condition is shown to be modified by the atom interaction strength. In particular, the effective eigenfrequency is reduced for growing interaction in the mean-field regime. For a stronger interaction, the impact of the global parametric drive is determined by the numerically exact time-evolving block decimation scheme. When the trapped bosons in the lattice are in a Mott insulating state, the absorption of energy from the driving field is suppressed due to the strongly reduced local compressibility of the quantum many-body state. In particular, we find that the width of the local Mott region shows a breathing dynamics. Finally, we observe that the global modulation also induces an effective time-independent inhomogeneous hopping strength for the atoms.

  1. IoT-Based User-Driven Service Modeling Environment for a Smart Space Management System

    Science.gov (United States)

    Choi, Hoan-Suk; Rhee, Woo-Seop

    2014-01-01

    The existing Internet environment has been extended to the Internet of Things (IoT) as an emerging new paradigm. The IoT connects various physical entities. These entities have communication capability and deploy the observed information to various service areas such as building management, energy-saving systems, surveillance services, and smart homes. These services are designed and developed by professional service providers. Moreover, users' needs have become more complicated and personalized with the spread of user-participation services such as social media and blogging. Therefore, some active users want to create their own services to satisfy their needs, but the existing IoT service-creation environment is difficult for the non-technical user because it requires a programming capability to create a service. To solve this problem, we propose the IoT-based user-driven service modeling environment to provide an easy way to create IoT services. Also, the proposed environment deploys the defined service to another user. Through the personalization and customization of the defined service, the value and dissemination of the service is increased. This environment also provides the ontology-based context-information processing that produces and describes the context information for the IoT-based user-driven service. PMID:25420153

  2. IoT-based user-driven service modeling environment for a smart space management system.

    Science.gov (United States)

    Choi, Hoan-Suk; Rhee, Woo-Seop

    2014-11-20

    The existing Internet environment has been extended to the Internet of Things (IoT) as an emerging new paradigm. The IoT connects various physical entities. These entities have communication capability and deploy the observed information to various service areas such as building management, energy-saving systems, surveillance services, and smart homes. These services are designed and developed by professional service providers. Moreover, users' needs have become more complicated and personalized with the spread of user-participation services such as social media and blogging. Therefore, some active users want to create their own services to satisfy their needs, but the existing IoT service-creation environment is difficult for the non-technical user because it requires a programming capability to create a service. To solve this problem, we propose the IoT-based user-driven service modeling environment to provide an easy way to create IoT services. Also, the proposed environment deploys the defined service to another user. Through the personalization and customization of the defined service, the value and dissemination of the service is increased. This environment also provides the ontology-based context-information processing that produces and describes the context information for the IoT-based user-driven service.

  3. Error Analysis of Satellite Precipitation-Driven Modeling of Flood Events in Complex Alpine Terrain

    Directory of Open Access Journals (Sweden)

    Yiwen Mei

    2016-03-01

    Full Text Available The error in satellite precipitation-driven complex terrain flood simulations is characterized in this study for eight different global satellite products and 128 flood events over the Eastern Italian Alps. The flood events are grouped according to two flood types: rain floods and flash floods. The satellite precipitation products and runoff simulations are evaluated based on systematic and random error metrics applied on the matched event pairs and basin-scale event properties (i.e., rainfall and runoff cumulative depth and time series shape. Overall, error characteristics exhibit dependency on the flood type. Generally, timing of the event precipitation mass center and dispersion of the time series derived from satellite precipitation exhibits good agreement with the reference; the cumulative depth is mostly underestimated. The study shows a dampening effect in both systematic and random error components of the satellite-driven hydrograph relative to the satellite-retrieved hyetograph. The systematic error in shape of the time series shows a significant dampening effect. The random error dampening effect is less pronounced for the flash flood events and the rain flood events with a high runoff coefficient. This event-based analysis of the satellite precipitation error propagation in flood modeling sheds light on the application of satellite precipitation in mountain flood hydrology.

  4. Comparison of current-driven and conductance-driven neocortical model neurons with Hodgkin-Huxley voltage-gated channels.

    Science.gov (United States)

    Tiesinga, P H; José, J V; Sejnowski, T J

    2000-12-01

    Intrinsic noise and random synaptic inputs generate a fluctuating current across neuron membranes. We determine the statistics of the output spike train of a biophysical model neuron as a function of the mean and variance of the fluctuating current, when the current is white noise, or when it derives from Poisson trains of excitatory and inhibitory postsynaptic conductances. In the first case, the firing rate increases with increasing variance of the current, whereas in the latter case it decreases. In contrast, the firing rate is independent of variance (for constant mean) in the commonly used random walk, and perfect integrate-and-fire models for spike generation. The model neuron can be in the current-dominated state, representative of neurons in the in vitro slice preparation, or in the fluctuation-dominated state, representative of in vivo neurons. We discuss the functional relevance of these states to cortical information processing.

  5. A model of energetic ion effects on pressure driven tearing modes in tokamaks

    Science.gov (United States)

    Halfmoon, M. R.; Brennan, D. P.

    2017-06-01

    The effects that energetic trapped ions have on linear resistive magnetohydrodynamic (MHD) instabilities are studied in a reduced model that captures the essential physics driving or damping the modes through variations in the magnetic shear. The drift-kinetic orbital interaction of a slowing down distribution of trapped energetic ions with a resistive MHD instability is integrated to a scalar contribution to the perturbed pressure, and entered into an asymptotic matching formalism for the resistive MHD dispersion relation. Toroidal magnetic field line curvature is included to model trapping in the particle distribution, in an otherwise cylindrical model. The focus is on a configuration that is driven unstable to the m/n = 2/1 mode by increasing pressure, where m is the poloidal mode number and n is the toroidal. The particles and pressure can affect the mode both in the core region where there can be low and reversed shear and outside the resonant surface in significant positive shear. The results show that the energetic ions damp and stabilize the mode when orbiting in significant positive shear, increasing the marginal stability boundary. However, the inner core region contribution with low and reversed shear can drive the mode unstable. This effect of shear on the energetic ion pressure contribution is found to be consistent with the literature. These results explain the observation that the 2/1 mode was found to be damped and stabilized by energetic ions in δf-MHD simulations of tokamak experiments with positive shear throughout, while the 2/1 mode was found to be driven unstable in simulations of experiments with weakly reversed shear in the core. This is also found to be consistent with related experimental observations of the stability of the 2/1 mode changing significantly with core shear.

  6. Photospheric Current Spikes And Their Possible Association With Flares - Results from an HMI Data Driven Model

    Science.gov (United States)

    Goodman, M. L.; Kwan, C.; Ayhan, B.; Eric, S. L.

    2016-12-01

    A data driven, near photospheric magnetohydrodynamic model predicts spikes in the horizontal current density, and associated resistive heating rate. The spikes appear as increases by orders of magnitude above background values in neutral line regions (NLRs) of active regions (ARs). The largest spikes typically occur a few hours to a few days prior to M or X flares. The spikes correspond to large vertical derivatives of the horizontal magnetic field. The model takes as input the photospheric magnetic field observed by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. This 2.5 D field is used to determine an analytic expression for a 3 D magnetic field, from which the current density, vector potential, and electric field are computed in every AR pixel for 14 ARs. The field is not assumed to be force-free. The spurious 6, 12, and 24 hour Doppler periods due to SDO orbital motion are filtered out of the time series of the HMI magnetic field for each pixel. The subset of spikes analyzed at the pixel level are found to occur on HMI and granulation scales of 1 arcsec and 12 minutes. Spikes are found in ARs with and without M or X flares, and outside as well as inside NLRs, but the largest spikes are localized in the NLRs of ARs with M or X flares. The energy to drive the heating associated with the largest current spikes comes from bulk flow kinetic energy, not the electromagnetic field, and the current density is highly non-force free. The results suggest that, in combination with the model, HMI is revealing strong, convection driven, non-force free heating events on granulation scales, and it is plausible these events are correlated with subsequent M or X flares. More and longer time series need to be analyzed to determine if such a correlation exists.

  7. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.

    Science.gov (United States)

    Wang, Shuo; Zhou, Mu; Liu, Zaiyi; Liu, Zhenyu; Gu, Dongsheng; Zang, Yali; Dong, Di; Gevaert, Olivier; Tian, Jie

    2017-08-01

    Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%. Copyright © 2017. Published by Elsevier B.V.

  8. Data-Driven Extraction of a Nested Model of Human Brain Function.

    Science.gov (United States)

    Bolt, Taylor; Nomi, Jason S; Yeo, B T Thomas; Uddin, Lucina Q

    2017-07-26

    Decades of cognitive neuroscience research have revealed two basic facts regarding task-driven brain activation patterns. First, distinct patterns of activation occur in response to different task demands. Second, a superordinate, dichotomous pattern of activation/deactivation, is common across a variety of task demands. We explore the possibility that a hierarchical model incorporates these two observed brain activation phenomena into a unifying framework. We apply a latent variable approach, exploratory bifactor analysis, to a large set of human (both sexes) brain activation maps ( n = 108) encompassing cognition, perception, action, and emotion behavioral domains, to determine the potential existence of a nested structure of factors that underlie a variety of commonly observed activation patterns. We find that a general factor, associated with a superordinate brain activation/deactivation pattern, explained the majority of the variance (52.37%) in brain activation patterns. The bifactor analysis also revealed several subfactors that explained an additional 31.02% of variance in brain activation patterns, associated with different manifestations of the superordinate brain activation/deactivation pattern, each emphasizing different contexts in which the task demands occurred. Importantly, this nested factor structure provided better overall fit to the data compared with a non-nested factor structure model. These results point to a domain-general psychological process, representing a "focused awareness" process or "attentional episode" that is variously manifested according to the sensory modality of the stimulus and degree of cognitive processing. This novel model provides the basis for constructing a biologically informed, data-driven taxonomy of psychological processes. SIGNIFICANCE STATEMENT A crucial step in identifying how the brain supports various psychological processes is a well-defined categorization or taxonomy of psychological processes and their

  9. Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm

    International Nuclear Information System (INIS)

    Wang, Xinli; Cai, Wenjian; Lu, Jiangang; Sun, Youxian; Zhao, Lei

    2015-01-01

    This study presents a model-based optimization strategy for an actual chiller driven dehumidifier of liquid desiccant dehumidification system operating with lithium chloride solution. By analyzing the characteristics of the components, energy predictive models for the components in the dehumidifier are developed. To minimize the energy usage while maintaining the outlet air conditions at the pre-specified set-points, an optimization problem is formulated with an objective function, the constraints of mechanical limitations and components interactions. Model-based optimization strategy using genetic algorithm is proposed to obtain the optimal set-points for desiccant solution temperature and flow rate, to minimize the energy usage in the dehumidifier. Experimental studies on an actual system are carried out to compare energy consumption between the proposed optimization and the conventional strategies. The results demonstrate that energy consumption using the proposed optimization strategy can be reduced by 12.2% in the dehumidifier operation. - Highlights: • Present a model-based optimization strategy for energy saving in LDDS. • Energy predictive models for components in dehumidifier are developed. • The Optimization strategy are applied and tested in an actual LDDS. • Optimization strategy can achieve energy savings by 12% during operation

  10. Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data.

    Science.gov (United States)

    Buonaccorsi, Giovanni A; O'Connor, James P B; Caunce, Angela; Roberts, Caleb; Cheung, Sue; Watson, Yvonne; Davies, Karen; Hope, Lynn; Jackson, Alan; Jayson, Gordon C; Parker, Geoffrey J M

    2007-11-01

    Dynamic contrast-enhanced MRI (DCE-MRI) time series data are subject to unavoidable physiological motion during acquisition (e.g., due to breathing) and this motion causes significant errors when fitting tracer kinetic models to the data, particularly with voxel-by-voxel fitting approaches. Motion correction is problematic, as contrast enhancement introduces new features into postcontrast images and conventional registration similarity measures cannot fully account for the increased image information content. A methodology is presented for tracer kinetic model-driven registration that addresses these problems by explicitly including a model of contrast enhancement in the registration process. The iterative registration procedure is focused on a tumor volume of interest (VOI), employing a three-dimensional (3D) translational transformation that follows only tumor motion. The implementation accurately removes motion corruption in a DCE-MRI software phantom and it is able to reduce model fitting errors and improve localization in 3D parameter maps in patient data sets that were selected for significant motion problems. Sufficient improvement was observed in the modeling results to salvage clinical trial DCE-MRI data sets that would otherwise have to be rejected due to motion corruption. Copyright 2007 Wiley-Liss, Inc.

  11. Neuromuscular interfacing: a novel approach to EMG-driven multiple DOF physiological models.

    Science.gov (United States)

    Pau, James W L; Xie, Shane S Q; Xu, W L

    2013-01-01

    This paper presents a novel approach that involves first identifying and verifying the available superficial muscles that can be recorded by surface electromyography (EMG) signals, and then developing a musculoskeletal model based on these findings, which have specifically independent DOFs for movement. Such independently controlled multiple DOF EMG-driven models have not been previously developed and a two DOF model for the masticatory system was achieved by implementing independent antagonist muscle combinations for vertical and lateral movements of the jaw. The model has six channels of EMG signals from the bilateral temporalis, masseter and digastric muscles to predict the motion of the mandible. This can be used in a neuromuscular interface to manipulate a jaw exoskeleton for rehabilitation. For a range of different complexities of jaw movements, the presented model is able to consistently identify movements with 0.28 - 0.46 average normalized RMSE. The results demonstrate the feasibility of the approach at determining complex multiple DOF movements and its applicability to any joint system.

  12. Data-driven model for the assessment ofMycobacterium tuberculosistransmission in evolving demographic structures.

    Science.gov (United States)

    Arregui, Sergio; Iglesias, María José; Samper, Sofía; Marinova, Dessislava; Martin, Carlos; Sanz, Joaquín; Moreno, Yamir

    2018-03-21

    In the case of tuberculosis (TB), the capabilities of epidemic models to produce quantitatively robust forecasts are limited by multiple hindrances. Among these, understanding the complex relationship between disease epidemiology and populations' age structure has been highlighted as one of the most relevant. TB dynamics depends on age in multiple ways, some of which are traditionally simplified in the literature. That is the case of the heterogeneities in contact intensity among different age strata that are common to all airborne diseases, but still typically neglected in the TB case. Furthermore, while demographic structures of many countries are rapidly aging, demographic dynamics are pervasively ignored when modeling TB spreading. In this work, we present a TB transmission model that incorporates country-specific demographic prospects and empirical contact data around a data-driven description of TB dynamics. Using our model, we find that the inclusion of demographic dynamics is followed by an increase in the burden levels predicted for the next decades in the areas of the world that are most hit by the disease today. Similarly, we show that considering realistic patterns of contacts among individuals in different age strata reshapes the transmission patterns reproduced by the models, a result with potential implications for the design of age-focused epidemiological interventions. Copyright © 2018 the Author(s). Published by PNAS.

  13. A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference

    KAUST Repository

    Tegner, Jesper

    2016-10-04

    Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.

  14. A GDP-driven model for the binary and weighted structure of the International Trade Network

    Science.gov (United States)

    Almog, Assaf; Squartini, Tiziano; Garlaschelli, Diego

    2015-01-01

    Recent events such as the global financial crisis have renewed the interest in the topic of economic networks. One of the main channels of shock propagation among countries is the International Trade Network (ITN). Two important models for the ITN structure, the classical gravity model of trade (more popular among economists) and the fitness model (more popular among networks scientists), are both limited to the characterization of only one representation of the ITN. The gravity model satisfactorily predicts the volume of trade between connected countries, but cannot reproduce the missing links (i.e. the topology). On the other hand, the fitness model can successfully replicate the topology of the ITN, but cannot predict the volumes. This paper tries to make an important step forward in the unification of those two frameworks, by proposing a new gross domestic product (GDP) driven model which can simultaneously reproduce the binary and the weighted properties of the ITN. Specifically, we adopt a maximum-entropy approach where both the degree and the strength of each node are preserved. We then identify strong nonlinear relationships between the GDP and the parameters of the model. This ultimately results in a weighted generalization of the fitness model of trade, where the GDP plays the role of a ‘macroeconomic fitness’ shaping the binary and the weighted structure of the ITN simultaneously. Our model mathematically explains an important asymmetry in the role of binary and weighted network properties, namely the fact that binary properties can be inferred without the knowledge of weighted ones, while the opposite is not true.

  15. Geophysical monitoring and reactive transport modeling of ureolytically-driven calcium carbonate precipitation

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Y.; Ajo-Franklin, J.B.; Spycher, N.; Hubbard, S.S.; Zhang, G.; Williams, K.H.; Taylor, J.; Fujita, Y.; Smith, R.

    2011-07-15

    Ureolytically-driven calcium carbonate precipitation is the basis for a promising in-situ remediation method for sequestration of divalent radionuclide and trace metal ions. It has also been proposed for use in geotechnical engineering for soil strengthening applications. Monitoring the occurrence, spatial distribution, and temporal evolution of calcium carbonate precipitation in the subsurface is critical for evaluating the performance of this technology and for developing the predictive models needed for engineering application. In this study, we conducted laboratory column experiments using natural sediment and groundwater to evaluate the utility of geophysical (complex resistivity and seismic) sensing methods, dynamic synchrotron x-ray computed tomography (micro-CT), and reactive transport modeling for tracking ureolytically-driven calcium carbonate precipitation processes under site relevant conditions. Reactive transport modeling with TOUGHREACT successfully simulated the changes of the major chemical components during urea hydrolysis. Even at the relatively low level of urea hydrolysis observed in the experiments, the simulations predicted an enhanced calcium carbonate precipitation rate that was 3-4 times greater than the baseline level. Reactive transport modeling results, geophysical monitoring data and micro-CT imaging correlated well with reaction processes validated by geochemical data. In particular, increases in ionic strength of the pore fluid during urea hydrolysis predicted by geochemical modeling were successfully captured by electrical conductivity measurements and confirmed by geochemical data. The low level of urea hydrolysis and calcium carbonate precipitation suggested by the model and geochemical data was corroborated by minor changes in seismic P-wave velocity measurements and micro-CT imaging; the latter provided direct evidence of sparsely distributed calcium carbonate precipitation. Ion exchange processes promoted through NH{sub 4}{sup

  16. Geophysical monitoring and reactive transport modeling of ureolytically-driven calcium carbonate precipitation

    Directory of Open Access Journals (Sweden)

    Taylor Joanna

    2011-09-01

    Full Text Available Abstract Ureolytically-driven calcium carbonate precipitation is the basis for a promising in-situ remediation method for sequestration of divalent radionuclide and trace metal ions. It has also been proposed for use in geotechnical engineering for soil strengthening applications. Monitoring the occurrence, spatial distribution, and temporal evolution of calcium carbonate precipitation in the subsurface is critical for evaluating the performance of this technology and for developing the predictive models needed for engineering application. In this study, we conducted laboratory column experiments using natural sediment and groundwater to evaluate the utility of geophysical (complex resistivity and seismic sensing methods, dynamic synchrotron x-ray computed tomography (micro-CT, and reactive transport modeling for tracking ureolytically-driven calcium carbonate precipitation processes under site relevant conditions. Reactive transport modeling with TOUGHREACT successfully simulated the changes of the major chemical components during urea hydrolysis. Even at the relatively low level of urea hydrolysis observed in the experiments, the simulations predicted an enhanced calcium carbonate precipitation rate that was 3-4 times greater than the baseline level. Reactive transport modeling results, geophysical monitoring data and micro-CT imaging correlated well with reaction processes validated by geochemical data. In particular, increases in ionic strength of the pore fluid during urea hydrolysis predicted by geochemical modeling were successfully captured by electrical conductivity measurements and confirmed by geochemical data. The low level of urea hydrolysis and calcium carbonate precipitation suggested by the model and geochemical data was corroborated by minor changes in seismic P-wave velocity measurements and micro-CT imaging; the latter provided direct evidence of sparsely distributed calcium carbonate precipitation. Ion exchange processes

  17. A 3D steady-state model of a tendon-driven continuum soft manipulator inspired by the octopus arm.

    Science.gov (United States)

    Renda, F; Cianchetti, M; Giorelli, M; Arienti, A; Laschi, C

    2012-06-01

    Control and modelling of continuum robots are challenging tasks for robotic researchers. Most works on modelling are limited to piecewise constant curvature. In many cases they neglect to model the actuators or avoid a continuum approach. In particular, in the latter case this leads to a complex model hardly implemented. In this work, a geometrically exact steady-state model of a tendon-driven manipulator inspired by the octopus arm is presented. It takes a continuum approach, fast enough to be implemented in the control law, and includes a model of the actuation system. The model was experimentally validated and the results are reported. In conclusion, the model presented can be used as a tool for mechanical design of continuum tendon-driven manipulators, for planning control strategies or as internal model in an embedded system.

  18. A 3D steady-state model of a tendon-driven continuum soft manipulator inspired by the octopus arm

    International Nuclear Information System (INIS)

    Renda, F; Cianchetti, M; Giorelli, M; Arienti, A; Laschi, C

    2012-01-01

    Control and modelling of continuum robots are challenging tasks for robotic researchers. Most works on modelling are limited to piecewise constant curvature. In many cases they neglect to model the actuators or avoid a continuum approach. In particular, in the latter case this leads to a complex model hardly implemented. In this work, a geometrically exact steady-state model of a tendon-driven manipulator inspired by the octopus arm is presented. It takes a continuum approach, fast enough to be implemented in the control law, and includes a model of the actuation system. The model was experimentally validated and the results are reported. In conclusion, the model presented can be used as a tool for mechanical design of continuum tendon-driven manipulators, for planning control strategies or as internal model in an embedded system. (paper)

  19. Model-driven design using IEC 61499 a synchronous approach for embedded and automation systems

    CERN Document Server

    Yoong, Li Hsien; Bhatti, Zeeshan E; Kuo, Matthew M Y

    2015-01-01

    This book describes a novel approach for the design of embedded systems and industrial automation systems, using a unified model-driven approach that is applicable in both domains.  The authors illustrate their methodology, using the IEC 61499 standard as the main vehicle for specification, verification, static timing analysis and automated code synthesis.  The well-known synchronous approach is used as the main vehicle for defining an unambiguous semantics that ensures determinism and deadlock freedom. The proposed approach also ensures very efficient implementations either on small-scale embedded devices or on industry-scale programmable automation controllers (PACs). It can be used for both centralized and distributed implementations. Significantly, the proposed approach can be used without the need for any run-time support. This approach, for the first time, blurs the gap between embedded systems and automation systems and can be applied in wide-ranging applications in automotive, robotics, and industri...

  20. Collision-model approach to steering of an open driven qubit

    Science.gov (United States)

    Beyer, Konstantin; Luoma, Kimmo; Strunz, Walter T.

    2018-03-01

    We investigate quantum steering of an open quantum system by measurements on its environment in the framework of collision models. As an example we consider a coherently driven qubit dissipatively coupled to a bath. We construct local nonadaptive and adaptive as well as nonlocal measurement scenarios specifying explicitly the measured observable on the environment. Our approach shows transparently how the conditional evolution of the open system depends on the type of the measurement scenario and the measured observables. These can then be optimized for steering. The nonlocal measurement scenario leads to maximal violation of the used steering inequality at zero temperature. Further, we investigate the robustness of the constructed scenarios against thermal noise. We find generally that steering becomes harder at higher temperatures. Surprisingly, the system can be steered even when bipartite entanglement between the system and individual subenvironments vanishes.

  1. A model-driven approach for representing clinical archetypes for Semantic Web environments.

    Science.gov (United States)

    Martínez-Costa, Catalina; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás; Maldonado, José Alberto

    2009-02-01

    The life-long clinical information of any person supported by electronic means configures his Electronic Health Record (EHR). This information is usually distributed among several independent and heterogeneous systems that may be syntactically or semantically incompatible. There are currently different standards for representing and exchanging EHR information among different systems. In advanced EHR approaches, clinical information is represented by means of archetypes. Most of these approaches use the Archetype Definition Language (ADL) to specify archetypes. However, ADL has some drawbacks when attempting to perform semantic activities in Semantic Web environments. In this work, Semantic Web technologies are used to specify clinical archetypes for advanced EHR architectures. The advantages of using the Ontology Web Language (OWL) instead of ADL are described and discussed in this work. Moreover, a solution combining Semantic Web and Model-driven Engineering technologies is proposed to transform ADL into OWL for the CEN EN13606 EHR architecture.

  2. Climate-driven ichthyoplankton drift model predicts growth of top predator young.

    Directory of Open Access Journals (Sweden)

    Mari S Myksvoll

    Full Text Available Climate variability influences seabird population dynamics in several ways including access to prey near colonies during the critical chick-rearing period. This study addresses breeding success in a Barents Sea colony of common guillemots Uria aalge where trophic conditions vary according to changes in the northward transport of warm Atlantic Water. A drift model was used to simulate interannual variations in transport of cod Gadus morhua larvae along the Norwegian coast towards their nursery grounds in the Barents Sea. The results showed that the arrival of cod larvae from southern spawning grounds had a major effect on the size of common guillemot chicks at fledging. Furthermore, the fraction of larvae from the south was positively correlated to the inflow of Atlantic Water into the Barents Sea thus clearly demonstrating the mechanisms by which climate-driven bottom-up processes influence interannual variations in reproductive success in a marine top predator.

  3. USACM Thematic Workshop On Uncertainty Quantification And Data-Driven Modeling.

    Energy Technology Data Exchange (ETDEWEB)

    Stewart, James R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    The USACM Thematic Workshop on Uncertainty Quantification and Data-Driven Modeling was held on March 23-24, 2017, in Austin, TX. The organizers of the technical program were James R. Stewart of Sandia National Laboratories and Krishna Garikipati of University of Michigan. The administrative organizer was Ruth Hengst, who serves as Program Coordinator for the USACM. The organization of this workshop was coordinated through the USACM Technical Thrust Area on Uncertainty Quantification and Probabilistic Analysis. The workshop website (http://uqpm2017.usacm.org) includes the presentation agenda as well as links to several of the presentation slides (permission to access the presentations was granted by each of those speakers, respectively). Herein, this final report contains the complete workshop program that includes the presentation agenda, the presentation abstracts, and the list of posters.

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

    International Nuclear Information System (INIS)

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

    2017-01-01

    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. (paper)

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

    Science.gov (United States)

    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.

  6. Aerosol-Driven Surface Solar Dimming Over Asia: Insights from a Model-Observation Intercomparison

    Science.gov (United States)

    Persad, G.; Ming, Y.; Ramaswamy, V.

    2012-12-01

    Sun photometer and satellite data have indicated a reduction in surface solar radiation (SSR) over India and China during the second half of the 20th century that is at least partly due to anthropogenic aerosols. Recent integrated observational studies of aerosol properties also suggest that this SSR reduction may have a strong contribution from atmospheric absorption by carbonaceous aerosols over Asia. The reduction in SSR and associated redistribution of energy between the surface and atmosphere may have significant implications for regional hydrological systems like the summertime monsoon. Previous generations of general circulation models (GCMs), however, have been largely unsuccessful at recreating aerosol-driven trends in SSR, hindering theoretical investigation of causes and effects of these trends in regional climate. We analyze the behavior of SSR over Asia in the Geophysical Fluid Dynamics Laboratory's AM3 Atmospheric General Circulation Model—the updated aerosol treatment of which contains internal mixing of aerosols and interactive dry and wet deposition—in the context of new satellite and ground-based observational estimates of aerosol-driven SSR reduction. We find that AM3 is more successful than the previous generation of GCMs at recreating the observed SSR trend over South and East Asia and also suggests that as much as half of the clear-sky trend may be attributable to increases in atmospheric absorption in both regions. We will discuss the SSR and atmospheric absorption trends over China and India, as depicted in both observations and AM3, as well the particular aerosol processes responsible for the model's recreation of the trends and their implications for regional climate.

  7. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Directory of Open Access Journals (Sweden)

    A. Elshorbagy

    2010-10-01

    Full Text Available In this second part of the two-part paper, the data driven modeling (DDM experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs, genetic programming (GP, evolutionary polynomial regression (EPR, Support vector machines (SVM, M5 model trees (M5, K-nearest neighbors (K-nn, and multiple linear regression (MLR techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it

  8. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Science.gov (United States)

    Elshorbagy, A.; Corzo, G.; Srinivasulu, S.; Solomatine, D. P.

    2010-10-01

    In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it should

  9. Towards a Tool-Supported Quality Model for Model-Driven Engineering

    OpenAIRE

    Mohagheghi, Parastoo

    2008-01-01

    This paper reviews definitions of model quality before introducing five properties of models that are important for building high-quality models. These are identified to be correctness, completeness, consistency, comprehensibility and confinement. We have earlier defined a quality model that separates intangible quality goals from tangible quality-carrying properties and practices that should be in place to support these properties.  A part of that work was to define a metamodel for deve...

  10. Data-driven models of dominantly-inherited Alzheimer's disease progression.

    Science.gov (United States)

    Oxtoby, Neil P; Young, Alexandra L; Cash, David M; Benzinger, Tammie L S; Fagan, Anne M; Morris, John C; Bateman, Randall J; Fox, Nick C; Schott, Jonathan M; Alexander, Daniel C

    2018-03-22

    Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1

  11. How realistic are air quality hindcasts driven by forcings from climate model simulations?

    Science.gov (United States)

    Lacressonnière, G.; Peuch, V.-H.; Arteta, J.; Josse, B.; Joly, M.; Marécal, V.; Saint Martin, D.; Déqué, M.; Watson, L.

    2012-12-01

    Predicting how European air quality could evolve over the next decades in the context of changing climate requires the use of climate models to produce results that can be averaged in a climatologically and statistically sound manner. This is a very different approach from the one that is generally used for air quality hindcasts for the present period; analysed meteorological fields are used to represent specifically each date and hour. Differences arise both from the fact that a climate model run results in a pure model output, with no influence from observations (which are useful to correct for a range of errors), and that in a "climate" set-up, simulations on a given day, month or even season cannot be related to any specific period of time (but can just be interpreted in a climatological sense). Hence, although an air quality model can be thoroughly validated in a "realistic" set-up using analysed meteorological fields, the question remains of how far its outputs can be interpreted in a "climate" set-up. For this purpose, we focus on Europe and on the current decade using three 5-yr simulations performed with the multiscale chemistry-transport model MOCAGE and use meteorological forcings either from operational meteorological analyses or from climate simulations. We investigate how statistical skill indicators compare in the different simulations, discriminating also the effects of meteorology on atmospheric fields (winds, temperature, humidity, pressure, etc.) and on the dependent emissions and deposition processes (volatile organic compound emissions, deposition velocities, etc.). Our results show in particular how differing boundary layer heights and deposition velocities affect horizontal and vertical distributions of species. When the model is driven by operational analyses, the simulation accurately reproduces the observed values of O3, NOx, SO2 and, with some bias that can be explained by the set-up, PM10. We study how the simulations driven by climate

  12. Muscle synergy control model-tuned EMG driven torque estimation system with a musculo-skeletal model.

    Science.gov (United States)

    Min, Kyuengbo; Shin, Duk; Lee, Jongho; Kakei, Shinji

    2013-01-01

    Muscle activity is the final signal for motion control from the brain. Based on this biological characteristic, Electromyogram (EMG) signals have been applied to various systems that interface human with external environments such as external devices. In order to use EMG signals as input control signal for this kind of system, the current EMG driven torque estimation models generally employ the mathematical model that estimates the nonlinear transformation function between the input signal and the output torque. However, these models need to estimate too many parameters and this process cause its estimation versatility in various conditions to be poor. Moreover, as these models are designed to estimate the joint torque, the input EMG signals are tuned out of consideration for the physiological synergetic contributions of multiple muscles for motion control. To overcome these problems of the current models, we proposed a new tuning model based on the synergy control mechanism between multiple muscles in the cortico-spinal tract. With this synergetic tuning model, the estimated contribution of multiple muscles for the motion control is applied to tune the EMG signals. Thus, this cortico-spinal control mechanism-based process improves the precision of torque estimation. This system is basically a forward dynamics model that transforms EMG signals into the joint torque. It should be emphasized that this forward dynamics model uses a musculo-skeletal model as a constraint. The musculo-skeletal model is designed with precise musculo-skeletal data, such as origins and insertions of individual muscles or maximum muscle force. Compared with the mathematical model, the proposed model can be a versatile model for the torque estimation in the various conditions and estimates the torque with improved accuracy. In this paper, we also show some preliminary experimental results for the discussion about the proposed model.

  13. Business Value of Information Technology Service Quality Based on Probabilistic Business-Driven Model

    Directory of Open Access Journals (Sweden)

    Jaka Sembiring

    2015-08-01

    Full Text Available The business value of information technology (IT services is often difficult to assess, especially from the point of view of a non-IT manager. This condition could severely impact organizational IT strategic decisions. Various approaches have been proposed to quantify the business value, but some are trapped in technical complexity while others misguide managers into directly and subjectively judging some technical entities outside their domain of expertise. This paper describes a method on how to properly capture both perspectives based on a  probabilistic business-driven model. The proposed model presents a procedure to calculate the business value of IT services. The model also covers IT security services and their business value as an important aspect of IT services that is not covered in previously published researches. The impact of changes in the quality of IT services on business value will also be discussed. A simulation and a case illustration are provided to show the possible application of the proposed model for a simple business process in an enterprise.

  14. A stress driven growth model for soft tissue considering biological availability

    International Nuclear Information System (INIS)

    Oller, S; Bellomo, F J; Nallim, L G; Armero, F

    2010-01-01

    Some of the key factors that regulate growth and remodeling of tissues are fundamentally mechanical. However, it is important to take into account the role of bioavailability together with the stresses and strains in the processes of normal or pathological growth. In this sense, the model presented in this work is oriented to describe the growth of soft biological tissue under 'stress driven growth' and depending on the biological availability of the organism. The general theoretical framework is given by a kinematic formulation in large strain combined with the thermodynamic basis of open systems. The formulation uses a multiplicative decomposition of deformation gradient, splitting it in a growth part and visco-elastic part. The strains due to growth are incompatible and are controlled by an unbalanced stresses related to a homeostatic state. Growth implies a volume change with an increase of mass maintaining constant the density. One of the most interesting features of the proposed model is the generation of new tissue taking into account the contribution of mass to the system controlled through biological availability. Because soft biological tissues in general have a hierarchical structure with several components (usually a soft matrix reinforced with collagen fibers), the developed growth model is suitable for the characterization of the growth of each component. This allows considering a different behavior for each of them in the context of a generalized theory of mixtures. Finally, we illustrate the response of the model in case of growth and atrophy with an application example.

  15. A Hamiltonian driven quantum-like model for overdistribution in episodic memory recollection.

    Science.gov (United States)

    Broekaert, Jan B.; Busemeyer, Jerome R.

    2017-06-01

    While people famously forget genuine memories over time, they also tend to mistakenly over-recall equivalent memories concerning a given event. The memory phenomenon is known by the name of episodic overdistribution and occurs both in memories of disjunctions and partitions of mutually exclusive events and has been tested, modeled and documented in the literature. The total classical probability of recalling exclusive sub-events most often exceeds the probability of recalling the composed event, i.e. a subadditive total. We present a Hamiltonian driven propagation for the Quantum Episodic Memory model developed by Brainerd (et al., 2015) for the episodic memory overdistribution in the experimental immediate item false memory paradigm (Brainerd and Reyna, 2008, 2010, 2015). Following the Hamiltonian method of Busemeyer and Bruza (2012) our model adds time-evolution of the perceived memory state through the stages of the experimental process based on psychologically interpretable parameters - γ_c for recollection capability of cues, κ_p for bias or description-dependence by probes and β for the average gist component in the memory state at start. With seven parameters the Hamiltonian model shows good accuracy of predictions both in the EOD-disjunction and in the EOD-subadditivity paradigm. We noticed either an outspoken preponderance of the gist over verbatim trace, or the opposite, in the initial memory state when β is real. Only for complex β a mix of both traces is present in the initial state for the EOD-subadditivity paradigm.

  16. Modeling and simulations of radiative blast wave driven Rayleigh-Taylor instability experiments

    Science.gov (United States)

    Shimony, Assaf; Huntington, Channing M.; Trantham, Matthew; Malamud, Guy; Elbaz, Yonatan; Kuranz, Carolyn C.; Drake, R. Paul; Shvarts, Dov

    2017-10-01

    Recent experiments at the National Ignition Facility measured the growth of Rayleigh-Taylor RT instabilities driven by radiative blast waves, relevant to astrophysics and other HEDP systems. We constructed a new Buoyancy-Drag (BD) model, which accounts for the ablation effect on both bubble and spike. This ablation effect is accounted for by using the potential flow model ]Oron et al PoP 1998], adding another term to the classical BD formalism: βDuA / u , where β the Takabe constant, D the drag term, uA the ablation velocity and uthe instability growth velocity. The model results are compared with the results of experiments and 2D simulations using the CRASH code, with nominal radiation or reduced foam opacity (by a factor of 1000). The ablation constant of the model, βb / s, for the bubble and for the spike fronts, are calibrated using the results of the radiative shock experiments. This work is funded by the Lawrence Livermore National Laboratory under subcontract B614207, and was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344.

  17. Modeling of strongly heat-driven flow in partially saturated fractured porous media

    International Nuclear Information System (INIS)

    Pruess, K.; Tsang, Y.W.; Wang, J.S.Y.

    1985-01-01

    The authors have performed modeling studies on the simultaneous transport of heat, liquid water, vapor, and air in partially saturated fractured porous media, with particular emphasis on strongly heat-driven flow. The presence of fractures makes the transport problem very complex, both in terms of flow geometry and physics. The numerical simulator used for their flow calculations takes into account most of the physical effects which are important in multi-phase fluid and heat flow. It has provisions to handle the extreme non-linearities which arise in phase transitions, component disappearances, and capillary discontinuities at fracture faces. They model a region around an infinite linear string of nuclear waste canisters, taking into account both the discrete fractures and the porous matrix. From an analysis of the results obtained with explicit fractures, they develop equivalent continuum models which can reproduce the temperature, saturation, and pressure variation, and gas and liquid flow rates of the discrete fracture-porous matrix calculations. The equivalent continuum approach makes use of a generalized relative permeability concept to take into account the fracture effects. This results in a substantial simplification of the flow problem which makes larger scale modeling of complicated unsaturated fractured porous systems feasible. Potential applications for regional scale simulations and limitations of the continuum approach are discussed. 27 references, 13 figures, 2 tables

  18. Modeling of strongly heat-driven flow in partially saturated fractured porous media

    International Nuclear Information System (INIS)

    Pruess, K.; Tsang, Y.W.; Wang, J.S.Y.

    1984-10-01

    We have performed modeling studies on the simultaneous transport of heat, liquid water, vapor, and air in partially saturated fractured porous media, with particular emphasis on strongly heat-driven flow. The presence of fractures makes the transport problem very complex, both in terms of flow geometry and physics. The numerical simulator used for our flow calculations takes into account most of the physical effects which are important in multi-phase fluid and heat flow. It has provisions to handle the extreme non-linearities which arise in phase transitions, component disappearances, and capillary discontinuities at fracture faces. We model a region around an infinite linear string of nuclear waste canisters, taking into account both the discrete fractures and the porous matrix. From an analysis of the results obtained with explicit fractures, we develop equivalent continuum models which can reproduce the temperature, saturation, and pressure variation, and gas and liquid flow rates of the discrete fracture-porous matrix calculations. The equivalent continuum approach makes use of a generalized relative permeability concept to take into account for fracture effects. This results in a substantial simplification of the flow problem which makes larger scale modeling of complicated unsaturated fractured porous systems feasible. Potential applications for regional scale simulations and limitations of the continuum approach are discussed. 27 references, 13 figures, 2 tables

  19. A Hamiltonian Driven Quantum-Like Model for Overdistribution in Episodic Memory Recollection

    Directory of Open Access Journals (Sweden)

    Jan B. Broekaert

    2017-06-01

    Full Text Available While people famously forget genuine memories over time, they also tend to mistakenly over-recall equivalent memories concerning a given event. The memory phenomenon is known by the name of episodic overdistribution and occurs both in memories of disjunctions and partitions of mutually exclusive events and has been tested, modeled and documented in the literature. The total classical probability of recalling exclusive sub-events most often exceeds the probability of recalling the composed event, i.e., a subadditive total. We present a Hamiltonian driven propagation for the Quantum Episodic Memory model developed by Brainerd et al. [1] for the episodic memory overdistribution in the experimental immediate item false memory paradigm [1–3]. Following the Hamiltonian method of Busemeyer and Bruza [4] our model adds time-evolution of the perceived memory state through the stages of the experimental process based on psychologically interpretable parameters—γc for recollection capability of cues, κp for bias or description-dependence by probes and β for the average gist component in the memory state at start. With seven parameters the Hamiltonian model shows good accuracy of predictions both in the EOD-disjunction and in the EOD-subadditivity paradigm. We noticed either an outspoken preponderance of the gist over verbatim trace, or the opposite, in the initial memory state when β is real. Only for complex β a mix of both traces is present in the initial state for the EOD-subadditivity paradigm.

  20. Engineering Smart Grids: Applying Model-Driven Development from Use Case Design to Deployment

    Directory of Open Access Journals (Sweden)

    Filip Pröstl Andrén

    2017-03-01

    Full Text Available The rollout of smart grid solutions has already started and new methods are deployed to the power systems of today. However, complexity is still increasing as focus is moving from a single system, to a system of systems perspective. The results are increasing engineering efforts and escalating costs. For this reason, new and automated engineering methods are necessary. This paper addresses these needs with a rapid engineering methodology that covers the overall engineering process for smart grid applications—from use case design to deployment. Based on a model-driven development approach, the methodology consists of three main parts: use case modeling, code generation, and deployment. A domain-specific language is introduced supporting the use case design according to the Smart Grid Architecture Model. It is combined with the IEC 61499 distributed control model to improve the function layer design. After a completed use case design, executable code and communication configurations (e.g., IEC 61850 are generated and deployed onto compatible field devices. This paper covers the proposed rapid engineering methodology and a corresponding prototypical implementation which is validated in a laboratory experiment. Compared to other methods the proposed methodology decreases the number of engineering steps and reduces the use case design and implementation complexity.

  1. The social structure of ''experimental'' strings at Fermilab; a physics and detector driven model

    International Nuclear Information System (INIS)

    Bodnarczuk, M.

    1990-01-01

    Physicists in HEP have been forced to organize large scientific projects without a well defined organizational or sociological model to guide them. In the absence of such models, what structures do experimentalists use to develop social structures in HEP? In this paper, I claim that physicists organize around what they know best, the physics problems they study and the detectors and devices they study them with. After describing the advent of ''management'' in HEP, I use a case study of 4 Fermilab experiments as the base upon which to propose a physics and detector driven model of social structure for experiments. In addition, I show how this model can be extended to describe ''strings'' of experiments, where continuities of physics interests, spectrometer design, and a core group of physicists become a definable sociological unit that can exist for over 15 years. A dominate theme that emerges from my analysis is the conscious attempt on the part of experimenters to remove the uncertainties that are part of the practice of HEP

  2. A sensitivity driven meta-model optimisation tool for hydrological models

    Science.gov (United States)

    Oppel, Henning; Schumann, Andreas

    2017-04-01

    The calibration of rainfall-runoff-models containing a high number of parameters can be done readily by the use of different calibration methods and algorithms. Monte-Carlo Methods, gradient based search algorithms and others are well-known and established in hydrological sciences. Thus, the calibration of a model for a desired application is not a challenging task, but retaining regional comparability and process integrity is, due to the equifinality-problem, a prevailing topic. This set of issues is mainly a result of the overdeterminaton given the high number of parameters in rainfall-runoff-models, where different parameters are affecting the same facet of model performance (i.e. runoff volume, variance and timing). In this study a calibration strategy is presented which considers model sensitivity as well as parameter interaction and different criteria of model performance. At first a range of valid values for each model parameter was defined and the individual effect on model performance within the defined parameter range was evaluated. By use of the gained knowledge a meta-model, lumping different parameters affecting the same facet of model performance, was established. Hereafter, the parsimonious meta-model, where each parameter is assigned to a nearly disjoint facet of model performance is optimized. By retransformation of the lumped parameters to the original model, a parametrisation for the original model is obtained. An application of this routine to a set of watersheds in the eastern part of Germany displays the benefits of the routine. Results of the meta-parametrised model are compared to parametrisations obtained from common calibration routines in a validation study and process orientated numerical experiment.

  3. Development of an integrated modelling framework: comparing client-server and demand-driven control flow for model execution

    Science.gov (United States)

    Schmitz, Oliver; Karssenberg, Derek; de Jong, Kor; de Kok, Jean-Luc; de Jong, Steven M.

    2014-05-01

    The construction of hydrological models at the catchment or global scale depends on the integration of component models representing various environmental processes, often operating at different spatial and temporal discretisations. A flexible construction of spatio-temporal model components, a means to specify aggregation or disaggregation to bridge discretisation discrepancies, ease of coupling these into complex integrated models, and support for stochastic modelling and the assessment of model outputs are the desired functionalities for the development of integrated models. These functionalities are preferably combined into one modelling framework such that domain specialists can perform exploratory model development without the need to change their working environment. We implemented an integrated modelling framework in the Python programming language, providing support for 1) model construction and 2) model execution. The framework enables modellers to represent spatio-temporal processes or to specify spatio-temporal (dis)aggregation with map algebra operations provided by the PCRaster library. Model algebra operations can be used by the modeller to specify the exchange of data and therefore the coupling of components. The framework determines the control flow for the ordered execution based on the time steps and couplings of the model components given by the modeller. We implemented two different control flow mechanisms. First, a client-server approach is used with a central entity controlling the execution of the component models and steering the data exchange. Second, a demand-driven approach is used that triggers the execution of a component model when data is requested by a coupled component model. We show that both control flow mechanisms allow for the execution of stochastic, multi-scale integrated models. We examine the implications of each control flow mechanism on the terminology used by the modeller to specify integrated models, and illustrate the

  4. Large-Scale Modelling of the Environmentally-Driven Population Dynamics of Temperate Aedes albopictus (Skuse.

    Directory of Open Access Journals (Sweden)

    Kamil Erguler

    Full Text Available The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations.

  5. Data-Driven Modeling of Complex Systems by means of a Dynamical ANN

    Science.gov (United States)

    Seleznev, A.; Mukhin, D.; Gavrilov, A.; Loskutov, E.; Feigin, A.

    2017-12-01

    The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: (i) determining the phase subspace of the system, or embedding, from available time series and (ii) constructing an evolution operator acting in this reduced subspace. In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network (ANN) with special topology. The proposed ANN-based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold. Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on. The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed. The study was supported by the Russian Science Foundation (grant #16-12-10198).

  6. The Application of Cyber Physical System for Thermal Power Plants: Data-Driven Modeling

    Directory of Open Access Journals (Sweden)

    Yongping Yang

    2018-03-01

    Full Text Available Optimal operation of energy systems plays an important role to enhance their lifetime security and efficiency. The determination of optimal operating strategies requires intelligent utilization of massive data accumulated during operation or prediction. The investigation of these data solely without combining physical models may run the risk that the established relationships between inputs and outputs, the models which reproduce the behavior of the considered system/component in a wide range of boundary conditions, are invalid for certain boundary conditions, which never occur in the database employed. Therefore, combining big data with physical models via cyber physical systems (CPS is of great importance to derive highly-reliable and -accurate models and becomes more and more popular in practical applications. In this paper, we focus on the description of a systematic method to apply CPS to the performance analysis and decision making of thermal power plants. We proposed a general procedure of CPS with both offline and online phases for its application to thermal power plants and discussed the corresponding methods employed to support each sub-procedure. As an example, a data-driven model of turbine island of an existing air-cooling based thermal power plant is established with the proposed procedure and demonstrates its practicality, validity and flexibility. To establish such model, the historical operating data are employed in the cyber layer for modeling and linking each physical component. The decision-making procedure of optimal frequency of air-cooling condenser is also illustrated to show its applicability of online use. It is concluded that the cyber physical system with the data mining technique is effective and promising to facilitate the real-time analysis and control of thermal power plants.

  7. Probing the dynamics of identified neurons with a data-driven modeling approach.

    Directory of Open Access Journals (Sweden)

    Thomas Nowotny

    2008-07-01

    Full Text Available In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach.

  8. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    Science.gov (United States)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-06-01

    Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space Rn. An isometric mapping F from M to a low-dimensional, compact, connected set A⊂Rd(d≪n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructing low

  9. Diffusion tensor magnetic resonance imaging driven growth modeling for radiotherapy target definition in glioblastoma.

    Science.gov (United States)

    Jensen, Morten B; Guldberg, Trine L; Harbøll, Anja; Lukacova, Slávka; Kallehauge, Jesper F

    2017-11-01

    The clinical target volume (CTV) in radiotherapy is routinely based on gadolinium contrast enhanced T1 weighted (T1w + Gd) and T2 weighted fluid attenuated inversion recovery (T2w FLAIR) magnetic resonance imaging (MRI) sequences which have been shown to over- or underestimate the microscopic tumor cell spread. Gliomas favor spread along the white matter fiber tracts. Tumor growth models incorporating the MRI diffusion tensors (DTI) allow to account more consistently for the glioma growth. The aim of the study was to investigate the potential of a DTI driven growth model to improve target definition in glioblastoma (GBM). Eleven GBM patients were scanned using T1w, T2w FLAIR, T1w + Gd and DTI. The brain was segmented into white matter, gray matter and cerebrospinal fluid. The Fisher-Kolmogorov growth model was used assuming uniform proliferation and a difference in white and gray matter diffusion of a ratio of 10. The tensor directionality was tested using an anisotropy weighting parameter set to zero (γ0) and twenty (γ20). The volumetric comparison was performed using Hausdorff distance, Dice similarity coefficient (DSC) and surface area. The median of the standard CTV (CTVstandard) was 180 cm 3 . The median surface area of CTVstandard was 211 cm 2 . The median surface area of respective CTV γ0 and CTV γ20 significantly increased to 338 and 376 cm 2 , respectively. The Hausdorff distance was greater than zero and significantly increased for both CTV γ0 and CTV γ20 with respective median of 18.7 and 25.2 mm. The DSC for both CTV γ0 and CTV γ20 were significantly below one with respective median of 0.74 and 0.72, which means that 74 and 72% of CTVstandard were included in CTV γ0 and CTV γ20, respectively. DTI driven growth models result in CTVs with a significantly increased surface area, a significantly increased Hausdorff distance and decreased overlap between the standard and model derived volume.

  10. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    International Nuclear Information System (INIS)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-01-01

    Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R n . An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R d (d<< n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology

  11. Modeling and Partitioning of Regional Evapotranspiration Using a Satellite-Driven Water-Carbon Coupling Model

    Directory of Open Access Journals (Sweden)

    Zhongmin Hu

    2017-01-01

    Full Text Available The modeling and partitioning of regional evapotranspiration (ET are key issues in global hydrological and ecological research. We incorporated a stomatal conductance model and a light-use efficiency-based gross primary productivity (GPP model into the Shuttleworth–Wallace model to develop a simplified carbon-water coupling model, SWH, for estimating ET using meteorological and remote sensing data. To enable regional application of the SWH model, we optimized key parameters with measurements from global eddy covariance (EC tower sites. In addition, we estimated soil water content with the principle of the bucket system. The model prediction of ET agreed well with the estimates obtained with the EC measurements, with an average R2 of 0.77 and a root mean square error of 0.72 mm·day−1. The model performance was generally better for woody ecosystems than herbaceous ecosystems. Finally, the spatial patterns of ET and relevant model outputs (i.e., GPP, water-use efficiency and the ratio of soil water evaporation to ET in China with the model simulations were assessed.

  12. Implementation, Comparison and Application of an Average Simulation Model of a Wind Turbine Driven Doubly Fed Induction Generator

    Directory of Open Access Journals (Sweden)

    Lidula N. Widanagama Arachchige

    2017-10-01

    Full Text Available Wind turbine driven doubly-fed induction generators (DFIGs are widely used in the wind power industry. With the increasing penetration of wind farms, analysis of their effect on power systems has become a critical requirement. This paper presents the modeling of wind turbine driven DFIGs using the conventional vector controls in a detailed model of a DFIG that represents power electronics (PE converters with device level models and proposes an average model eliminating the PE converters. The PSCAD/EMTDC™ (4.6 electromagnetic transient simulation software is used to develop the detailed and the proposing average model of a DFIG. The comparison of the two models reveals that the designed average DFIG model is adequate for simulating and analyzing most of the transient conditions.

  13. Modeling X-ray Absorbers in AGNs with MHD-Driven Accretion-Disk Winds

    Science.gov (United States)

    Fukumura, Keigo; Kazanas, D.; Shrader, C. R.; Tombesi, F.; Contopoulos, J.; Behar, E.

    2013-04-01

    We have proposed a systematic view of the observed X-ray absorbers, namely warm absorbers (WAs) in soft X-ray and highly-ionized ultra-fast outflows (UFOs), in the context of magnetically-driven accretion-disk wind models. While potentially complicated by variability and thermal instability in these energetic outflows, in this simplistic model we have calculated 2D kinematic field as well as density and ionization structure of the wind with density profile of 1/r corresponding to a constant column distribution per decade of ionization parameter. In particular we show semi-analytically that the inner layer of the disk-wind manifests itself as the strongly-ionized fast outflows while the outer layer is identified as the moderately-ionized absorbers. The computed characteristics of these two apparently distinct absorbers are consistent with X-ray data (i.e. a factor of ~100 difference in column and ionization parameters as well as low wind velocity vs. near-relativistic flow). With the predicted contour curves for these wind parameters one can constrain allowed regions for the presence of WAs and UFOs.The model further implies that the UFO's gas pressure is comparable to that of the observed radio jet in 3C111 suggesting that the magnetized disk-wind with density profile of 1/r is a viable agent to help sustain such a self-collimated jet at small radii.

  14. A Model-Driven Co-Design Framework for Fusing Control and Scheduling Viewpoints

    Science.gov (United States)

    Navet, Nicolas; Havet, Lionel

    2018-01-01

    Model-Driven Engineering (MDE) is widely applied in the industry to develop new software functions and integrate them into the existing run-time environment of a Cyber-Physical System (CPS). The design of a software component involves designers from various viewpoints such as control theory, software engineering, safety, etc. In practice, while a designer from one discipline focuses on the core aspects of his field (for instance, a control engineer concentrates on designing a stable controller), he neglects or considers less importantly the other engineering aspects (for instance, real-time software engineering or energy efficiency). This may cause some of the functional and non-functional requirements not to be met satisfactorily. In this work, we present a co-design framework based on timing tolerance contract to address such design gaps between control and real-time software engineering. The framework consists of three steps: controller design, verified by jitter margin analysis along with co-simulation, software design verified by a novel schedulability analysis, and the run-time verification by monitoring the execution of the models on target. This framework builds on CPAL (Cyber-Physical Action Language), an MDE design environment based on model-interpretation, which enforces a timing-realistic behavior in simulation through timing and scheduling annotations. The application of our framework is exemplified in the design of an automotive cruise control system. PMID:29461489

  15. A Model-driven Role-based Access Control for SQL Databases

    Directory of Open Access Journals (Sweden)

    Raimundas Matulevičius

    2015-07-01

    Full Text Available Nowadays security has become an important aspect in information systems engineering. A mainstream method for information system security is Role-based Access Control (RBAC, which restricts system access to authorised users. While the benefits of RBAC are widely acknowledged, the implementation and administration of RBAC policies remains a human intensive activity, typically postponed until the implementation and maintenance phases of system development. This deferred security engineering approach makes it difficult for security requirements to be accurately captured and for the system’s implementation to be kept aligned with these requirements as the system evolves. In this paper we propose a model-driven approach to manage SQL database access under the RBAC paradigm. The starting point of the approach is an RBAC model captured in SecureUML. This model is automatically translated to Oracle Database views and instead-of triggers code, which implements the security constraints. The approach has been fully instrumented as a prototype and its effectiveness has been validated by means of a case study.

  16. PRAGMATICS DRIVEN LAND COVER SERVICE COMPOSITION UTILIZING BEHAVIOR-INTENTION MODEL

    Directory of Open Access Journals (Sweden)

    H. Wu

    2016-06-01

    Full Text Available Web service composition is one of the key issues to develop a global land cover (GLC information service portal. Aiming at the defect that traditional syntax and semantic service compositionare difficult to take pragmatic information into account, the paper firstly analyses three tiers of web service language and their succession relations, discusses the conceptual model of pragmatic web service, and proposes the idea of pragmatics-oriented adaptive composition method based on the analysis of some examples. On this basis it puts forward the pragmatic web service model based on Behavior-Intention through presetting and expression of service usability, users' intention, and other pragmatic information, develops the on-demand assembly method based on the agent theory and matching and reconstruction method on heterogeneous message, solves the key technological issue of algorithm applicability and heterogeneous message transformation in the process of covering web service composition on the ground, applies these methods into service combination, puts forward the pragmatic driven service composition method based on behavior-intention model, and effectively settles the issue of coordination and interaction of composite service invocation.

  17. Reinforcement-driven dimensionality reduction--a model for information processing in the basal ganglia.

    Science.gov (United States)

    Bar-Gad, I; Havazelet-Heimer, G; Goldberg, J A; Ruppin, E; Bergman, H

    2000-01-01

    Although anatomical studies of the basal ganglia show the existence of extensive convergence and lateral inhibitory connections, physiological studies failed to show correlated neural activity or lateral interaction in these nuclei. These seemingly contradictory results could be explained with a model in which the basal ganglia reduce the dimensionality of cortical information using optimal extraction methods. Simulations of this model predict a transient change in the efficacy of the feed-forward and lateral synapses following changes in reinforcement signal, causing an increase in correlated firing rates. This process ultimately restores the steady-state situation with diminished efficacy of lateral inhibition and no correlation of firing. Our experimental results confirm the model's predictions: rate correlations show a drastic decrease between the input stage (cortex) and output stage (pallidum). Moreover, preliminary analysis revealed that pallidal correlations show a transient increase following discrepancies between the animal's predictions and reality. We therefore propose that by using a reinforcement-driven dimensionality reduction process the basal ganglia achieve efficient extraction of cortical salient information that may then be used by the frontal cortex for execution and planning of forthcoming actions.

  18. A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatment

    Science.gov (United States)

    Nör, Jacques Eduardo

    2018-01-01

    Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. Here we present a modeling framework that operates at both the cellular and molecular levels, for investigating IL-6 mediated, cancer stem cell driven tumor growth and targeted treatment with anti-IL6 antibodies. Our immediate goal is to quantify the influence of IL-6 on cancer stem cell self-renewal and survival, and to characterize the subsequent impact on tumor growth dynamics. By including the molecular details of IL-6 binding, we are able to quantify the temporal changes in fractional occupancies of bound receptors and their influence on tumor volume. There is a strong correlation between the model output and experimental data for primary tumor xenografts. We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth. PMID:29351275

  19. Global warming projection in the 21st century based on an observational data-driven model

    Science.gov (United States)

    Zeng, Xubin; Geil, Kerrie

    2016-10-01

    Global warming has been projected primarily by Earth system models (ESMs). Complementary to this approach, here we provide the decadal and long-term global warming projections based on an observational data-driven model. This model combines natural multidecadal variability with anthropogenic warming that depends on the history of annual emissions. It shows good skill in decadal hindcasts with the recent warming slowdown well captured. While our ensemble mean temperature projections at the end of 21st century are consistent with those from ESMs, our decadal warming projection of 0.35 (0.30-0.43) K from 1986-2005 to 2016-2035 is within their projection range and only two-thirds of the ensemble mean from ESMs. Our predicted warming rate in the next few years is slower than in the 1980s and 1990s, followed by a greater warming rate. Our projection uncertainty range is just one-third of that from ESMs, and its implication is also discussed.

  20. Dynamic modeling and motion simulation for a winged hybrid-driven underwater glider

    Science.gov (United States)

    Wang, Shu-Xin; Sun, Xiu-Jun; Wang, Yan-Hui; Wu, Jian-Guo; Wang, Xiao-Ming

    2011-03-01

    PETREL, a winged hybrid-driven underwater glider is a novel and practical marine survey platform which combines the features of legacy underwater glider and conventional AUV (autonomous underwater vehicle). It can be treated as a multi-rigid-body system with a floating base and a particular hydrodynamic profile. In this paper, theorems on linear and angular momentum are used to establish the dynamic equations of motion of each rigid body and the effect of translational and rotational motion of internal masses on the attitude control are taken into consideration. In addition, due to the unique external shape with fixed wings and deflectable rudders and the dual-drive operation in thrust and glide modes, the approaches of building dynamic model of conventional AUV and hydrodynamic model of submarine are introduced, and the tailored dynamic equations of the hybrid glider are formulated. Moreover, the behaviors of motion in glide and thrust operation are analyzed based on the simulation and the feasibility of the dynamic model is validated by data from lake field trials.

  1. Full field reservoir modeling of shale assets using advanced data-driven analytics

    Directory of Open Access Journals (Sweden)

    Soodabeh Esmaili

    2016-01-01

    Full Text Available Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems - induced or natural leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called “hard data” directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The “hard data” refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of “soft data” (non-measured, interpretive data such as frac length, width, height and conductivity in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.

  2. An automated toolchain for the data-driven and dynamical modeling of combined sewer systems.

    Science.gov (United States)

    Troutman, Sara C; Schambach, Nathaniel; Love, Nancy G; Kerkez, Branko

    2017-12-01

    The recent availability and affordability of sensors and wireless communications is poised to transform our understanding and management of water systems. This will enable a new generation of adaptive water models that can ingest large quantities of sensor feeds and provide the best possible estimates of current and future conditions. To that end, this paper presents a novel data-driven identification/learning toolchain for combined sewer and stormwater systems. The toolchain uses Gaussian Processes to model dry-weather flows (domestic wastewater) and dynamical System Identification to represent wet-weather flows (rainfall runoff). By using a large and high-resolution sensor dataset across a real-world combined sewer system, we illustrate that relatively simple models can achieve good forecasting performance, subject to a finely-tuned and continuous re-calibration procedure. The data requirements of the proposed toolchain are evaluated, showing sensitivity to spatial heterogeneity and unique time-scales across which models of individual sites remain representative. We identify a near-optimal time record, or data "age," for which historical measurements must be available to ensure good forecasting performance. We also show that more data do not always lead to a better model due to system uncertainty, such as shifts in climate or seasonal wastewater patterns. Furthermore, the individual components of the model (wet- and dry-weather) often require different volumes of historical observations for optimal forecasting performance, thus highlighting the need for a flexible re-calibration toolchain rather than a one-size-fits-all approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Electrostatic models of electron-driven proton transfer across a lipid membrane

    Energy Technology Data Exchange (ETDEWEB)

    Smirnov, Anatoly Yu; Nori, Franco [Advanced Science Institute, RIKEN, Wako-shi, Saitama, 351-0198 (Japan); Mourokh, Lev G [Department of Physics, Queens College, The City University of New York, Flushing, NY 11367 (United States)

    2011-06-15

    We present two models for electron-driven uphill proton transport across lipid membranes, with the electron energy converted to the proton gradient via the electrostatic interaction. In the first model, associated with the cytochrome c oxidase complex in the inner mitochondria membranes, the electrostatic coupling to the site occupied by an electron lowers the energy level of the proton-binding site, making proton transfer possible. In the second model, roughly describing the redox loop in a nitrate respiration of E. coli bacteria, an electron displaces a proton from the negative side of the membrane to a shuttle, which subsequently diffuses across the membrane and unloads the proton to its positive side. We show that both models can be described by the same approach, which can be significantly simplified if the system is separated into several clusters, with strong Coulomb interaction inside each cluster and weak transfer couplings between them. We derive and solve the equations of motion for the electron and proton creation/annihilation operators, taking into account the appropriate Coulomb terms, tunnel couplings, and the interaction with the environment. For the second model, these equations of motion are solved jointly with a Langevin-type equation for the shuttle position. We obtain expressions for the electron and proton currents and determine their dependence on the electron and proton voltage build-ups, on-site charging energies, reorganization energies, temperature, and other system parameters. We show that the quantum yield in our models can be up to 100% and the power-conversion efficiency can reach 35%.

  4. A simple model for farmland nitrogen loss to surface runoff with raindrop driven process

    Science.gov (United States)

    Tong, J.; Li, J.

    2016-12-01

    It has been widely recognized that surface runoff from the agricultural fields is an important source of non-point source pollution (NPSP). Moreover, as the agricultural country with the largest nitrogen fertilizer production, import and consumption in the world, our nation should pay greater attention to the over-application and inefficient use of nitrogen (N) fertilizer, which may cause severe pollution both in surface water and groundwater. To figure out the transfer mechanism between the soil solution and surface runoff, lots of laboratory test were conducted and related models were established in this study. But little of them was carried out in field scale since a part of variables are hard to control and some uncontrollable natural factors including rainfall intensity, temperature, wind speeds, soil spatial heterogeneity etc., may affect the field experimental results. Despite that, field tests can better reflect the mechanism of soil chemical loss to surface runoff than laboratory experiments, and the latter tend to oversimplify the environmental conditions. Therefore, a physically based, nitrogen transport model was developed and tested with so called semi-field experiments (i.e., artificial rainfall instead of natural rainfall was applied in the test). Our model integrated both raindrop driven process and diffusion effect along with the simplified nitrogen chain reactions. The established model was solved numerically through the modified Hydrus-1d source code, and the model simulations closely agree with the experimental data. Furthermore, our model indicates that the depth of the exchange layer and raindrop induced water transfer rate are two important parameters, and they have different impacts on the simulation results. The study results can provide references for preventing and controlling agricultural NPSP.

  5. Agent-based Modeling Automated: Data-driven Generation of Innovation Diffusion Models

    NARCIS (Netherlands)

    Jensen, T.; Chappin, E.J.L.

    2016-01-01

    Simulation modeling is useful to gain insights into driving mechanisms of diffusion of innovations. This study aims to introduce automation to make identification of such mechanisms with agent-based simulation modeling less costly in time and labor. We present a novel automation procedure in which

  6. Vibration control of a pneumatic driven piezoelectric flexible manipulator using self-organizing map based multiple models

    Science.gov (United States)

    Zhao, Zhi-li; Qiu, Zhi-cheng; Zhang, Xian-min; Han, Jian-da

    2016-03-01

    A kind of hybrid pneumatic-piezoelectric flexible manipulator system has been presented in the paper. A hybrid driving scheme is achieved by combining of a pneumatic proportional valve based pneumatic drive and a piezoelectric actuator bonded to the flexible beam. The system dynamics models are obtained based on system identification approaches, using the established experimental system. For system identification of the flexible piezoelectric manipulator subsystem, parametric estimation methods are utilized. For the pneumatic driven system, a single global linear model is not accurate enough to describe its dynamics, due to the high nonlinearity of the pneumatic driven system. Therefore, a self-organizing map (SOM) based multi-model system identification approach is used to get multiple local linear models. Then, a SOM based multi-model inverse controller and a variable damping pole-placement controller are applied to the pneumatic drive and piezoelectric actuator, respectively. Experiments on pneumatic driven vibration control, piezoelectric vibration control and hybrid vibration control are conducted, utilized proportional and derivative (PD) control, SOM based multi-model inverse controller, and the variable damping pole-placement controller. Experimental results demonstrate that the investigated control algorithms can improve the vibration control performance of the pneumatic driven flexible piezoelectric manipulator system.

  7. Automatic extraction of soft tissues from 3D MRI head images using model driven analysis

    International Nuclear Information System (INIS)

    Jiang, Hao; Yamamoto, Shinji; Imao, Masanao.

    1995-01-01

    This paper presents an automatic extraction system (called TOPS-3D : Top Down Parallel Pattern Recognition System for 3D Images) of soft tissues from 3D MRI head images by using model driven analysis algorithm. As the construction of system TOPS we developed, two concepts have been considered in the design of system TOPS-3D. One is the system having a hierarchical structure of reasoning using model information in higher level, and the other is a parallel image processing structure used to extract plural candidate regions for a destination entity. The new points of system TOPS-3D are as follows. (1) The TOPS-3D is a three-dimensional image analysis system including 3D model construction and 3D image processing techniques. (2) A technique is proposed to increase connectivity between knowledge processing in higher level and image processing in lower level. The technique is realized by applying opening operation of mathematical morphology, in which a structural model function defined in higher level by knowledge representation is immediately used to the filter function of opening operation as image processing in lower level. The system TOPS-3D applied to 3D MRI head images consists of three levels. First and second levels are reasoning part, and third level is image processing part. In experiments, we applied 5 samples of 3D MRI head images with size 128 x 128 x 128 pixels to the system TOPS-3D to extract the regions of soft tissues such as cerebrum, cerebellum and brain stem. From the experimental results, the system is robust for variation of input data by using model information, and the position and shape of soft tissues are extracted corresponding to anatomical structure. (author)

  8. Model-driven approach to data collection and reporting for quality improvement.

    Science.gov (United States)

    Curcin, Vasa; Woodcock, Thomas; Poots, Alan J; Majeed, Azeem; Bell, Derek

    2014-12-01

    Continuous data collection and analysis have been shown essential to achieving improvement in healthcare. However, the data required for local improvement initiatives are often not readily available from hospital Electronic Health Record (EHR) systems or not routinely collected. Furthermore, improvement teams are often restricted in time and funding thus requiring inexpensive and rapid tools to support their work. Hence, the informatics challenge in healthcare local improvement initiatives consists of providing a mechanism for rapid modelling of the local domain by non-informatics experts, including performance metric definitions, and grounded in established improvement techniques. We investigate the feasibility of a model-driven software approach to address this challenge, whereby an improvement model designed by a team is used to automatically generate required electronic data collection instruments and reporting tools. To that goal, we have designed a generic Improvement Data Model (IDM) to capture the data items and quality measures relevant to the project, and constructed Web Improvement Support in Healthcare (WISH), a prototype tool that takes user-generated IDM models and creates a data schema, data collection web interfaces, and a set of live reports, based on Statistical Process Control (SPC) for use by improvement teams. The software has been successfully used in over 50 improvement projects, with more than 700 users. We present in detail the experiences of one of those initiatives, Chronic Obstructive Pulmonary Disease project in Northwest London hospitals. The specific challenges of improvement in healthcare are analysed and the benefits and limitations of the approach are discussed. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  9. First steps towards modeling of ion-driven turbulence in Wendelstein 7-X

    Science.gov (United States)

    Warmer, F.; Xanthopoulos, P.; Proll, J. H. E.; Beidler, C. D.; Turkin, Y.; Wolf, R. C.

    2018-01-01

    Due to foreseen improvement of neoclassical confinement in optimised stellarators—like the newly commissioned Wendelstein 7-X (W7-X) experiment in Greifswald, Germany—it is expected that turbulence will significantly contribute to the heat and particle transport, thus posing a limit to the performance of such devices. In order to develop discharge scenarios, it is thus necessary to develop a model which could reliably capture the basic characteristics of turbulence and try to predict the levels thereof. The outcome will not only be affordable, using only a fraction of the computational cost which is normally required for repetitive direct turbulence simulations, but would also highlight important physics. In this model, we seek to describe the ion heat flux caused by ion temperature gradient (ITG) micro-turbulence, which, in certain heating scenarios, can be a strong source of free energy. With the aid of a relatively small number of state-of-the-art nonlinear gyrokinetic simulations, an initial critical gradient model (CGM) is devised, with the aim to replace an empirical model, stemming from observations in prior stellarator experiments. The novel CGM, in its present form, encapsulates all available knowledge about ion-driven 3D turbulence to date, also allowing for further important extensions, towards an accurate interpretation and prediction of the ‘anomalous’ transport. The CGM depends on the stiffness of the ITG turbulence scaling in W7-X, and implicitly includes the nonlinear zonal flow response. It is shown that the CGM is suitable for a 1D framework turbulence modeling.

  10. New trajectory-driven aerosol and chemical process model Chemical and Aerosol Lagrangian Model (CALM

    Directory of Open Access Journals (Sweden)

    P. Tunved

    2010-11-01

    Full Text Available A new Chemical and Aerosol Lagrangian Model (CALM has been developed and tested. The model incorporates all central aerosol dynamical processes, from nucleation, condensation, coagulation and deposition to cloud formation and in-cloud processing. The model is tested and evaluated against observations performed at the SMEAR II station located at Hyytiälä (61° 51' N, 24° 17' E over a time period of two years, 2000–2001. The model shows good agreement with measurements throughout most of the year, but fails in reproducing the aerosol properties during the winter season, resulting in poor agreement between model and measurements especially during December–January. Nevertheless, through the rest of the year both trends and magnitude of modal concentrations show good agreement with observation, as do the monthly average size distribution properties. The model is also shown to capture individual nucleation events to a certain degree. This indicates that nucleation largely is controlled by the availability of nucleating material (as prescribed by the [H2SO4], availability of condensing material (in this model 15% of primary reactions of monoterpenes (MT are assumed to produce low volatile species and the properties of the size distribution (more specifically, the condensation sink. This is further demonstrated by the fact that the model captures the annual trend in nuclei mode concentration. The model is also used, alongside sensitivity tests, to examine which processes dominate the aerosol size distribution physical properties. It is shown, in agreement with previous studies, that nucleation governs the number concentration during transport from clean areas. It is also shown that primary number emissions almost exclusively govern the CN concentration when air from Central Europe is advected north over Scandinavia. We also show that biogenic emissions have a large influence on the amount of potential CCN observed

  11. Performance-based parameter tuning method of model-driven PID control systems.

    Science.gov (United States)

    Zhao, Y M; Xie, W F; Tu, X W

    2012-05-01

    In this paper, performance-based parameter tuning method of model-driven Two-Degree-of-Freedom PID (MD TDOF PID) control system has been proposed to enhance the control performances of a process. Known for its ability of stabilizing the unstable processes, fast tracking to the change of set points and rejecting disturbance, the MD TDOF PID has gained research interest recently. The tuning methods for the reported MD TDOF PID are based on internal model control (IMC) method instead of optimizing the performance indices. In this paper, an Integral of Time Absolute Error (ITAE) zero-position-error optimal tuning and noise effect minimizing method is proposed for tuning two parameters in MD TDOF PID control system to achieve the desired regulating and disturbance rejection performance. The comparison with Two-Degree-of-Freedom control scheme by modified smith predictor (TDOF CS MSP) and the designed MD TDOF PID tuned by the IMC tuning method demonstrates the effectiveness of the proposed tuning method. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Numerical Modelling and Simulation of Dynamic Parameters for Vibration Driven Mobile Robot: Preliminary Study

    Science.gov (United States)

    Baharudin, M. E.; Nor, A. M.; Saad, A. R. M.; Yusof, A. M.

    2018-03-01

    The motion of vibration-driven robots is based on an internal oscillating mass which can move without legs or wheels. The oscillation of the unbalanced mass by a motor is translated into vibration which in turn produces vertical and horizontal forces. Both vertical and horizontal oscillations are of the same frequency but the phases are shifted. The vertical forces will deflect the bristles which cause the robot to move forward. In this paper, the horizontal motion direction caused by the vertically vibrated bristle is numerically simulated by tuning the frequency of their oscillatory actuation. As a preliminary work, basic equations for a simple off-centered vibration location on the robot platform and simulation model for vibration excitement are introduced. It involves both static and dynamic vibration analysis of robots and analysis of different type of parameters. In addition, the orientation of the bristles and oscillators are also analysed. Results from the numerical integration seem to be in good agreement with those achieved from the literature. The presented numerical integration modeling can be used for designing the bristles and controlling the speed and direction of the robot.

  13. Modeling particle emission and power flow in pulsed-power driven, nonuniform transmission lines

    Directory of Open Access Journals (Sweden)

    Nichelle Bruner

    2008-04-01

    Full Text Available Pulsed-power driven x-ray radiographic systems are being developed to operate at higher power in an effort to increase source brightness and penetration power. Essential to the design of these systems is a thorough understanding of electron power flow in the transmission line that couples the pulsed-power driver to the load. In this paper, analytic theory and fully relativistic particle-in-cell simulations are used to model power flow in several experimental transmission-line geometries fielded on Sandia National Laboratories’ upgraded Radiographic Integrated Test Stand [IEEE Trans. Plasma Sci. 28, 1653 (2000ITPSBD0093-381310.1109/27.901250]. Good agreement with measured electrical currents is demonstrated on a shot-by-shot basis for simulations which include detailed models accounting for space-charge-limited electron emission, surface heating, and stimulated particle emission. Resonant cavity modes related to the transmission-line impedance transitions are also shown to be excited by electron power flow. These modes can drive oscillations in the output power of the system, degrading radiographic resolution.

  14. Investigation of SIBM driven recrystallization in alpha Zirconium based on EBSD data and Monte Carlo modeling

    Science.gov (United States)

    Jedrychowski, M.; Bacroix, B.; Salman, O. U.; Tarasiuk, J.; Wronski, S.

    2015-08-01

    The work focuses on the influence of moderate plastic deformation on subsequent partial recrystallization of hexagonal zirconium (Zr702). In the considered case, strain induced boundary migration (SIBM) is assumed to be the dominating recrystallization mechanism. This hypothesis is analyzed and tested in detail using experimental EBSD-OIM data and Monte Carlo computer simulations. An EBSD investigation is performed on zirconium samples, which were channel-die compressed in two perpendicular directions: normal direction (ND) and transverse direction (TD) of the initial material sheet. The maximal applied strain was below 17%. Then, samples were briefly annealed in order to achieve a partly recrystallized state. Obtained EBSD data were analyzed in terms of texture evolution associated with a microstructural characterization, including: kernel average misorientation (KAM), grain orientation spread (GOS), twinning, grain size distributions, description of grain boundary regions. In parallel, Monte Carlo Potts model combined with experimental microstructures was employed in order to verify two main recrystallization scenarios: SIBM driven growth from deformed sub-grains and classical growth of recrystallization nuclei. It is concluded that simulation results provided by the SIBM model are in a good agreement with experimental data in terms of texture as well as microstructural evolution.

  15. A data-driven decomposition approach to model aerodynamic forces on flapping airfoils

    Science.gov (United States)

    Raiola, Marco; Discetti, Stefano; Ianiro, Andrea

    2017-11-01

    In this work, we exploit a data-driven decomposition of experimental data from a flapping airfoil experiment with the aim of isolating the main contributions to the aerodynamic force and obtaining a phenomenological model. Experiments are carried out on a NACA 0012 airfoil in forward flight with both heaving and pitching motion. Velocity measurements of the near field are carried out with Planar PIV while force measurements are performed with a load cell. The phase-averaged velocity fields are transformed into the wing-fixed reference frame, allowing for a description of the field in a domain with fixed boundaries. The decomposition of the flow field is performed by means of the POD applied on the velocity fluctuations and then extended to the phase-averaged force data by means of the Extended POD approach. This choice is justified by the simple consideration that aerodynamic forces determine the largest contributions to the energetic balance in the flow field. Only the first 6 modes have a relevant contribution to the force. A clear relationship can be drawn between the force and the flow field modes. Moreover, the force modes are closely related (yet slightly different) to the contributions of the classic potential models in literature, allowing for their correction. This work has been supported by the Spanish MINECO under Grant TRA2013-41103-P.

  16. Spatially explicit integrated modeling and economic valuation of climate driven land use change and its indirect effects

    OpenAIRE

    Bateman, Ian; Agarwala, Matthew; Binner, Amy; Coombes, Emma; Day, Brett; Ferrini, Silvia; Fezzi, Carlo; Hutchins, Michael; Lovett, Andrew; Posen, Paulette

    2016-01-01

    We present an integrated model of the direct consequences of climate change on land use, and the indirect effects of induced land use change upon the natural environment. The model predicts climate-driven shifts in the profitability of alternative uses of agricultural land. Both the direct impact of climate change and the induced shift in land use patterns will cause secondary effects on the water environment, for which agriculture is the major source of diffuse pollution. We model the impact...

  17. Observable signatures of wind--driven chemistry with a fully consistent three dimensional radiative hydrodynamics model of HD 209458b

    OpenAIRE

    Drummond, Benjamin; Mayne, N. J.; Manners, James; Carter, Aarynn L.; Boutle, Ian A.; Baraffe, Isabelle; Hebrard, Eric; Tremblin, Pascal; Sing, David K.; Amundsen, David S.; Acreman, Dave

    2018-01-01

    We present a study of the effect of wind-driven advection on the chemical composition of hot Jupiter atmospheres using a fully-consistent 3D hydrodynamics, chemistry and radiative transfer code, the Met Office Unified Model (UM). Chemical modelling of exoplanet atmospheres has primarily been restricted to 1D models that cannot account for 3D dynamical processes. In this work we couple a chemical relaxation scheme to the UM to account for the chemical interconversion of methane and carbon mono...

  18. A Model-Driven Approach for 3D Modeling of Pylon from Airborne LiDAR Data

    Directory of Open Access Journals (Sweden)

    Qingquan Li

    2015-09-01

    Full Text Available Reconstructing three-dimensional model of the pylon from LiDAR (Light Detection And Ranging point clouds automatically is one of the key techniques for facilities management GIS system of high-voltage nationwide transmission smart grid. This paper presents a model-driven three-dimensional pylon modeling (MD3DM method using airborne LiDAR data. We start with constructing a parametric model of pylon, based on its actual structure and the characteristics of point clouds data. In this model, a pylon is divided into three parts: pylon legs, pylon body and pylon head. The modeling approach mainly consists of four steps. Firstly, point clouds of individual pylon are detected and segmented from massive high-voltage transmission corridor point clouds automatically. Secondly, an individual pylon is divided into three relatively simple parts in order to reconstruct different parts with different strategies. Its position and direction are extracted by contour analysis of the pylon body in this stage. Thirdly, the geometric features of the pylon head are extracted, from which the head type is derived with a SVM (Support Vector Machine classifier. After that, the head is constructed by seeking corresponding model from pre-build model library. Finally, the body is modeled by fitting the point cloud to planes. Experiment results on several point clouds data sets from China Southern high-voltage nationwide transmission grid from Yunnan Province to Guangdong Province show that the proposed approach can achieve the goal of automatic three-dimensional modeling of the pylon effectively.

  19. Generalizable occupant-driven optimization model for domestic hot water production in NZEB

    International Nuclear Information System (INIS)

    Kazmi, H.; D’Oca, S.; Delmastro, C.; Lodeweyckx, S.; Corgnati, S.P.

    2016-01-01

    Highlights: • Smart meter data for domestic hot water consumption is collected for 46 NZEB. • Reinforcement learning optimizes energy consumed while constrained on user comfort. • Online optimization models learn occupant behaviour and system thermodynamics. • Offline generalizable models calibrate dynamically the storage vessel operation. • Real world application of the active controls resulted in energy savings of 27%. - Abstract: The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water (DHW) production in residential buildings. This is done by deriving data-driven optimal heating schedules (used interchangeably with policies) automatically. The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism – an air-source heat pump (ASHP) in this case – at the highest possible efficiency. The proposed algorithm, while tested on an ASHP, is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well. Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings (NZEB) in the Netherlands are presented. These show a reduction of energy consumption for DHW by 20% using a computationally inexpensive heuristic approach, and 27% when using a more intensive hybrid ant colony optimization based method. The energy savings are strongly dependent on occupant comfort level. This is demonstrated in real-world settings for a low-consumption house where active control was performed using heuristics for 3.5 months and resulted in energy savings of 27% (61 kW h). It is straightforward to extend the same models to perform automatic demand side management (ADSM) by treating the DHW vessel as a flexibility bearing device.

  20. Modeling and Experimental Tests on the Hydraulically Driven Control Rod option for IRIS Reactor

    International Nuclear Information System (INIS)

    Cammi, Antonio; Ricotti, Marco E.; Vitulo, Alessia

    2004-01-01

    The adoption of Internal Control Rod Drive Mechanisms (ICRDMs) represents a valuable alternative to classical, external CRDMs based on electro-magnetic devices, as adopted in current PWRs. The advantages on the safety features of the reactor are apparent: inherent elimination of the Rod Ejection accidents and of possible concerns about the vessel head penetrations. A further positive feedback on the design is the reduction of the primary system overall dimensions. Within the frame of the ICRDM concepts, the Hydraulically Driven Control Rod solution is investigated as a possible option for the IRIS integral reactor. After a brief comparison of the solutions currently proposed for integral reactors, the configuration of the Hydraulic Control Rod device for IRIS, made up by an external movable piston and an internal fixed cylinder, is described. A description of the whole control system is reported as well. Particular attention is devoted to the Control Rod profile characterization, performed by means of a Computational Fluid Dynamics (CFD) analysis. The investigation of the system behavior has been carried out, including the dynamic equilibrium and its stability properties, the withdrawal and insertion step movement and the sensitivity study on command time periods. A suitable dynamic model has been set up for the mentioned purposes: the models corresponding to the various Control Rod system devices have been written in an Object-Oriented language (Modelica), thus allowing an easy implementation of such a system into the simulator for the whole reactor. Finally, a preliminary low pressure, low temperature, reduced length experimental facility has been built. Tests on HDCR stability and operational transients have been performed. The results are compared with the dynamic system model and CFD simulation model, showing good agreement between simulations and experimental data. During these preliminary tests, the control system performed correctly, allowing stable dynamic

  1. Variations in Modeled Dengue Transmission over Puerto Rico Using a Climate Driven Dynamic Model

    Science.gov (United States)

    Morin, Cory; Monaghan, Andrew; Crosson, William; Quattrochi, Dale; Luvall, Jeffrey

    2014-01-01

    Dengue fever is a mosquito-borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Because of variations in topography, ocean influences and atmospheric processes, temperature and rainfall patterns vary across Puerto Rico and so do dengue virus transmission rates. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input, ground-based observations for temperature input, and laboratory confirmed dengue cases reported by the Centers for Disease Control and Prevention for parameter calibration, we modeled dengue transmission at the county level across Puerto Rico from 2010-2013 using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations for each county in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. The top 1% of model simulations that best reproduced the reported dengue case data were then analyzed to determine the most important parameters for dengue virus transmission in each county, as well as the relative influence of climate variability on transmission. These results can be used by public health workers to implement dengue control methods that are targeted for specific locations and climate conditions.

  2. User-driven Cloud Implementation of environmental models and data for all

    Science.gov (United States)

    Gurney, R. J.; Percy, B. J.; Elkhatib, Y.; Blair, G. S.

    2014-12-01

    Environmental data and models come from disparate sources over a variety of geographical and temporal scales with different resolutions and data standards, often including terabytes of data and model simulations. Unfortunately, these data and models tend to remain solely within the custody of the private and public organisations which create the data, and the scientists who build models and generate results. Although many models and datasets are theoretically available to others, the lack of ease of access tends to keep them out of reach of many. We have developed an intuitive web-based tool that utilises environmental models and datasets located in a cloud to produce results that are appropriate to the user. Storyboards showing the interfaces and visualisations have been created for each of several exemplars. A library of virtual machine images has been prepared to serve these exemplars. Each virtual machine image has been tailored to run computer models appropriate to the end user. Two approaches have been used; first as RESTful web services conforming to the Open Geospatial Consortium (OGC) Web Processing Service (WPS) interface standard using the Python-based PyWPS; second, a MySQL database interrogated using PHP code. In all cases, the web client sends the server an HTTP GET request to execute the process with a number of parameter values and, once execution terminates, an XML or JSON response is sent back and parsed at the client side to extract the results. All web services are stateless, i.e. application state is not maintained by the server, reducing its operational overheads and simplifying infrastructure management tasks such as load balancing and failure recovery. A hybrid cloud solution has been used with models and data sited on both private and public clouds. The storyboards have been transformed into intuitive web interfaces at the client side using HTML, CSS and JavaScript, utilising plug-ins such as jQuery and Flot (for graphics), and Google Maps

  3. The enhanced information flow from visual cortex to frontal area facilitates SSVEP response: evidence from model-driven and data-driven causality analysis

    Science.gov (United States)

    Li, Fali; Tian, Yin; Zhang, Yangsong; Qiu, Kan; Tian, Chunyang; Jing, Wei; Liu, Tiejun; Xia, Yang; Guo, Daqing; Yao, Dezhong; Xu, Peng

    2015-10-01

    The neural mechanism of steady-state visual evoked potentials (SSVEP) is still not clearly understood. Especially, only certain frequency stimuli can evoke SSVEP. Our previous network study reveals that 8 Hz stimulus that can evoke strong SSVEP response shows the enhanced linkage strength between frontal and visual cortex. To further probe the directed information flow between the two cortex areas for various frequency stimuli, this paper develops a causality analysis based on the inversion of double columns model using particle swarm optimization (PSO) to characterize the directed information flow between visual and frontal cortices with the intracranial rat electroencephalograph (EEG). The estimated model parameters demonstrate that the 8 Hz stimulus shows the enhanced directional information flow from visual cortex to frontal lobe facilitates SSVEP response, which may account for the strong SSVEP response for 8 Hz stimulus. Furthermore, the similar finding is replicated by data-driven causality analysis. The inversion of neural mass model proposed in this study may be helpful to provide the new causality analysis to link the physiological model and the observed datasets in neuroscience and clinical researches.

  4. Data-driven modeling of transportation systems and traffic data analysis during a major power outage in the Netherlands

    NARCIS (Netherlands)

    Melnikov, V.R.; Krzhizhanovskaya, V.V.; Boukhanovsky, A.V.; Sloot, P.M.A.

    2015-01-01

    Efficient methods and tools for road network planning and traffic management are critically important in the ever more urbanized world. The goal of our research is the development of a data-driven multiscale modeling approach for accurate simulation of road traffic in real-life transportation

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

    Science.gov (United States)

    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.

  6. Ising and Bloch domain walls in a two-dimensional parametrically driven Ginzburg-Landau equation model with nonlinearity management

    DEFF Research Database (Denmark)

    Gaididei, Yu. B.; Christiansen, Peter Leth

    2008-01-01

    We study a parametrically driven Ginzburg-Landau equation model with nonlinear management. The system is made of laterally coupled long active waveguides placed along a circumference. Stationary solutions of three kinds are found: periodic Ising states and two types of Bloch states, staggered and...

  7. Simulation Methods for High-Cycle Fatigue-Driven Delamination using Cohesive Zone Models - Fundamental Behavior and Benchmark Studies

    DEFF Research Database (Denmark)

    Bak, Brian Lau Verndal; Lindgaard, Esben; Turon, A.

    2015-01-01

    A novel computational method for simulating fatigue-driven delamination cracks in composite laminated structures under cyclic loading based on a cohesive zone model [2] and new benchmark studies with four other comparable methods [3-6] are presented. The benchmark studies describe and compare the...

  8. Learning-Goals-Driven Design Model: Developing Curriculum Materials that Align with National Standards and Incorporate Project-Based Pedagogy

    Science.gov (United States)

    Krajcik, Joseph; McNeill, Katherine L.; Reiser, Brian J.

    2008-01-01

    Reform efforts in science education emphasize the importance of rigorous treatment of science standards and use of innovative pedagogical approaches to make science more meaningful and successful. In this paper, we present a learning-goals-driven design model for developing curriculum materials, which combines national standards and a…

  9. Using the Dynamic Model to develop an evidence-based and theory-driven approach to school improvement

    NARCIS (Netherlands)

    Creemers, B.P.M.; Kyriakides, L.

    2010-01-01

    This paper refers to a dynamic perspective of educational effectiveness and improvement stressing the importance of using an evidence-based and theory-driven approach. Specifically, an approach to school improvement based on the dynamic model of educational effectiveness is offered. The recommended

  10. Current Trends in the Detection of Sociocultural Signatures: Data-Driven Models

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Bell, Eric B.; Corley, Courtney D.

    2014-09-15

    available that are shaping social computing as a strongly data-driven experimental discipline with an increasingly stronger impact on the decision-making process of groups and individuals alike. In this chapter, we review current advances and trends in the detection of sociocultural signatures. Specific embodiments of the issues discussed are provided with respect to the assessment of violent intent and sociopolitical contention. We begin by reviewing current approaches to the detection of sociocultural signatures in these domains. Next, we turn to the review of novel data harvesting methods for social media content. Finally, we discuss the application of sociocultural models to social media content, and conclude by commenting on current challenges and future developments.

  11. Data-Driven Microbial Modeling for Soil Carbon Decomposition and Stabilization

    Science.gov (United States)

    Luo, Yiqi; Chen, Ji; Chen, Yizhao; Feng, Wenting

    2017-04-01

    Microorganisms have long been known to catalyze almost all the soil organic carbon (SOC) transformation processes (e.g., decomposition, stabilization, and mineralization). Representing microbial processes in Earth system models (ESMs) has the potential to improve projections of SOC dynamics. We have recently examined (1) relationships of microbial functions with environmental factors and (2) microbial regulations of decomposition and other key soil processes. According to three lines of evidence, we have developed a data-driven enzyme (DENZY) model to simulate soil microbial decomposition and stabilization. First, our meta-analysis of 64 published field studies showed that field experimental warming significantly increased soil microbial communities abundance, which is negatively correlated with the mean annual temperature. The negative correlation indicates that warming had stronger effects in colder than warmer regions. Second, we found that the SOC decomposition, especially the transfer between labile SOC and protected SOC, is nonlinearly regulated by soil texture parameters, such as sand and silt contents. Third, we conducted a global analysis of the C-degrading enzyme activities, soil respiration, and SOC content under N addition. Our results show that N addition has contrasting effects on cellulase (hydrolytic C-degrading enzymes) and ligninase (oxidative C-degrading enzymes) activities. N-enhanced cellulase activity contributes to the minor stimulation of soil respiration whereas N-induced repression on ligninase activity drives soil C sequestration. Our analysis links the microbial extracellular C-degrading enzymes to the SOC dynamics at ecosystem scales across scores of experimental sites around the world. It offers direct evidence that N-induced changes in microbial community and physiology play fundamental roles in controlling the soil C cycle. Built upon those three lines of empirical evidence, the DENZY model includes two enzyme pools and explicitly

  12. A Pathophysiological Model-Driven Communication for Dynamic Distributed Medical Best Practice Guidance Systems.

    Science.gov (United States)

    Hosseini, Mohammad; Jiang, Yu; Wu, Poliang; Berlin, Richard B; Ren, Shangping; Sha, Lui

    2016-11-01

    There is a great divide between rural and urban areas, particularly in medical emergency care. Although medical best practice guidelines exist and are in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. In this paper, we propose the concept of distributed executable medical best practice guidance systems to assist adherence to best practice from the time that a patient first presents at a rural hospital, through diagnosis and ambulance transfer to arrival and treatment at a regional tertiary hospital center. We codify complex medical knowledge in the form of simplified distributed executable disease automata, from the thin automata at rural hospitals to the rich automata in the regional center hospitals. However, a main challenge is how to efficiently and safely synchronize distributed best practice models as the communication among medical facilities, devices, and professionals generates a large number of messages. This complex problem of patient diagnosis and transport from rural to center facility is also fraught with many uncertainties and changes resulting in a high degree of dynamism. A critically ill patient's medical conditions can change abruptly in addition to changes in the wireless bandwidth during the ambulance transfer. Such dynamics have yet to be addressed in existing literature on telemedicine. To address this situation, we propose a pathophysiological model-driven message exchange communication architecture that ensures the real-time and dynamic requirements of synchronization among distributed emergency best practice models are met in a reliable and safe manner. Taking the signs, symptoms, and progress of stroke patients transported across a geographically distributed healthcare network as the motivating use case, we implement our communication system and apply it to our developed best practice

  13. Satellite-driven modeling approach for monitoring lava flow hazards during the 2017 Etna eruption

    Science.gov (United States)

    Del Negro, C.; Bilotta, G.; Cappello, A.; Ganci, G.; Herault, A.; Zago, V.

    2017-12-01

    The integration of satellite data and modeling represents an efficient strategy that may provide immediate answers to the main issues raised at the onset of a new effusive eruption. Satellite-based thermal remote sensing of hotspots related to effusive activity can effectively provide a variety of products suited to timing, locating, and tracking the radiant character of lava flows. Hotspots show the location and occurrence of eruptive events (vents). Discharge rate estimates may indicate the current intensity (effusion rate) and potential magnitude (volume). High-spatial resolution multispectral satellite data can complement field observations for monitoring the front position (length) and extension of flows (area). Physics-based models driven, or validated, by satellite-derived parameters are now capable of fast and accurate forecast of lava flow inundation scenarios (hazard). Here, we demonstrate the potential of the integrated application of satellite remote-sensing techniques and lava flow models during the 2017 effusive eruption at Mount Etna in Italy. This combined approach provided insights into lava flow field evolution by supplying detailed views of flow field construction (e.g., the opening of ephemeral vents) that were useful for more accurate and reliable forecasts of eruptive activity. Moreover, we gave a detailed chronology of the lava flow activity based on field observations and satellite images, assessed the potential extent of impacted areas, mapped the evolution of lava flow field, and executed hazard projections. The underside of this combination is the high sensitivity of lava flow inundation scenarios to uncertainties in vent location, discharge rate, and other parameters, which can make interpreting hazard forecasts difficult during an effusive crisis. However, such integration at last makes timely forecasts of lava flow hazards during effusive crises possible at the great majority of volcanoes for which no monitoring exists.

  14. Missing Mass Approximations for the Partition Function of Stimulus Driven Ising Models

    Directory of Open Access Journals (Sweden)

    Robert eHaslinger

    2013-07-01

    Full Text Available Ising models are routinely used to quantify the second order, functional structure of neural populations. With some recent exceptions, they generally do not include the influence of time varying stimulus drive. Yet if the dynamics of network function are to be understood, time varying stimuli must be taken into account. Inclusion of stimulus drive carries a heavy computational burden because the partition function becomes stimulus dependent and must be separately calculated for all unique stimuli observed. This potentially increases computation time by the length of the data set. Here we present an extremely fast, yet simply implemented, method for approximating the stimulus dependent partition function in minutes or seconds. Noting that the most probable spike patterns (which are few occur in the training data, we sum partition function terms corresponding to those patterns explicitly. We then approximate the sum over the remaining patterns (which are improbable, but many by casting it in terms of the stimulus modulated missing mass (total stimulus dependent probability of all patterns not observed in the training data. We use use a product of conditioned logistic regression models to approximate the stimulus modulated missing mass. This method has complexity of roughly O(LNN_{pat} where is L the data length, N the number of neurons and N_{pat} the number of unique patterns in the data, contrasting with the O(L2^N complexity of alternate methods. Using multiple unit recordings from rat hippocampus, macaque DLPFC and cat Area 18 we demonstrate our method requires orders of magnitude less computation time than Monte Carlo methods and can approximate the stimulus driven partition function more accurately than either Monte Carlo methods or deterministic approximations. This advance allows stimuli to be easily included in Ising models making them suitable for studying population based stimulus encoding.

  15. Ontology driven modeling for the knowledge of genetic susceptibility to disease.

    Science.gov (United States)

    Lin, Yu; Sakamoto, Norihiro

    2009-04-30

    For the machine helped exploring the relationships between genetic factors and complex diseases, a well-structured conceptual framework of the background knowledge is needed. However, because of the complexity of determining a genetic susceptibility factor, there is no formalization for the knowledge of genetic susceptibility to disease, which makes the interoperability between systems impossible. Thus, the ontology modeling language OWL was used for formalization in this paper. After introducing the Semantic Web and OWL language propagated by W3C, we applied text mining technology combined with competency questions to specify the classes of the ontology. Then, an N-ary pattern was adopted to describe the relationships among these defined classes. Based on the former work of OGSF-DM (Ontology of Genetic Susceptibility Factors to Diabetes Mellitus), we formalized the definition of "Genetic Susceptibility", "Genetic Susceptibility Factor" and other classes by using OWL-DL modeling language; and a reasoner automatically performed the classification of the class "Genetic Susceptibility Factor". The ontology driven modeling is used for formalization the knowledge of genetic susceptibility to complex diseases. More importantly, when a class has been completely formalized in an ontology, the OWL reasoning can automatically compute the classification of the class, in our case, the class of "Genetic Susceptibility Factors". With more types of genetic susceptibility factors obtained from the laboratory research, our ontologies always needs to be refined, and many new classes must be taken into account to harmonize with the ontologies. Using the ontologies to develop the semantic web needs to be applied in the future.

  16. Proteome and Transcriptome Profiles of a Her2/Neu-driven Mouse Model of Breast Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Schoenherr, Regine M.; Kelly-Spratt, Karen S.; Lin, Chen Wei; Whiteaker, Jeffrey R.; Liu, Tao; Holzman, Ted; Coleman, Ilsa; Feng, Li-Chia; Lorentzen, Travis D.; Krasnoselsky, Alexei L.; Wang, Pei; Liu, Yan; Gurley, Kay E.; Amon, Lynn M.; Schepmoes, Athena A.; Moore, Ronald J.; Camp, David G.; Chodosh, Lewis A.; Smith, Richard D.; Nelson, Peter S.; McIntosh, Martin; Kemp, Christopher; Paulovich, Amanda G.

    2011-04-01

    In recent years, mouse models have proven to be invaluable in expanding our understanding of cancer biology. We have amassed a tremendous amount of proteomics and transcriptomics data profiling blood and tissues from a Her2-driven mouse model of breast cancer that closely recapitulates the pathology and natural history of human breast cancer. The purpose of this report is to make all of these data publicly available in raw and processed forms, as a resource to the community. Importantly, high quality biospecimens from this same mouse model are freely available through a sample repository that we established, so researchers can readily obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Specifically, six proteomics and six transcriptomics datasets are available, with the former encompassing 841 liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments of both plasma and tissue samples, and the latter including 255 individual microarray analyses of five different tissue types (thymus, spleen, liver, blood cells, and breast ± laser capture microdissection). A total of 18,880 unique peptides were identified with a PeptideProphet error rate ≤1%, with 3884 non-redundant protein groups identified in five plasma datasets, and 1659 non-redundant protein groups in a tissue dataset (4977 non-redundant protein groups in total). We anticipate that these data will be of use to the community for software tool development, investigations of analytical variation in MS/MS data, development of quality control tools (multiple technical replicates are provided for a subset of the data), empirical selection of proteotypic peptides for multiple reaction monitoring mass spectrometry, and for advancing our understanding of cancer biology.

  17. A Model-Driven Approach for Hybrid Power Estimation in Embedded Systems Design

    Directory of Open Access Journals (Sweden)

    Ben Atitallah Rabie

    2011-01-01

    Full Text Available Abstract As technology scales for increased circuit density and performance, the management of power consumption in system-on-chip (SoC is becoming critical. Today, having the appropriate electronic system level (ESL tools for power estimation in the design flow is mandatory. The main challenge for the design of such dedicated tools is to achieve a better tradeoff between accuracy and speed. This paper presents a consumption estimation approach allowing taking the consumption criterion into account early in the design flow during the system cosimulation. The originality of this approach is that it allows the power estimation for both white-box intellectual properties (IPs using annotated power models and black-box IPs using standalone power estimators. In order to obtain accurate power estimates, our simulations were performed at the cycle-accurate bit-accurate (CABA level, using SystemC. To make our approach fast and not tedious for users, the simulated architectures, including standalone power estimators, were generated automatically using a model driven engineering (MDE approach. Both annotated power models and standalone power estimators can be used together to estimate the consumption of the same architecture, which makes them complementary. The simulation results showed that the power estimates given by both estimation techniques for a hardware component are very close, with a difference that does not exceed 0.3%. This proves that, even when the IP code is not accessible or not modifiable, our approach allows obtaining quite accurate power estimates that early in the design flow thanks to the automation offered by the MDE approach.

  18. HIGH-FIDELITY SIMULATION-DRIVEN MODEL DEVELOPMENT FOR COARSE-GRAINED COMPUTATIONAL FLUID DYNAMICS

    Energy Technology Data Exchange (ETDEWEB)

    Hanna, Botros N.; Dinh, Nam T.; Bolotnov, Igor A.

    2016-06-01

    Nuclear reactor safety analysis requires identifying various credible accident scenarios and determining their consequences. For a full-scale nuclear power plant system behavior, it is impossible to obtain sufficient experimental data for a broad range of risk-significant accident scenarios. In single-phase flow convective problems, Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) can provide us with high fidelity results when physical data are unavailable. However, these methods are computationally expensive and cannot be afforded for simulation of long transient scenarios in nuclear accidents despite extraordinary advances in high performance scientific computing over the past decades. The major issue is the inability to make the transient computation parallel, thus making number of time steps required in high-fidelity methods unaffordable for long transients. In this work, we propose to apply a high fidelity simulation-driven approach to model sub-grid scale (SGS) effect in Coarse Grained Computational Fluid Dynamics CG-CFD. This approach aims to develop a statistical surrogate model instead of the deterministic SGS model. We chose to start with a turbulent natural convection case with volumetric heating in a horizontal fluid layer with a rigid, insulated lower boundary and isothermal (cold) upper boundary. This scenario of unstable stratification is relevant to turbulent natural convection in a molten corium pool during a severe nuclear reactor accident, as well as in containment mixing and passive cooling. The presented approach demonstrates how to create a correction for the CG-CFD solution by modifying the energy balance equation. A global correction for the temperature equation proves to achieve a significant improvement to the prediction of steady state temperature distribution through the fluid layer.

  19. Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method

    International Nuclear Information System (INIS)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2007-01-01

    In recent years, there has been intense interest in understanding various physical phenomena in random heterogeneous media. Any accurate description/simulation of a process in such media has to satisfactorily account for the twin issues of randomness as well as the multilength scale variations in the material properties. An accurate model of the material property variation in the system is an important prerequisite towards complete characterization of the system response. We propose a general methodology to construct a data-driven, reduced-order model to describe property variations in realistic heterogeneous media. This reduced-order model then serves as the input to the stochastic partial differential equation describing thermal diffusion through random heterogeneous media. A decoupled scheme is used to tackle the problems of stochasticity and multilength scale variations in properties. A sparse-grid collocation strategy is utilized to reduce the solution of the stochastic partial differential equation to a set of deterministic problems. A variational multiscale method with explicit subgrid modeling is used to solve these deterministic problems. An illustrative example using experimental data is provided to showcase the effectiveness of the proposed methodology

  20. The effect of ac-driven force on superlubricity in a two-dimensional Frenkel-Kontorova model

    International Nuclear Information System (INIS)

    Lin Maimai

    2010-01-01

    By using the molecular dynamic simulation method with a fourth-order Runge-Kutta algorithm, a two-dimensional dc- and ac-driven Frenkel-Kontorova model with a square symmetry substrate potential for a square lattice layer has been investigated in this paper. For this system, the effects of many different parameters on the static friction force have been studied in detail. It was found that not only the amplitude and frequency of the ac-driven force, but also the direction of dc- and ac-driven forces and the misfit angle between two layers have a strong influence on the static friction force. This indicated that the phenomenon of superlubricity appears easily with larger ac amplitude and smaller ac frequency for some special direction of the external driving force and misfit angle.

  1. Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM

    KAUST Repository

    Amer, Abdelhalim

    2013-01-01

    Extracting maximum performance of multi-core architectures is a difficult task primarily due to bandwidth limitations of the memory subsystem and its complex hierarchy. In this work, we study the implications of fork-join and data-driven execution models on this type of architecture at the level of task parallelism. For this purpose, we use a highly optimized fork-join based implementation of the FMM and extend it to a data-driven implementation using a distributed task scheduling approach. This study exposes some limitations of the conventional fork-join implementation in terms of synchronization overheads. We find that these are not negligible and their elimination by the data-driven method, with a careful data locality strategy, was beneficial. Experimental evaluation of both methods on state-of-the-art multi-socket multi-core architectures showed up to 22% speed-ups of the data-driven approach compared to the original method. We demonstrate that a data-driven execution of FMM not only improves performance by avoiding global synchronization overheads but also reduces the memory-bandwidth pressure caused by memory-intensive computations. © 2013 Springer-Verlag.

  2. Finite Element Modeling of Avascular Tumor Growth Using a Stress-Driven Model.

    Science.gov (United States)

    Iranmanesh, Faezeh; Nazari, Mohammad Ali

    2017-08-01

    Tumor growth being a multistage process has been investigated from different aspects. In the present study, an attempt is made to represent a constitutive-structure-based model of avascular tumor growth in which the effects of tensile stresses caused by collagen fibers are considered. Collagen fibers as a source of anisotropy in the structure of tissue are taken into account using a continuous fiber distribution formulation. To this end, a finite element modeling is implemented in which a neo-Hookean hyperelastic material is assigned to the tumor and its surrounding host. The tumor is supplied with a growth term. The growth term includes the effect of parameters such as nutrient concentration on the tumor growth and the tumor's solid phase content in the formulation. Results of the study revealed that decrease of solid phase is indicative of decrease in growth rate and the final steady-state value of tumor's radius. Moreover, fiber distribution affects the final shape of the tumor, and it could be used to control the shape and geometry of the tumor in complex morphologies. Finally, the findings demonstrated that the exerted stresses on the tumor increase as time passes. Compression of tumor cells leads to the reduction of tumor growth rate until it gradually reaches an equilibrium radius. This finding is in accordance with experimental data. Hence, this formulation can be deployed to evaluate both the residual stresses induced by growth and the mechanical interactions with the host tissue.

  3. Deep Modeling: Circuit Characterization Using Theory Based Models in a Data Driven Framework

    Energy Technology Data Exchange (ETDEWEB)

    Bolme, David S [ORNL; Mikkilineni, Aravind K [ORNL; Rose, Derek C [ORNL; Yoginath, Srikanth B [ORNL; Holleman, Jeremy [University of Tennessee, Knoxville (UTK); Judy, Mohsen [University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science

    2017-01-01

    Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilize measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.

  4. A transparent and data-driven global tectonic regionalisation model for seismic hazard assessment

    Science.gov (United States)

    Chen, Yen-Shin; Weatherill, Graeme; Pagani, Marco; Cotton, Fabrice

    2018-01-01

    A key concept that is common to many assumptions inherent within seismic hazard assessment is that of tectonic similarity. This recognises that certain regions of the globe may display similar geophysical characteristics, such as in the attenuation of seismic waves, the magnitude scaling properties of seismogenic sources or the seismic coupling of the lithosphere. Previous attempts at tectonic regionalisation, particularly within a seismic hazard assessment context, have often been based on expert judgements; in most of these cases, the process for delineating tectonic regions is neither reproducible nor consistent from location to location. In this work, the regionalisation process is implemented in a scheme that is reproducible, comprehensible from a geophysical rationale, and revisable when new relevant data are published. A spatial classification-scheme is developed based on fuzzy logic, enabling the quantification of concepts that are approximate rather than precise. Using the proposed methodology, we obtain a transparent and data-driven global tectonic regionalisation model for seismic hazard applications as well as the subjective probabilities (e.g. degree of being active/degree of being cratonic) indicate the degree to which a site belongs in a tectonic category.

  5. Coevolution in management fashion: an agent-based model of consultant-driven innovation.

    Science.gov (United States)

    Strang, David; David, Robert J; Akhlaghpour, Saeed

    2014-07-01

    The rise of management consultancy has been accompanied by increasingly marked faddish cycles in management techniques, but the mechanisms that underlie this relationship are not well understood. The authors develop a simple agent-based framework that models innovation adoption and abandonment on both the supply and demand sides. In opposition to conceptions of consultants as rhetorical wizards who engineer waves of management fashion, firms and consultants are treated as boundedly rational actors who chase the secrets of success by mimicking their highest-performing peers. Computational experiments demonstrate that consultant-driven versions of this dynamic in which the outcomes of firms are strongly conditioned by their choice of consultant are robustly faddish. The invasion of boom markets by low-quality consultants undercuts popular innovations while simultaneously restarting the fashion cycle by prompting the flight of high-quality consultants into less densely occupied niches. Computational experiments also indicate conditions involving consultant mobility, aspiration levels, mimic probabilities, and client-provider matching that attenuate faddishness.

  6. Econometric models for distinguishing between market-driven and publicly-funded energy efficiency

    International Nuclear Information System (INIS)

    Horowitz, Marvin J.

    2005-01-01

    Central to the problem of estimating energy program benefits is the necessity to differentiate between changes in energy use that would have occurred in the absence of public programs versus declines in energy use that would not have occurred but for public programs. The former changes are often referred to as naturally-occurring or market-driven effects. They occur due to a combination of one or more independent variables, such as changes in prices, incomes, weather, and technology. For a rigorous, scientifically-valid program evaluation, it is essential to first control for these variables before making statistical inferences related to public program effects. This paper describes the economic and statistical issues surrounding quantitative studies of energy use, energy efficiency, and public programs. To illustrate the strengths and weaknesses of different impact evaluation approaches, this paper describes three new studies related to electricity use in the U. S. commercial buildings sector. Specification and estimation of time series and cross section econometric models are discussed, as are their capabilities for obtaining long-run estimates of the net impacts of energy efficiency programs

  7. Modeling and simulations of new electrostatically driven, bimorph actuator for high beam steering micromirror deflection angles

    Science.gov (United States)

    Walton, John P.; Coutu, Ronald A.; Starman, LaVern

    2015-02-01

    There are numerous applications for micromirror arrays seen in our everyday lives. From flat screen televisions and computer monitors, found in nearly every home and office, to advanced military weapon systems and space vehicles, each application bringing with it a unique set of requirements. The microelectromechanical systems (MEMS) industry has researched many ways micromirror actuation can be accomplished and the different constraints on performance each design brings with it. This paper investigates a new "zipper" approach to electrostatically driven micromirrors with the intent of improving duel plane beam steering by coupling large deflection angles, over 30°, and a fast switching speed. To accomplish this, an extreme initial deflection is needed which can be reached using high stress bimorph beams. Currently this requires long beams and high voltage for the electrostatic pull in or slower electrothermal switching. The idea for this "zipper" approach is to stack multiple beams of a much shorter length and allow for the deflection of each beam to be added together in order to reach the required initial deflection height. This design requires much less pull-in voltage because the pull-in of one short beam will in turn reduce the height of the all subsequent beams, making it much easier to actuate. Using modeling and simulation software to characterize operations characteristics, different bimorph cantilever beam configurations are explored in order to optimize the design. These simulations show that this new "zipper" approach increases initial deflection as additional beams are added to the assembly without increasing the actuation voltage.

  8. Numerical modeling of tokamak breakdown phase driven by pure Ohmic heating under ideal conditions

    Science.gov (United States)

    Jiang, Wei; Peng, Yanli; Zhang, Ya; Lapenta, Giovanni

    2016-12-01

    We have simulated tokamak breakdown phase driven by pure Ohmic heating with implicit particle in cell/Monte Carlo collision (PIC/MCC) method. We have found two modes can be differentiated. When performing breakdown at low initial gas pressure, we find that it works at lower density and current, but higher temperature, and requires lower heating power, compared to when having a high initial pressure. Further, two stages can be distinguished during the avalanche process. One is the fast avalanche stage, in which the plasma is heated by induced toroidal electric field. The other is the slow avalanche stage, which begins when the plasma density reaches 1015 m-3. It has been shown that ions are mainly heated by ambipolar field and become stochastic in the velocity distribution. However, when the induced electric field is low, there exists a transition phase between the two stages. Our model simulates the breakdown and early hydrogen burn-through under ideal conditions during tokamak start-up. It adopted fewer assumptions, and can give an idealized range of operative parameters for Ohmic start-up. Qualitatively, the results agree well with certain experimental observations.

  9. Population reversal driven by unrestrained interactions in molecular dynamics simulations: A dialanine model

    Directory of Open Access Journals (Sweden)

    Filippo Pullara

    2015-10-01

    Full Text Available Standard Molecular Dynamics simulations (MD are usually performed under periodic boundary conditions using the well-established “Ewald summation”. This implies that the distance among each element in a given lattice cell and its corresponding element in another cell, as well as their relative orientations, are constant. Consequently, protein-protein interactions between proteins in different cells—important in many biological activities, such as protein cooperativity and physiological/pathological aggregation—are severely restricted, and features driven by protein-protein interactions are lost. The consequences of these restrictions, although conceptually understood and mentioned in the literature, have not been quantitatively studied before. The effect of protein-protein interactions on the free energy landscape of a model system, dialanine, is presented. This simple system features a free energy diagram with well-separated minima. It is found that, in the case of absence of peptide-peptide (p-p interactions, the ψ = 150° dihedral angle determines the most energetically favored conformation (global free-energy minimum. When strong p-p interactions are induced, the global minimum switches to the ψ = 0° conformation. This shows that the free-energy landscape of an individual molecule is dramatically affected by the presence of other freely interacting molecules of its same type. Results of the study suggest how taking into account p-p interactions in MD allows having a more realistic picture of system activity and functional conformations.

  10. Data-driven interdisciplinary mathematical modelling quantitatively unveils competition dynamics of co-circulating influenza strains.

    Science.gov (United States)

    Ho, Bin-Shenq; Chao, Kun-Mao

    2017-07-28

    Co-circulation of influenza strains is common to seasonal epidemics and pandemic emergence. Competition was considered involved in the vicissitudes of co-circulating influenza strains but never quantitatively studied at the human population level. The main purpose of the study was to explore the competition dynamics of co-circulating influenza strains in a quantitative way. We constructed a heterogeneous dynamic transmission model and ran the model to fit the weekly A/H1N1 influenza virus isolation rate through an influenza season. The construction process started on the 2007-2008 single-clade influenza season and, with the contribution from the clade-based A/H1N1 epidemiological curves, advanced to the 2008-2009 two-clade influenza season. Pearson method was used to estimate the correlation coefficient between the simulated epidemic curve and the observed weekly A/H1N1 influenza virus isolation rate curve. The model found the potentially best-fit simulation with correlation coefficient up to 96% and all the successful simulations converging to the best-fit. The annual effective reproductive number of each co-circulating influenza strain was estimated. We found that, during the 2008-2009 influenza season, the annual effective reproductive number of the succeeding A/H1N1 clade 2B-2, carrying H275Y mutation in the neuraminidase, was estimated around 1.65. As to the preceding A/H1N1 clade 2C-2, the annual effective reproductive number would originally be equivalent to 1.65 but finally took on around 0.75 after the emergence of clade 2B-2. The model reported that clade 2B-2 outcompeted for the 2008-2009 influenza season mainly because clade 2C-2 suffered from a reduction of transmission fitness of around 71% on encountering the former. We conclude that interdisciplinary data-driven mathematical modelling could bring to light the transmission dynamics of the A/H1N1 H275Y strains during the 2007-2009 influenza seasons worldwide and may inspire us to tackle the

  11. Lithospheric-scale effects of a subduction-driven Alboran plate: improved neotectonic modeling

    Science.gov (United States)

    Neres, Marta; Carafa, Michele; Terrinha, Pedro; Fernandes, Rui; Matias, Luis; Duarte, João; Barba, Salvatore

    2016-04-01

    The presence of a subducted slab under the Gibraltar arc is now widely accepted. However, discussion still remains on whether subduction is active and what is its influence in the lithospheric processes, in particular in the observed geodesy, deformation rates and seismicity. Aiming at bringing new insights into the discussion, we have performed a neotectonic numerical study of a segment of the Africa-Eurasia plate boundary, from the Gloria fault to the Northern Algerian margin. Specifically, we have tested the effect of including or excluding an independently driven Alboran plate, i.e. testing active subduction versus inactive subduction (2plates versus 3plates scenarios). We used the dynamic code SHELLS (Bird et al., 2008) to model the surface velocity field and the ongoing deformation, using a new up-to-date simplified tectonic map of the region, new available lithospheric data and boundary conditions determined from two alternative Africa-Eurasia angular velocities, respectively: SEGAL2013, a new pole based on stable Africa and stable Eurasia gps data (last decades); and MORVEL, a geological-scale pole (3.16 Ma). We also extensively studied the variation within the parametric space of fault friction coefficient, subduction resistance and surface velocities imposed to the Alboran plate. The final run comprised a total of 5240 experiments, and each generated model was scored against geodetic velocities, stress direction data and seismic strain rates. The preferred model corresponds to the 3plates scenario, SEGAL2013 pole and fault friction of 0.225, with scoring results: gps misfit of 0.78 mm/yr; SHmax misfit of 13.6° and correlation with seismic strain rate of 0.62, significantly better than previous models. We present predicted fault slip rates for the recognized active structures and off-faults permanent strain rates, which can be used for seismic and tsunami hazard calculations (the initial motivation for this work was contributing for calculation of

  12. Data-Driven Neural Network Model for Robust Reconstruction of Automobile Casting

    Science.gov (United States)

    Lin, Jinhua; Wang, Yanjie; Li, Xin; Wang, Lu

    2017-09-01

    In computer vision system, it is a challenging task to robustly reconstruct complex 3D geometries of automobile castings. However, 3D scanning data is usually interfered by noises, the scanning resolution is low, these effects normally lead to incomplete matching and drift phenomenon. In order to solve these problems, a data-driven local geometric learning model is proposed to achieve robust reconstruction of automobile casting. In order to relieve the interference of sensor noise and to be compatible with incomplete scanning data, a 3D convolution neural network is established to match the local geometric features of automobile casting. The proposed neural network combines the geometric feature representation with the correlation metric function to robustly match the local correspondence. We use the truncated distance field(TDF) around the key point to represent the 3D surface of casting geometry, so that the model can be directly embedded into the 3D space to learn the geometric feature representation; Finally, the training labels is automatically generated for depth learning based on the existing RGB-D reconstruction algorithm, which accesses to the same global key matching descriptor. The experimental results show that the matching accuracy of our network is 92.2% for automobile castings, the closed loop rate is about 74.0% when the matching tolerance threshold τ is 0.2. The matching descriptors performed well and retained 81.6% matching accuracy at 95% closed loop. For the sparse geometric castings with initial matching failure, the 3D matching object can be reconstructed robustly by training the key descriptors. Our method performs 3D reconstruction robustly for complex automobile castings.

  13. A satellite-driven, client-server hydro-economic model prototype for agricultural water management

    Science.gov (United States)

    Maneta, Marco; Kimball, John; He, Mingzhu; Payton Gardner, W.

    2017-04-01

    Anticipating agricultural water demand, land reallocation, and impact on farm revenues associated with different policy or climate constraints is a challenge for water managers and for policy makers. While current integrated decision support systems based on programming methods provide estimates of farmer reaction to external constraints, they have important shortcomings such as the high cost of data collection surveys necessary to calibrate the model, biases associated with inadequate farm sampling, infrequent model updates and recalibration, model overfitting, or their deterministic nature, among other problems. In addition, the administration of water supplies and the generation of policies that promote sustainable agricultural regions depend on more than one bureau or office. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. To overcome these limitations, we present a client-server, integrated hydro-economic modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks. The core of the framework is a stochastic data assimilation system that sequentially ingests remote sensing observations and corrects the parameters of the hydro-economic model at unprecedented spatial and temporal resolutions. An economic model of agricultural production, based on mathematical programming, requires information on crop type and extent, crop yield, crop transpiration and irrigation technology. A regional hydro-climatologic model provides biophysical constraints to an economic model of agricultural production with a level of detail that permits the study of the spatial impact of large- and small-scale water use decisions. Crop type and extent is obtained from the Cropland Data Layer (CDL), which is multi-sensor operational classification of crops maintained by the United States Department of Agriculture. Because

  14. Evaluation of global water quality - the potential of a data- and model-driven analysis

    Science.gov (United States)

    Bärlund, Ilona; Flörke, Martina; Alcamo, Joseph; Völker, Jeanette; Malsy, Marcus; Kaus, Andrew; Reder, Klara; Büttner, Olaf; Katterfeld, Christiane; Dietrich, Désirée; Borchardt, Dietrich

    2016-04-01

    The ongoing socio-economic development presents a new challenge for water quality worldwide, especially in developing and emerging countries. It is estimated that due to population growth and the extension of water supply networks, the amount of waste water will rise sharply. This can lead to an increased risk of surface water quality degradation, if the wastewater is not sufficiently treated. This development has impacts on ecosystems and human health, as well as food security. The United Nations Member States have adopted targets for sustainable development. They include, inter alia, sustainable protection of water quality and sustainable use of water resources. To achieve these goals, appropriate monitoring strategies and the development of indicators for water quality are required. Within the pre-study for a 'World Water Quality Assessment' (WWQA) led by United Nations Environment Programme (UNEP), a methodology for assessing water quality, taking into account the above-mentioned objectives has been developed. The novelty of this methodology is the linked model- and data-driven approach. The focus is on parameters reflecting the key water quality issues, such as increased waste water pollution, salinization or eutrophication. The results from the pre-study show, for example, that already about one seventh of all watercourses in Latin America, Africa and Asia show high organic pollution. This is of central importance for inland fisheries and associated food security. In addition, it could be demonstrated that global water quality databases have large gaps. These must be closed in the future in order to obtain an overall picture of global water quality and to target measures more efficiently. The aim of this presentation is to introduce the methodology developed within the WWQA pre-study and to show selected examples of application in Latin America, Africa and Asia.

  15. Results of Formal Evaluation of a Data and Modeling Driven Hydrology Learning Module

    Science.gov (United States)

    Ruddell, B. L.; Sanchez, C. A.; Schiesser, R.; Merwade, V.

    2014-12-01

    New hydrologists should not only develop a well-defined knowledgebase of basic hydrological concepts, but also synthesize this factual learning with more authentic 'real-world' knowledge gained from the interpretation and analysis of data from hydrological models (Merwade and Ruddell, 2012, Wagener et al., 2007). However, hydrological instruction is often implemented using a traditional teacher-centered approach (e.g., lectures) (Wagener, 2007). The emergence of rich and dynamic computer simulation techniques which allow students the opportunity for more authentic application of knowledge (Merwade & Ruddell, 2012). This study evaluates the efficacy of using such data-driven simulations to increase the understanding of the field of hydrology in the lower-division undergraduate geoscience classroom. In this study, 88 students at a local community college who were enrolled in an Introductory Earth Science class were evaluated on their learning performance in a unit on applying the Rational Method to estimate hydrographs and flooding for urban areas. Students were either presented with a data and visualization rich computer module (n=52), or with paper and pencil calculation activities (n=36). All conceptual material presented in lecture was consistent across these two conditions. Students were evaluated for not only changes in their knowledge and application of the concepts within the unit (e.g., effects of urbanization and impervious cover, discharge rates), but also for their broad "T-shaped" profile of professional knowledge and skills. While results showed significant (p<.05) increases from pre to post assessments in all learning areas for both groups, there is a significantly larger benefit for the data module group when it came to (1) understanding the effects of urbanization and impervious cover on flooding, (2) applying consistent vocabulary appropriately within context, and (3) explaining the roles and responsibilities of hydrologists and flood managers.

  16. Model for biological communication in a nanofabricated cell-mimic driven by stochastic resonance

    Energy Technology Data Exchange (ETDEWEB)

    Karig, David K [ORNL; Siuti, Piro [ORNL; Dar, Roy D. [University of Tennessee, Knoxville (UTK); Retterer, Scott T [ORNL; Doktycz, Mitchel John [ORNL; Simpson, Michael L [ORNL

    2011-01-01

    Cells offer natural examples of highly efficient networks of nanomachines. Accordingly, both intracellular and intercellular communication mechanisms in nature are looked to as a source of inspiration and instruction for engineered nanocommunication. Harnessing biological functionality in this manner requires an interdisciplinary approach that integrates systems biology, synthetic biology, and nanofabrication. Recent years have seen the amassing of a tremendous wealth of data from the sequencing of new organisms and from high throughput expression experiments. At the same time, a deeper fundamental understanding of individual cell function has been developed, as exemplified by the growth of fields such as noise biology, which seeks to characterize the role of noise in gene expression. The availability of well characterized biological components coupled with a deeper understanding of cell function has led to efforts to engineer both living cells and to create bio-like functionality in non-living substrates in the field of synthetic biology. Here, we present a model system that exemplifies the synergism between these realms of research. We propose a synthetic gene network for operation in a nanofabricated cell mimic array that propagates a biomolecular signal over long distances using the phenomenon of stochastic resonance. Our system consists of a bacterial quorum sensing signal molecule, a bistable genetic switch triggered by this signal, and an array of nanofabricated cell mimic wells that contain the genetic system. An optimal level of noise in the system helps to propagate a time-varying AHL signal over long distances through the array of mimics. This noise level is determined both by the system volume and by the parameters of the genetic network. Our proposed genetically driven stochastic resonance system serves as a testbed for exploring the potential harnessing of gene expression noise to aid in the transmission of a time-varying molecular signal.

  17. Adaptive Admittance Control for an Ankle Exoskeleton Using an EMG-Driven Musculoskeletal Model

    Directory of Open Access Journals (Sweden)

    Shaowei Yao

    2018-04-01

    Full Text Available Various rehabilitation robots have been employed to recover the motor function of stroke patients. To improve the effect of rehabilitation, robots should promote patient participation and provide compliant assistance. This paper proposes an adaptive admittance control scheme (AACS consisting of an admittance filter, inner position controller, and electromyography (EMG-driven musculoskeletal model (EDMM. The admittance filter generates the subject's intended motion according to the joint torque estimated by the EDMM. The inner position controller tracks the intended motion, and its parameters are adjusted according to the estimated joint stiffness. Eight healthy subjects were instructed to wear the ankle exoskeleton robot, and they completed a series of sinusoidal tracking tasks involving ankle dorsiflexion and plantarflexion. The robot was controlled by the AACS and a non-adaptive admittance control scheme (NAACS at four fixed parameter levels. The tracking performance was evaluated using the jerk value, position error, interaction torque, and EMG levels of the tibialis anterior (TA and gastrocnemius (GAS. For the NAACS, the jerk value and position error increased with the parameter levels, and the interaction torque and EMG levels of the TA tended to decrease. In contrast, the AACS could maintain a moderate jerk value, position error, interaction torque, and TA EMG level. These results demonstrate that the AACS achieves a good tradeoff between accurate tracking and compliant assistance because it can produce a real-time response to stiffness changes in the ankle joint. The AACS can alleviate the conflict between accurate tracking and compliant assistance and has potential for application in robot-assisted rehabilitation.

  18. Experience-driven formation of parts-based representations in a model of layered visual memory

    Directory of Open Access Journals (Sweden)

    Jenia Jitsev

    2009-09-01

    Full Text Available Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.

  19. Jupiter Thermospheric General Circulation Model (JTGCM): Global Structure and Dynamics Driven by Auroral and Joule Heating

    Science.gov (United States)

    Bougher, S. W.; J. Il. Waite, Jr.; Majeed, T.

    2005-01-01

    A growing multispectral database plus recent Galileo descent measurements are being used to construct a self-consistent picture of the Jupiter thermosphere/ionosphere system. The proper characterization of Jupiter s upper atmosphere, embedded ionosphere, and auroral features requires the examination of underlying processes, including the feedbacks of energetics, neutral-ion dynamics, composition, and magnetospheric coupling. A fully 3-D Jupiter Thermospheric General Circulation Model (JTGCM) has been developed and exercised to address global temperatures, three-component neutral winds, and neutral-ion species distributions. The domain of this JTGCM extends from 20-microbar (capturing hydrocarbon cooling) to 1.0 x 10(exp -4) nbar (including aurora/Joule heating processes). The resulting JTGCM has been fully spun-up and integrated for greater than or equal to40 Jupiter rotations. Results from three JTGCM cases incorporating moderate auroral heating, ion drag, and moderate to strong Joule heating processes are presented. The neutral horizontal winds at ionospheric heights vary from 0.5 km/s to 1.2 km/s, atomic hydrogen is transported equatorward, and auroral exospheric temperatures range from approx.1200-1300 K to above 3000 K, depending on the magnitude of Joule heating. The equatorial temperature profiles from the JTGCM are compared with the measured temperature structure from the Galileo AS1 data set. The best fit to the Galileo data implies that the major energy source for maintaining the equatorial temperatures is due to dynamical heating induced by the low-latitude convergence of the high-latitude-driven thermospheric circulation. Overall, the Jupiter thermosphere/ionosphere system is highly variable and is shown to be strongly dependent on magnetospheric coupling which regulates Joule heating.

  20. Data-driven behavioural modelling of residential water consumption to inform water demand management strategies

    Science.gov (United States)

    Giuliani, Matteo; Cominola, Andrea; Alshaf, Ahmad; Castelletti, Andrea; Anda, Martin

    2016-04-01

    The continuous expansion of urban areas worldwide is expected to highly increase residential water demand over the next few years, ultimately challenging the distribution and supply of drinking water. Several studies have recently demonstrated that actions focused only on the water supply side of the problem (e.g., augmenting existing water supply infrastructure) will likely fail to meet future demands, thus calling for the concurrent deployment of effective water demand management strategies (WDMS) to pursue water savings and conservation. However, to be effective WDMS do require a substantial understanding of water consumers' behaviors and consumption patterns at different spatial and temporal resolutions. Retrieving information on users' behaviors, as well as their explanatory and/or causal factors, is key to spot potential areas for targeting water saving efforts and to design user-tailored WDMS, such as education campaigns and personalized recommendations. In this work, we contribute a data-driven approach to identify household water users' consumption behavioural profiles and model their water use habits. State-of-the-art clustering methods are coupled with big data machine learning techniques with the aim of extracting dominant behaviors from a set of water consumption data collected at the household scale. This allows identifying heterogeneous groups of consumers from the studied sample and characterizing them with respect to several consumption features. Our approach is validated onto a real-world household water consumption dataset associated with a variety of demographic and psychographic user data and household attributes, collected in nine towns of the Pilbara and Kimberley Regions of Western Australia. Results show the effectiveness of the proposed method in capturing the influence of candidate determinants on residential water consumption profiles and in attaining sufficiently accurate predictions of users' consumption behaviors, ultimately providing

  1. Modelling the possible interaction between edge-driven convection and the Canary Islands mantle plume

    Science.gov (United States)

    Negredo, A. M.; Rodríguez-González, J.; Fullea, J.; Van Hunen, J.

    2017-12-01

    The close location between many hotspots and the edges of cratonic lithosphere has led to the hypothesis that these hotspots could be explained by small-scale mantle convection at the edge of cratons (Edge Driven Convection, EDC). The Canary Volcanic Province hotspot represents a paradigmatic example of this situation due to its close location to the NW edge of the African Craton. Geochemical evidence, prominent low seismic velocity anomalies in the upper and lower mantle, and the rough NE-SW age-progression of volcanic centers consistently point out to a deep-seated mantle plume as the origin of the Canary Volcanic Province. It has been hypothesized that the plume material could be affected by upper mantle convection caused by the thermal contrast between thin oceanic lithosphere and thick (cold) African craton. Deflection of upwelling blobs due to convection currents would be responsible for the broader and more irregular pattern of volcanism in the Canary Province compared to the Madeira Province. In this study we design a model setup inspired on this scenario to investigate the consequences of possible interaction between ascending mantle plumes and EDC. The Finite Element code ASPECT is used to solve convection in a 2D box. The compositional field and melt fraction distribution are also computed. Free slip along all boundaries and constant temperature at top and bottom boundaries are assumed. The initial temperature distribution assumes a small long-wavelength perturbation. The viscosity structure is based on a thick cratonic lithosphere progressively varying to a thin, or initially inexistent, oceanic lithosphere. The effects of assuming different rheologies, as well as steep or gradual changes in lithospheric thickness are tested. Modelling results show that a very thin oceanic lithosphere (< 30 km) is needed to generate partial melting by EDC. In this case partial melting can occur as far as 700 km away from the edge of the craton. The size of EDC cells is

  2. Flow Characteristics of Lid-Driven Cavities with Particle Suspensions using an Eulerian-Lagrangian Modeling Approach

    Science.gov (United States)

    Adesemowo, Morakinyo; Shelton, John

    2016-11-01

    Previous experimental and numerical investigations involving lid-driven cavity flows with particle suspensions have primarily focused on particle tracking and the visualization of complex three-dimensional structures that compose the flow field. However, these particle suspensions and their resulting particle-particle interactions could also be viewed as a system of time-dependent perturbation equations to the steady-state Navier-Stokes equations and could affect both the stability and steady-state characteristics of the two-dimensional lid-driven cavity system. In this investigation, an Eulerian-Lagrangian approach to modeling particle suspensions in the lid-driven cavity is utilized in FV-CFD simulations to investigate the effect particle density, area fraction, and Reynolds number have on the two-dimensional flow characteristics of a laminar fluid. Observations have indicated that the development of the primary vortex in the lid-driven cavity varies according to the area fraction of particle suspensions in the system; transitioning from development via an adverse pressure gradient at the top-right corner of the cavity towards particle-laden behavior where particle-particle interactions dominate the development of the primary vortex. Dynamic responses were also observed for particle systems of less dense particles. Finally, a comparison between flows perturbed using disturbance velocities and from particle interactions was performed.

  3. Modelling and Analysis of Radial Flux Surface Mounted Direct-Driven PMSG in Small Scale Wind Turbine

    Directory of Open Access Journals (Sweden)

    Theint Zar Htet

    2017-11-01

    Full Text Available This paper presents the modelling and analysis of permanent magnet synchronous generator (PMSG which are used in direct driven small scale wind turbines. The 3 kW PM generator which is driven directly without gear system is analyzed by Ansoft Maxwell 2D RMxprt. The performance analysis of generator includes the cogging torque in two teeth, induced coil voltages under load, winding current under load, airgap flux density distribution and so on. The modelling analysis is based on the 2D finite element techniques. In an electrical machine, an accurate determination of the geometry parameters is a vital role. The proper performance results of 3kW PMSG in small scale wind turbine can be seen in this paper.

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

    Science.gov (United States)

    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%.

  5. Dynamic and Oscillatory Motions of Cable-Driven Parallel Robots Based on a Nonlinear Cable Tension Model

    OpenAIRE

    Baklouti , Sana; Courteille , Eric; Caro , Stéphane; DKHIL , Mohamed

    2017-01-01

    International audience; In this paper, dynamic modeling of cable-driven parallel robots (CDPRs) is addressed where each cable length is subjected to variations during operation. It is focusing on an original formulation of cable tension, which reveals a softening behavior when strains become large. The dynamic modulus of cable elasticity is experimentally identified through Dynamic Mechanical Analysis (DMA). Numerical investigations carried out on suspended CDPRs with different sizes show the...

  6. An EMG-driven biomechanical model that accounts for the decrease in moment generation capacity during a dynamic fatigued condition.

    Science.gov (United States)

    Rao, Guillaume; Berton, Eric; Amarantini, David; Vigouroux, Laurent; Buchanan, Thomas S

    2010-07-01

    Although it is well known that fatigue can greatly reduce muscle forces, it is not generally included in biomechanical models. The aim of the present study was to develop an electromyographic-driven (EMG-driven) biomechanical model to estimate the contributions of flexor and extensor muscle groups to the net joint moment during a nonisokinetic functional movement (squat exercise) performed in nonfatigued and in fatigued conditions. A methodology that aims at balancing the decreased muscle moment production capacity following fatigue was developed. During an isometric fatigue session, a linear regression was created linking the decrease in force production capacity of the muscle (normalized force/EMG ratio) to the EMG mean frequency. Using the decrease in mean frequency estimated through wavelet transforms between dynamic squats performed before and after the fatigue session as input to the previous linear regression, a coefficient accounting for the presence of fatigue in the quadriceps group was computed. This coefficient was used to constrain the moment production capacity of the fatigued muscle group within an EMG-driven optimization model dedicated to estimate the contributions of the knee flexor and extensor muscle groups to the net joint moment. During squats, our results showed significant increases in the EMG amplitudes with fatigue (+23.27% in average) while the outputs of the EMG-driven model were similar. The modifications of the EMG amplitudes following fatigue were successfully taken into account while estimating the contributions of the flexor and extensor muscle groups to the net joint moment. These results demonstrated that the new procedure was able to estimate the decrease in moment production capacity of the fatigued muscle group.

  7. A framework for modelling business processes in demand-driven supply chains

    NARCIS (Netherlands)

    Verdouw, C.N.; Beulens, A.J.M.; Trienekens, J.H.; Vorst, van der J.G.A.J.

    2011-01-01

    Demand-driven supply chains are highly dynamic networks of different participants with different allocations of business processes and different modes of control and coordination. Companies must be able to take part in multiple supply chain configurations concurrently and to switch rapidly to new or

  8. First Principles Modeling of the Performance of a Hydrogen-Peroxide-Driven Chem-E-Car

    Science.gov (United States)

    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…

  9. Established Models and New Paradigms for Hypoxia-Driven Cancer-Associated Bone Disease

    DEFF Research Database (Denmark)

    Cox, Thomas R.; Erler, Janine T.; Rumney, Robin M.H.

    2018-01-01

    -driven effects in the primary tumour are wide ranging including changes in gene expression, dysregulation of signalling pathways, resistance to chemotherapy, neovascularisation, increased tumour cell proliferation and migration. Paget’s seed and soil theory states that for a metastasising tumour cell ‘the seed...

  10. A Demand-driven Model of Decentralised Land-use Planning and ...

    African Journals Online (AJOL)

    sulaiman.adebowale

    2006-08-25

    Aug 25, 2006 ... result in more thoroughgoing empowerment if it is demand-driven. Even if empowerment is demanded we note that relations in decentralised arenas are not necessarily always egalitarian. We therefore argue that efforts to resolve the dilemma of community marginalisation through decentralisation should ...

  11. Inflammation-driven bone formation in a mouse model of ankylosing spondylitis: sequential not parallel processes.

    Science.gov (United States)

    Tseng, Hsu-Wen; Pitt, Miranda E; Glant, Tibor T; McRae, Allan F; Kenna, Tony J; Brown, Matthew A; Pettit, Allison R; Thomas, Gethin P

    2016-01-29

    Ankylosing spondylitis (AS) is an immune-mediated arthritis particularly targeting the spine and pelvis and is characterised by inflammation, osteoproliferation and frequently ankylosis. Current treatments that predominately target inflammatory pathways have disappointing efficacy in slowing disease progression. Thus, a better understanding of the causal association and pathological progression from inflammation to bone formation, particularly whether inflammation directly initiates osteoproliferation, is required. The proteoglycan-induced spondylitis (PGISp) mouse model of AS was used to histopathologically map the progressive axial disease events, assess molecular changes during disease progression and define disease progression using unbiased clustering of semi-quantitative histology. PGISp mice were followed over a 24-week time course. Spinal disease was assessed using a novel semi-quantitative histological scoring system that independently evaluated the breadth of pathological features associated with PGISp axial disease, including inflammation, joint destruction and excessive tissue formation (osteoproliferation). Matrix components were identified using immunohistochemistry. Disease initiated with inflammation at the periphery of the intervertebral disc (IVD) adjacent to the longitudinal ligament, reminiscent of enthesitis, and was associated with upregulated tumor necrosis factor and metalloproteinases. After a lag phase, established inflammation was temporospatially associated with destruction of IVDs, cartilage and bone. At later time points, advanced disease was characterised by substantially reduced inflammation, excessive tissue formation and ectopic chondrocyte expansion. These distinct features differentiated affected mice into early, intermediate and advanced disease stages. Excessive tissue formation was observed in vertebral joints only if the IVD was destroyed as a consequence of the early inflammation. Ectopic excessive tissue was predominantly

  12. An Updated Subsequent Injury Categorisation Model (SIC-2.0): Data-Driven Categorisation of Subsequent Injuries in Sport.

    Science.gov (United States)

    Toohey, Liam A; Drew, Michael K; Fortington, Lauren V; Finch, Caroline F; Cook, Jill L

    2018-03-03

    Accounting for subsequent injuries is critical for sports injury epidemiology. The subsequent injury categorisation (SIC-1.0) model was developed to create a framework for accurate categorisation of subsequent injuries but its operationalisation has been challenging. The objective of this study was to update the subsequent injury categorisation (SIC-1.0 to SIC-2.0) model to improve its utility and application to sports injury datasets, and to test its applicability to a sports injury dataset. The SIC-1.0 model was expanded to include two levels of categorisation describing how previous injuries relate to subsequent events. A data-driven classification level was established containing eight discrete injury categories identifiable without clinical input. A sequential classification level that sub-categorised the data-driven categories according to their level of clinical relatedness has 16 distinct subsequent injury types. Manual and automated SIC-2.0 model categorisation were applied to a prospective injury dataset collected for elite rugby sevens players over a 2-year period. Absolute agreement between the two coding methods was assessed. An automated script for automatic data-driven categorisation and a flowchart for manual coding were developed for the SIC-2.0 model. The SIC-2.0 model was applied to 246 injuries sustained by 55 players (median four injuries, range 1-12), 46 (83.6%) of whom experienced more than one injury. The majority of subsequent injuries (78.7%) were sustained to a different site and were of a different nature. Absolute agreement between the manual coding and automated statistical script category allocation was 100%. The updated SIC-2.0 model provides a simple flowchart and automated electronic script to allow both an accurate and efficient method of categorising subsequent injury data in sport.

  13. Better Equipping Reserve Military Intelligence Analyst to Meet the Needs of the Commander by Championing a Process-Driven Training Model

    Science.gov (United States)

    2013-06-14

    BETTER EQUIPPING RESERVE MILITARY INTELLIGENCE ANALYST TO MEET THE NEEDS OF...TITLE AND SUBTITLE Better Equipping Reserve Military Intelligence Analyst To Meet The Needs Of The Commander by Championing A Process-Driven Training...Analyst to Meet the Needs of the Commander by Championing a Process-Driven Training Model Approved by: , Thesis Committee Chair Jack D. Kem, Ph.D

  14. The extended distributed microstructure model for gradient-driven transport: A two-scale model for bypassing effective parameters

    Science.gov (United States)

    Carr, E. J.; Perré, P.; Turner, I. W.

    2016-12-01

    Numerous problems involving gradient-driven transport processes-e.g., Fourier's and Darcy's law-in heterogeneous materials concern a physical domain that is much larger than the scale at which the coefficients vary spatially. To overcome the prohibitive computational cost associated with such problems, the well-established Distributed Microstructure Model (DMM) provides a two-scale description of the transport process that produces a computationally cheap approximation to the fine-scale solution. This is achieved via the introduction of sparsely distributed micro-cells that together resolve small patches of the fine-scale structure: a macroscopic equation with an effective coefficient describes the global transport and a microscopic equation governs the local transport within each micro-cell. In this paper, we propose a new formulation, the Extended Distributed Microstructure Model (EDMM), where the macroscopic flux is instead defined as the average of the microscopic fluxes within the micro-cells. This avoids the need for any effective parameters and more accurately accounts for a non-equilibrium field in the micro-cells. Another important contribution of the work is the presentation of a new and improved numerical scheme for performing the two-scale computations using control volume, Krylov subspace and parallel computing techniques. Numerical tests are carried out on two challenging test problems: heat conduction in a composite medium and unsaturated water flow in heterogeneous soils. The results indicate that while DMM is more efficient, EDMM is more accurate and is able to capture additional fine-scale features in the solution.

  15. Generalized Boolean logic Driven Markov Processes: A powerful modeling framework for Model-Based Safety Analysis of dynamic repairable and reconfigurable systems

    International Nuclear Information System (INIS)

    Piriou, Pierre-Yves; Faure, Jean-Marc; Lesage, Jean-Jacques

    2017-01-01

    This paper presents a modeling framework that permits to describe in an integrated manner the structure of the critical system to analyze, by using an enriched fault tree, the dysfunctional behavior of its components, by means of Markov processes, and the reconfiguration strategies that have been planned to ensure safety and availability, with Moore machines. This framework has been developed from BDMP (Boolean logic Driven Markov Processes), a previous framework for dynamic repairable systems. First, the contribution is motivated by pinpointing the limitations of BDMP to model complex reconfiguration strategies and the failures of the control of these strategies. The syntax and semantics of GBDMP (Generalized Boolean logic Driven Markov Processes) are then formally defined; in particular, an algorithm to analyze the dynamic behavior of a GBDMP model is developed. The modeling capabilities of this framework are illustrated on three representative examples. Last, qualitative and quantitative analysis of GDBMP models highlight the benefits of the approach.

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

    Science.gov (United States)

    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.

  17. Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem

    Science.gov (United States)

    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.

  18. Correlation-Driven Lifshitz Transition at the Emergence of the Pseudogap Phase in the Two-Dimensional Hubbard Model

    Science.gov (United States)

    Bragança, Helena; Sakai, Shiro; Aguiar, M. C. O.; Civelli, Marcello

    2018-02-01

    We study the relationship between the pseudogap and Fermi-surface topology in the two-dimensional Hubbard model by means of the cellular dynamical mean-field theory. We find two possible mean-field metallic solutions on a broad range of interactions, doping, and frustration: a conventional renormalized metal and an unconventional pseudogap metal. At half filling, the conventional metal is more stable and displays an interaction-driven Mott metal-insulator transition. However, for large interactions and small doping, a region that is relevant for cuprates, the pseudogap phase becomes the ground state. By increasing doping, we show that a first-order transition from the pseudogap to the conventional metal is tied to a change of the Fermi surface from hole- to electronlike, unveiling a correlation-driven mechanism for a Lifshitz transition. This explains the puzzling link between the pseudogap phase and Fermi surface topology that has been pointed out in recent experiments.

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

    Science.gov (United States)

    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.

  20. [Kinematics Modeling and Analysis of Central-driven Robot for Upper Limb Rehabilitation after Stroke].

    Science.gov (United States)

    Yi, Jinhua; Yu, Hongliu; Zhang, Ying; Hu, Xin; Shi, Ping

    2015-12-01

    The present paper proposed a central-driven structure of upper limb rehabilitation robot in order to reduce the volume of the robotic arm in the structure, and also to reduce the influence of motor noise, radiation and other adverse factors on upper limb dysfunction patient. The forward and inverse kinematics equations have been obtained with using the Denavit-Hartenberg (D-H) parameter method. The motion simulation has been done to obtain the angle-time curve of each joint and the position-time curve of handle under setting rehabilitation path by using Solid Works software. Experimental results showed that the rationality with the central-driven structure design had been verified by the fact that the handle could move under setting rehabilitation path. The effectiveness of kinematics equations had been proved, and the error was less than 3° by comparing the angle-time curves obtained from calculation with those from motion simulation.

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

    OpenAIRE

    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 ...

  2. Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets

    OpenAIRE

    Brors Benedikt; Czwan Esteban; Kipling David

    2010-01-01

    Abstract Background Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the them...

  3. Modelling perceptions of criminality and remorse from faces using a data-driven computational approach.

    OpenAIRE

    Funk, Friederike; Walker, Mirella; Todorov, Alexander

    2016-01-01

    Perceptions of criminality and remorse are critical for legal decision-making. While faces perceived as criminal are more likely to be selected in police lineups and to receive guilty verdicts, faces perceived as remorseful are more likely to receive less severe punishment recommendations. To identify the information that makes a face appear criminal and/or remorseful, we successfully used two different data-driven computational approaches that led to convergent findings: one relying on the u...

  4. A new costing model in hospital management: time-driven activity-based costing system.

    Science.gov (United States)

    Öker, Figen; Özyapıcı, Hasan

    2013-01-01

    Traditional cost systems cause cost distortions because they cannot meet the requirements of today's businesses. Therefore, a new and more effective cost system is needed. Consequently, time-driven activity-based costing system has emerged. The unit cost of supplying capacity and the time needed to perform an activity are the only 2 factors considered by the system. Furthermore, this system determines unused capacity by considering practical capacity. The purpose of this article is to emphasize the efficiency of the time-driven activity-based costing system and to display how it can be applied in a health care institution. A case study was conducted in a private hospital in Cyprus. Interviews and direct observations were used to collect the data. The case study revealed that the cost of unused capacity is allocated to both open and laparoscopic (closed) surgeries. Thus, by using the time-driven activity-based costing system, managers should eliminate the cost of unused capacity so as to obtain better results. Based on the results of the study, hospital management is better able to understand the costs of different surgeries. In addition, managers can easily notice the cost of unused capacity and decide how many employees to be dismissed or directed to other productive areas.

  5. Data-Driven Nonlinear Subspace Modeling for Prediction and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Ping; Song, Heda; Wang, Hong; Chai, Tianyou

    2017-09-01

    Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multi-phase and multi-field coupling and large time delay occur during its operation. In BF operation, the molten iron temperature (MIT) as well as Si, P and S contents of molten iron are the most essential molten iron quality (MIQ) indices, whose measurement, modeling and control have always been important issues in metallurgic engineering and automation field. This paper develops a novel data-driven nonlinear state space modeling for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques. First, to improve modeling efficiency, a data-driven hybrid method combining canonical correlation analysis and correlation analysis is proposed to identify the most influential controllable variables as the modeling inputs from multitudinous factors would affect the MIQ indices. Then, a Hammerstein model for the prediction of MIQ indices is established using the LS-SVM based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method to fit the complex nonlinear kernel function. Compared to the original Hammerstein model, this simplified model can not only significantly reduce the computational complexity, but also has almost the same reliability and accuracy for a stable prediction of MIQ indices. Last, in order to verify the practicability of the developed model, it is applied in designing a genetic algorithm based nonlinear predictive controller for multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approach.

  6. Arsenic removal by solar-driven membrane distillation: modeling and experimental investigation with a new flash vaporization module.

    Science.gov (United States)

    Pa, Parimal; Manna, Ajay Kumar; Linnanen, Lassi

    2013-01-01

    A modeling and simulation study was carried out on a new flux-enhancing and solar-driven membrane distillation module for removal of arsenic from contaminated groundwater. The developed new model was validated with rigorous experimental investigations using arsenic-contaminated groundwater. By incorporating flash vaporization dynamics, the model turned out to be substantially different from the existing direct contact membrane distillation models and could successfully predict (with relative error of only 0.042 and a Willmott d-index of 0.997) the performance of such an arsenic removal unit where the existing models exhibited wide variation with experimental findings in the new design. The module with greater than 99% arsenic removal efficiency and greater than 50 L/m2 x h flux could be implemented in arsenic-affected villages in Southeast Asian countries with abundant solar energy, and thus could give relief to millions of affected people. These encouraging results will raise scale-up confidence.

  7. Mathematical Models of Feedback Systems for Control of Intra-Bunch Instabilities Driven by E-Clouds and TMCI

    CERN Document Server

    Rivetta, C H; Mastoridis, T; Pivi, M T F; Turgut, O; Höfle, W; Secondo, R; Vay, J L

    2011-01-01

    The feedback control of intrabunch instabilities driven by E-Clouds or strong head-tail coupling (TMCI) requires sufficient bandwidth to sense the vertical position and drive multiple sections of a nanosecond scale bunch. These requirements impose challenges and limits in the design and implementation of the feedback system. This paper presents models for the feedback subsystems: receiver, processing channel, amplifier and kicker, that take into account their frequency response and limits. These models are included in multiparticle simulation codes (WARP/CMAD/Head-Tail) and reduced mathematical models of the bunch dynamics to evaluate the impact of subsystem limitations in the bunch stabilization and emittance improvement. With this realistic model of the hardware, it is possible to analyze and design the feedback system. This research is crucial to evaluate the performance boundary of the feedback control system due to cost and technological limitations. These models define the impact of spurious perturbatio...

  8. Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering

    Directory of Open Access Journals (Sweden)

    C Sangavai

    2017-12-01

    Full Text Available 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. Keywords: Biofuel, Amino acid catabolism, Genome-scale model, Metabolic engineering, Systems biology, ABE fermentation, Clostridium sticklandii

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

    Science.gov (United States)

    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.

  10. A patient-specific EMG-driven neuromuscular model for the potential use of human-inspired gait rehabilitation robots.

    Science.gov (United States)

    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.

  11. A neuromusculoskeletal model of the human lower limb: towards EMG-driven actuation of multiple joints in powered orthoses.

    Science.gov (United States)

    Sartori, Massimo; Reggiani, Monica; Lloyd, David G; Pagello, Enrico

    2011-01-01

    This paper presents a novel neuromusculoskeletal (NMS) model of the human lower limb that uses the electromyo-graphic (EMG) signals from 16 muscles to estimate forces generated by 34 musculotendon actuators and the resulting joint moments at the hip, knee and ankle joints during varied contractile conditions. Our proposed methodology allows overcoming limitations on force computation shown by currently available NMS models, which constrain the operation of muscles to satisfy joint moments about one single degree of freedom (DOF) only (i.e. knee flexion-extension). The design of advanced human machine interfaces can benefit from the application of our proposed multi-DOF NMS model. The better estimates of the human internal state it provides with respect to single-DOF NMS models, will allow designing more intuitive human-machine interfaces for the simultaneous EMG-driven actuation of multiple joints in lower limb powered orthoses. © 2011 IEEE

  12. (I) A Declarative Framework for ERP Systems(II) Reactors: A Data-Driven Programming Model for Distributed Applications

    DEFF Research Database (Denmark)

    Stefansen, Christian Oskar Erik

    , namely the general ledger and accounts receivable. The result is an event-based approach to designing ERP systems and an abstract-level sketch of the architecture. • Compositional Specification of Commercial Contracts. The paper describes the design, multiple semantics, and use of a domain...... on the idea of soft constraints the paper explains the design, semantics, and use of a language for allocating work in business processes. The language lets process designers express both hard constraints and soft constraints. (II) The Reactors programming model: • Reactors: A Data-Oriented Synchronous....../Asynchronous Programming Model for Distributed Applications. The paper motivates, explains, and defines a distributed data-driven programming model. In the model a reactor is a stateful unit of distribution. A reactor specifies constructive, declarative constraints on its data and the data of other reactors in the style...

  13. Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets.

    Science.gov (United States)

    Czwan, Esteban; Brors, Benedikt; Kipling, David

    2010-01-11

    Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets). Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis) is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis. To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study. When applied to two public cancer datasets our technique revealed novel ontological categories with prognostic power, including significant correlations between "fatty acid metabolism" with overall survival in breast cancer, as well as "receptor mediated endocytosis", "brain development", "apical plasma membrane" and "MAPK signaling pathway" with overall survival in lung cancer. Current methods of theme-driven survival studies assume uniformity of p-values for

  14. Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets

    Directory of Open Access Journals (Sweden)

    Brors Benedikt

    2010-01-01

    Full Text Available Abstract Background Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test. The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets. Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis. Results To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study. When applied to two public cancer datasets our technique revealed novel ontological categories with prognostic power, including significant correlations between "fatty acid metabolism" with overall survival in breast cancer, as well as "receptor mediated endocytosis", "brain development", "apical plasma membrane" and "MAPK signaling pathway" with overall survival in lung cancer. Conclusions Current methods of theme-driven

  15. Direct Numerical Modeling of E-Cloud Driven Instability of a Bunch Train in the CERN SPS

    CERN Document Server

    Vay, J-L; Furman, M A

    2011-01-01

    The simulation package WARP-POSINST was recently upgraded for handling multiple bunches and modeling concurrently the electron cloud buildup and its effect on the beam, allowing for direct self-consistent simulation of bunch trains generating, and interacting with, electron clouds. We have used the WARP-POSINST package on massively parallel supercomputers to study the buildup and interaction of electron clouds with a proton bunch train in the CERN SPS accelerator. Results suggest that a positive feedback mechanism exists between the electron buildup and the e-cloud driven transverse instability, leading to a net increase in predicted electron density.

  16. Dynamic model and workspace analysis of novel incompletely restrained cable-suspension swing system driven by two cables

    Directory of Open Access Journals (Sweden)

    Naige Wang

    2017-03-01

    Full Text Available The incompletely restrained cable-suspension swing system driven by two cables is introduced in this article. Based on wrench of forces theory and Lagrange’s equation of first kind, the static and dynamics models of incompletely restrained cable-suspension swing system driven by two cables are established, respectively. In order to obtain an intuitive understanding of the trajectory analysis, a dynamics model consisting of governing equation and geometric constraint conditions which is a set of the mixed differential-algebraic equation in mathematics is established. A typical feedback controller and an inverse model were set up to estimate the driving function. The effective workspace, which is used to guarantee an efficient swing process, mostly depends on the geometrical shape rather than the volume itself which was calculated by trajectory analysis. In order to estimate system features and ensure a limited range of tension in underconstrained spatial cable system, the probable location of unbalanced loading was evaluated by pointwise evaluation techniques during normal work.

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

    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. 55 refs

  19. Amino acid catabolism-directed biofuel production inClostridium sticklandii:An insight into model-driven systems engineering.

    Science.gov (United States)

    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.

  20. A mathematical model for the motion analysis of embedded straight microcantilevers under a pressure-driven flow

    International Nuclear Information System (INIS)

    Ezkerra, A; Mayora, K; Ruano-López, J M; Wilson, P A

    2008-01-01

    A mathematical model that estimates the deflection of straight microcantilevers embedded in a microchannel under a pressure-driven flow at low Reynolds numbers is presented. The model makes use of the Schwarz–Christoffel mapping in order to couple the geometry of the structure and the flow passing around it. Therefore, it allows the determination of the most influential parameters and suitable modifications in order to achieve the desired performance. The model does not require specific knowledge of the flow conditions in the vicinity of the structure, which improves its practical use during the early stages of design. Estimations have been made for two straight cantilevers under a range of pressures. The results obtained show good agreement with measurements from experiments

  1. Reconstruction of dynamic image series from undersampled MRI data using data-driven model consistency condition (MOCCO).

    Science.gov (United States)

    Velikina, Julia V; Samsonov, Alexey A

    2015-11-01

    To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models preestimated from training data. We introduce the model consistency condition (MOCCO) technique, which utilizes temporal models to regularize reconstruction without constraining the solution to be low-rank, as is performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Our method was compared with a standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE-MRA) and cardiac CINE imaging. We studied the sensitivity of all methods to rank reduction and temporal subspace modeling errors. MOCCO demonstrated reduced sensitivity to modeling errors compared with the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE-MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. © 2014 Wiley Periodicals, Inc.

  2. Intra-urban variation of ultrafine particles as evaluated by process related land use and pollutant driven regression modelling.

    Science.gov (United States)

    Ghassoun, Yahya; Ruths, Matthias; Löwner, Marc-Oliver; Weber, Stephan

    2015-12-01

    The microscale intra-urban variation of ultrafine particle concentrations (UFP, diameter Dpland use regression model (LUR) using different urban morphology parameters as input is compared to a multiple regression type model driven by pollutant and meteorological parameters (PDR). While the LUR model was trained with UFP concentration the PDR model was trained with measured particle number size distribution data. The UFP concentration was then calculated from the modelled size distributions. Both statistical approaches include explanatory variables that try to address the 'process chain' of particle emission, dilution and deposition. LUR explained 74% and 85% of the variance of UFP for the full data set with a root mean square error (RMSE) of 668 cm(-3) and 1639 cm(-3) in summer and winter, respectively. PDR explained 56% and 74% of the variance with RMSE of 4066 cm(-3) and 6030 cm(-3) in summer and winter, respectively. Both models are capable to depict the spatial variation of UFP across the study area and in different outdoor microenvironments. The deviation from measured UFP concentrations is smaller in the LUR model than in PDR. The PDR model is well suited to predict urban particle number size distributions from the explanatory variables (total particle number concentration, black carbon and wind speed). The urban morphology parameters in the LUR model are able to resolve size dependent concentration variations but not as adequately as PDR. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

    Science.gov (United States)

    Karabatsos, George

    2017-02-01

    Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

  4. Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death

    Directory of Open Access Journals (Sweden)

    Andrew Abboud

    2016-09-01

    Full Text Available Inflammation induced by traumatic brain injury (TBI is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1 and 26 were survivors (GOS > 1. A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS] vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI.

  5. Modeling and optimization of a novel two-axis mirror-scanning mechanism driven by piezoelectric actuators

    International Nuclear Information System (INIS)

    Jing, Zijian; Xu, Minglong; Feng, Bo

    2015-01-01

    Mirror-scanning mechanisms are a key component in optical systems for diverse applications. However, the applications of existing piezoelectric scanners are limited due to their small angular travels. To overcome this problem, a novel two-axis mirror-scanning mechanism, which consists of a two-axis tip-tilt flexure mechanism and a set of piezoelectric actuators, is proposed in this paper. The focus of this research is on the design, theoretical modeling, and optimization of the piezoelectric-driven mechanism, with the goal of achieving large angular travels in a compact size. The design of the two-axis tip-tilt flexure mechanism is based on two nonuniform beams, which translate the limited linear output displacements of the piezoelectric actuators into large output angles. To exactly predict the angular travels, we built a voltage-angle model that characterizes the relationship between the input voltages to the piezoelectric actuators and the output angles of the piezoelectric-driven mechanism. Using this analytical model, the optimization is performed to improve the angular travels. A prototype of the mirror-scanning mechanism is fabricated based on the optimization results, and experiments are implemented to test the two-axis output angles. The experimental result shows that the angular travels of the scanner achieve more than 50 mrad, and the error between the analytical model and the experiment is about 11%. This error is much smaller than the error for the model built using the previous method because the influence of the stiffness of the mechanical structure on the deformation of the piezoelectric stack is considered in the voltage-angle model. (paper)

  6. Lithospheric deformation in the Africa-Iberia plate boundary: Improved neotectonic modeling testing a basal-driven Alboran plate

    Science.gov (United States)

    Neres, M.; Carafa, M. M. C.; Fernandes, R. M. S.; Matias, L.; Duarte, J. C.; Barba, S.; Terrinha, P.

    2016-09-01

    We present an improved neotectonic numerical model of the complex NW Africa-SW Eurasia plate boundary segment that runs from west to east along the Gloria Fault up to the northern Algerian margin. We model the surface velocity field and the ongoing lithospheric deformation using the most recent version of the thin-shell code SHELLS and updated lithospheric model and fault map of the region. To check the presence versus the absence of an independently driven Alboran domain, we develop two alternative plate models: one does not include an Alboran plate; another includes it and determines the basal shear tractions necessary to drive it with known velocities. We also compare two alternative sets of Africa-Eurasia velocity boundary conditions, corresponding to geodetic and geological-scale averages of plate motion. Finally, we perform an extensive parametric study of fault friction coefficient, trench resistance, and velocities imposed in Alboran nodes. The final run comprises 5240 experiments, each scored to geodetic velocities (estimated for 250 stations and here provided), stress direction data, and seismic strain rates. The model with the least discrepancy to the data includes the Alboran plate driven by a basal WSW directed shear traction, slightly oblique to the westward direction of Alboran motion. We provide estimates of long-term strain rates and slip rates for the modeled faults, which can be useful for further hazard studies. Our results support that a mechanism additional to the Africa-Eurasia convergence is required to drive the Alboran domain, which can be related to subduction processes occurring within the mantle.

  7. Studies on generalized kinetic model and Pareto optimization of a product-driven self-cycling bioprocess.

    Science.gov (United States)

    Sun, Kaibiao; Kasperski, Andrzej; Tian, Yuan

    2014-10-01

    The aim of this study is the optimization of a product-driven self-cycling bioprocess and presentation of a way to determine the best possible decision variables out of a set of alternatives based on the designed model. Initially, a product-driven generalized kinetic model, which allows a flexible choice of the most appropriate kinetics is designed and analysed. The optimization problem is given as the bi-objective one, where maximization of biomass productivity and minimization of unproductive loss of substrate are the objective functions. Then, the Pareto fronts are calculated for exemplary kinetics. It is found that in the designed bioprocess, a decrease of emptying/refilling fraction and an increase of substrate feeding concentration cause an increase of the biomass productivity. An increase of emptying/refilling fraction and a decrease of substrate feeding concentration cause a decrease of unproductive loss of substrate. The preferred solutions are calculated using the minimum distance from an ideal solution method, while giving proposals of their modifications derived from a decision maker's reactions to the generated solutions.

  8. Predictive Modeling for Strongly Correlated f-electron Systems: A first-principles and database driven machine learning approach

    Science.gov (United States)

    Ahmed, Towfiq; Khair, Adnan; Abdullah, Mueen; Harper, Heike; Eriksson, Olle; Wills, John; Zhu, Jian-Xin; Balatsky, Alexander

    Data driven computational tools are being developed for theoretical understanding of electronic properties in f-electron based materials, e.g., Lanthanides and Actnides compounds. Here we show our preliminary work on Ce compounds. Due to a complex interplay among the hybridization of f-electrons to non-interacting conduction band, spin-orbit coupling, and strong coulomb repulsion of f-electrons, no model or first-principles based theory can fully explain all the structural and functional phases of f-electron systems. Motivated by the large need in predictive modeling of actinide compounds, we adopted a data-driven approach. We found negative correlation between the hybridization and atomic volume. Mutual information between these two features were also investigated. In order to extend our search space with more features and predictability of new compounds, we are currently developing electronic structure database. Our f-electron database will be potentially aided by machine learning (ML) algorithm to extract complex electronic, magnetic and structural properties in f-electron system, and thus, will open up new pathways for predictive capabilities and design principles of complex materials. NSEC, IMS at LANL.

  9. Suppression of AGN-driven Turbulence by Magnetic Fields in a Magnetohydrodynamic Model of the Intracluster Medium

    Science.gov (United States)

    Bambic, Christopher J.; Morsony, Brian J.; Reynolds, Christopher S.

    2018-04-01

    We investigate the role of active galactic nucleus (AGN) feedback in turbulent heating of galaxy clusters. Specifically, we analyze the production of turbulence by g-modes generated by the supersonic expansion and buoyant rise of AGN-driven bubbles. Previous work that neglects magnetic fields has shown that this process is inefficient, with less than 1% of the injected energy ending up in turbulence. This inefficiency primarily arises because the bubbles are shredded apart by hydrodynamic instabilities before they can excite sufficiently strong g-modes. Using a plane-parallel model of the intracluster medium (ICM) and 3D ideal magnetohydrodynamics (MHD) simulations, we examine the role of a large-scale magnetic field that is able to drape around these rising bubbles, preserving them from hydrodynamic instabilities. We find that while magnetic draping appears better able to preserve AGN-driven bubbles, the driving of g-modes and the resulting production of turbulence is still inefficient. The magnetic tension force prevents g-modes from transitioning into the nonlinear regime, suppressing turbulence in our model ICM. Our work highlights the ways in which ideal MHD is an insufficient description for the cluster feedback process, and we discuss future work such as the inclusion of anisotropic viscosity as a means of simulating high β plasma kinetic effects. These results suggest the hypothesis that other mechanisms of heating the ICM plasma such as sound waves or cosmic rays may be responsible for the observed feedback in galaxy clusters.

  10. ICAM-1–targeted thrombomodulin mitigates tissue factor–driven inflammatory thrombosis in a human endothelialized microfluidic model

    Science.gov (United States)

    Johnston, Ian H.; Villa, Carlos H.; Gollomp, Kandace; Esmon, Charles T.; Cines, Douglas B.; Poncz, Mortimer

    2017-01-01

    Diverse human illnesses are characterized by loss or inactivation of endothelial thrombomodulin (TM), predisposing to microvascular inflammation, activation of coagulation, and tissue ischemia. Single-chain antibody fragment (scFv)/TM) fusion proteins, previously protective against end-organ injury in murine models of inflammation, are attractive candidates to treat inflammatory thrombosis. However, animal models have inherent differences in TM and coagulation biology, are limited in their ability to resolve and control endothelial biology, and do not allow in-depth testing of “humanized” scFv/TM fusion proteins, which are necessary for translation to the clinical domain. To address these challenges, we developed a human whole-blood, microfluidic model of inflammatory, tissue factor (TF)–driven coagulation that features a multichannel format for head-to-head comparison of therapeutic approaches. In this model, fibrin deposition, leukocyte adhesion, and platelet adhesion and aggregation showed a dose-dependent response to tumor necrosis factor-α activation and could be quantified via real-time microscopy. We used this model to compare hTM/R6.5, a humanized, intracellular adhesion molecule 1 (ICAM-1)–targeted scFv/TM biotherapeutic, to untargeted antithrombotic agents, including soluble human TM (shTM), anti–TF antibodies, and hirudin. The targeted hTM/R6.5 more effectively inhibited TF-driven coagulation in a protein C (PC)–dependent manner and demonstrated synergy with supplemental PC. These results support the translational prospects of ICAM-targeted scFv/TM and illustrate the utility of the microfluidic system as a platform to study humanized therapeutics at the interface of endothelium and whole blood under flow. PMID:29296786

  11. Climate-driven uncertainties in modeling terrestrial energy and water fluxes: a site-level to global-scale analysis.

    Science.gov (United States)

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

    2014-06-01

    We used a land surface model constrained using data from flux tower sites, to analyze the biases in ecosystem energy and water fluxes arising due to the use of meteorological reanalysis datasets. Following site-level model calibration encompassing major vegetation types from the tropics to the northern high-latitudes, we repeated the site and global simulations using two reanalysis datasets: the NCEP/NCAR and the CRUNCEP. In comparison with the model simulations using observed meteorology from sites, the reanalysis-driven simulations produced several systematic biases in net radiation (Rn ), latent heat (LE), and sensible heat (H) fluxes. These include: (i) persistently positive tropical/subtropical biases in Rn using the NCEP/NCAR, and gradually transitioning to negative Rn biases in the higher latitudes; (ii) large positive H biases in the tropics/subtropics using the NCEP/NCAR; (iii) negative LE biases using the NCEP/NCAR above 40°N; (iv) high tropical LE using the CRUNCEP in comparison with observationally derived global estimates; and (v) flux-partitioning biases from canopy and ground components. Across vegetation types, we investigated the role of the meteorological drivers (shortwave and longwave radiation, atmospheric humidity, temperature, precipitation) and their seasonal biases in controlling these reanalysis-driven uncertainties. At the global scale, our site-level analysis explains several model-data differences in the LE and H fluxes when compared with observationally derived global estimates of these fluxes. Using our results, we discuss the implications of site-level model calibration on subsequent regional/global applications to study energy and hydrological processes. The flux-partitioning biases presented in this study have potential implications on the couplings among terrestrial carbon, energy, and water fluxes, and for the calibration of land-atmosphere parameterizations that are dependent on LE/H partitioning. © 2013 John Wiley & Sons Ltd.

  12. A 'putty-practically-clay' vintage model with R and D driven biases in energy-saving technical change

    International Nuclear Information System (INIS)

    Van Zon, A.; Lontzek, T.

    2005-01-01

    This paper deals with the problem of tackling the adverse effect of output growth on environmental quality. For this purpose we use an intermediate sector that builds 'putty-practically-clay' capital consisting of an energy-raw capital amalgam used for final goods production. The putty-practically-clay model is a strongly simplified version of a full putty-clay model, that mimics all the relevant behaviour of a full puttyclay model, but that does not entail the administrative hassle of a full putty-clay vintage model. In addition to this, we introduce an R and D sector that develops renewable- and conventional energy-based technologies. The allocation of R and D activities over these two uses of R and D gives rise to an induced bias in technical change. In the context of our model, this implies that technological progress is primarily driven by the desire to counteract the upward pressure on production cost implied by a continuing price increase of conventional energy resources. Hotelling's rule suggests that this price rise is unavoidable in the face of the ongoing depletion of conventional energy reserves. By means of some illustrative model simulations we study the effects of energy policy on the dynamics of the model for alternative policy options aimed at achieving GHG emission reductions. We identify the conditions under which energy policy might partly backfire and present some non-standard policy implications

  13. A novel model for simulating the racing effect in capillary-driven underfill process in flip chip

    Science.gov (United States)

    Zhu, Wenhui; Wang, Kanglun; Wang, Yan

    2018-04-01

    Underfill is typically applied in flip chips to increase the reliability of the electronic packagings. In this paper, the evolution of the melt-front shape of the capillary-driven underfill flow is studied through 3D numerical analysis. Two different models, the prevailing surface force model and the capillary model based on the wetted wall boundary condition, are introduced to test their applicability, where level set method is used to track the interface of the two phase flow. The comparison between the simulation results and experimental data indicates that, the surface force model produces better prediction on the melt-front shape, especially in the central area of the flip chip. Nevertheless, the two above models cannot simulate properly the racing effect phenomenon that appears during underfill encapsulation. A novel ‘dynamic pressure boundary condition’ method is proposed based on the validated surface force model. Utilizing this approach, the racing effect phenomenon is simulated with high precision. In addition, a linear relationship is derived from this model between the flow front location at the edge of the flip chip and the filling time. Using the proposed approach, the impact of the underfill-dispensing length on the melt-front shape is also studied.

  14. Going beyond best technology and lowest price: on renewable energy investors’ preference for service-driven business models

    International Nuclear Information System (INIS)

    Loock, Moritz

    2012-01-01

    Renewable energy is becoming increasingly important for economies in many countries. But still in an emerging industry, renewable energy requires supportive energy policy helping firms to develop and protect competitive advantages in global competition. As a guideline for designing such policy, we consult well-informed stakeholders within the renewable energy industry: investors. Their preferences serve as explorative indicator for assessing which business models might succeed in competition. To contribute to only limited research on renewable energy investors’ preferences, we ask, which business models investment managers for renewable energy prefer to invest in. We report from an explorative study of 380 choices of renewable energy investment managers. Based on the stated preferences, we modelled three generic business models to calculate the share of investors’ preferences. We find exiting evidence: a “customer intimacy” business model that proposes best services is much more preferred by investors than business models that propose lowest price or best technology. Policy-makers can use those insights for designing policy that supports service-driven business models for renewable energy with a scope on customer needs rather than technology or price. Additionally, we state important implications for renewable energy entrepreneurs, managers and research.

  15. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control.

    Science.gov (United States)

    Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

    2017-02-08

    Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

  16. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

    Science.gov (United States)

    Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

    2017-01-01

    Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms. PMID:28208697

  17. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

    Directory of Open Access Journals (Sweden)

    René Felix Reinhart

    2017-02-01

    Full Text Available Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

  18. User-driven health care: answering multidimensional information needs in individual patients utilizing post-EBM approaches: an operational model.

    Science.gov (United States)

    Biswas, Rakesh; Maniam, Jayanthy; Lee, Edwin Wen Huo; Gopal, Premalatha; Umakanth, Shashikiran; Dahiya, Sumit; Ahmed, Sayeed

    2008-10-01

    The hypothesis in the conceptual model was that a user-driven innovation in presently available information and communication technology infrastructure would be able to meet patient and health professional users information needs and help them attain better health outcomes. An operational model was created to plan a trial on a sample diabetic population utilizing a randomized control trial design, assigning one randomly selected group of diabetics to receive electronic information intervention and analyse if it would improve their health outcomes in comparison with a matched diabetic population who would only receive regular medical intervention. Diabetes was chosen for this particular trial, as it is a major chronic illness in Malaysia as elsewhere in the world. It is in essence a position paper for how the study concept should be organized to stimulate wider discussion prior to beginning the study.

  19. Facial First Impressions Across Culture: Data-Driven Modeling of Chinese and British Perceivers' Unconstrained Facial Impressions.

    Science.gov (United States)

    Sutherland, Clare A M; Liu, Xizi; Zhang, Lingshan; Chu, Yingtung; Oldmeadow, Julian A; Young, Andrew W

    2018-04-01

    People form first impressions from facial appearance rapidly, and these impressions can have considerable social and economic consequences. Three dimensions can explain Western perceivers' impressions of Caucasian faces: approachability, youthful-attractiveness, and dominance. Impressions along these dimensions are theorized to be based on adaptive cues to threat detection or sexual selection, making it likely that they are universal. We tested whether the same dimensions of facial impressions emerge across culture by building data-driven models of first impressions of Asian and Caucasian faces derived from Chinese and British perceivers' unconstrained judgments. We then cross-validated the dimensions with computer-generated average images. We found strong evidence for common approachability and youthful-attractiveness dimensions across perceiver and face race, with some evidence of a third dimension akin to capability. The models explained ~75% of the variance in facial impressions. In general, the findings demonstrate substantial cross-cultural agreement in facial impressions, especially on the most salient dimensions.

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

    OpenAIRE

    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...

  1. A Time-dependent Heliospheric Model Driven by Empirical Boundary Conditions

    Science.gov (United States)

    Kim, T. K.; Arge, C. N.; Pogorelov, N. V.

    2017-12-01

    Consisting of charged particles originating from the Sun, the solar wind carries the Sun's energy and magnetic field outward through interplanetary space. The solar wind is the predominant source of space weather events, and modeling the solar wind propagation to Earth is a critical component of space weather research. Solar wind models are typically separated into coronal and heliospheric parts to account for the different physical processes and scales characterizing each region. Coronal models are often coupled with heliospheric models to propagate the solar wind out to Earth's orbit and beyond. The Wang-Sheeley-Arge (WSA) model is a semi-empirical coronal model consisting of a potential field source surface model and a current sheet model that takes synoptic magnetograms as input to estimate the magnetic field and solar wind speed at any distance above the coronal region. The current version of the WSA model takes the Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model as input to provide improved time-varying solutions for the ambient solar wind structure. When heliospheric MHD models are coupled with the WSA model, density and temperature at the inner boundary are treated as free parameters that are tuned to optimal values. For example, the WSA-ENLIL model prescribes density and temperature assuming momentum flux and thermal pressure balance across the inner boundary of the ENLIL heliospheric MHD model. We consider an alternative approach of prescribing density and temperature using empirical correlations derived from Ulysses and OMNI data. We use our own modeling software (Multi-scale Fluid-kinetic Simulation Suite) to drive a heliospheric MHD model with ADAPT-WSA input. The modeling results using the two different approaches of density and temperature prescription suggest that the use of empirical correlations may be a more straightforward, consistent method.

  2. The power of an ontology-driven developmental toxicity database for data mining and computational modeling

    Science.gov (United States)

    Modeling of developmental toxicology presents a significant challenge to computational toxicology due to endpoint complexity and lack of data coverage. These challenges largely account for the relatively few modeling successes using the structure–activity relationship (SAR) parad...

  3. Business process modelling in demand-driven agri-food supply chains : a reference framework

    NARCIS (Netherlands)

    Verdouw, C.N.

    2010-01-01

    Keywords: Business process models; Supply chain management; Information systems; Reference information models; Market orientation; Mass customisation; Configuration; Coordination; Control; SCOR; Pot plants; Fruit industry

    Abstract

    The increasing volatility and diversity of

  4. Numerical model for wind-driven circulation in the Bay of Bengal

    Digital Repository Service at National Institute of Oceanography (India)

    Bahulayan, N.; Varadachari, V.V.R.

    experiments have shown that when a uniform wind stress in suddenly imposed over the sea surface, a steady circulation is generated after 50 h of numerical integration of model equations. The sensitivity of this model to bathymetry and coastal configuration...

  5. Business process modelling in demand-driven agri-food supply chains : a reference framework

    NARCIS (Netherlands)

    Verdouw, C.N.

    2010-01-01

    Keywords: Business process models; Supply chain management; Information systems; Reference information models; Market orientation; Mass customisation; Configuration; Coordination; Control; SCOR; Pot plants; Fruit industry Abstract The increasing volatility and diversity of demand urge agri-food

  6. Analytic Model Driven Organizational Design and Experimentation in Adaptive Command and Control

    National Research Council Canada - National Science Library

    Levchuk, Yuri N; Pattipati, Krishna R; Kleinman, David L

    2005-01-01

    .... This methodology can be used to design an executable model for human-in-the-loop, model-based experiments that provide the necessary empirical components for current and future research in adaptive C2 architectures...

  7. An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health.

    Science.gov (United States)

    Mancy, Rebecca; Brock, Patrick M; Kao, Rowland R

    2017-01-01

    Process models that focus on explicitly representing biological mechanisms are increasingly important in disease ecology and animal health research. However, the large number of process modelling approaches makes it difficult to decide which is most appropriate for a given disease system and research question. Here, we discuss different motivations for using process models and present an integrated conceptual analysis that can be used to guide the construction of infectious disease process models and comparisons between them. Our presentation complements existing work by clarifying the major differences between modelling approaches and their relationship with the biological characteristics of the epidemiological system. We first discuss distinct motivations for using process models in epidemiological research, identifying the key steps in model design and use associated with each. We then present a conceptual framework for guiding model construction and comparison, organised according to key aspects of epidemiological systems. Specifically, we discuss the number and type of disease states, whether to focus on individual hosts (e.g., cows) or groups of hosts (e.g., herds or farms), how space or host connectivity affect disease transmission, whether demographic and epidemiological processes are periodic or can occur at any time, and the extent to which stochasticity is important. We use foot-and-mouth disease and bovine tuberculosis in cattle to illustrate our discussion and support explanations of cases in which different models are used to address similar problems. The framework should help those constructing models to structure their approach to modelling decisions and facilitate comparisons between models in the literature.

  8. A remote sensing driven distributed hydrological model of the Senegal River basin

    DEFF Research Database (Denmark)

    Stisen, Simon; Jensen, Karsten Høgh; Sandholt, Inge

    2008-01-01

    Distributed hydrological models require extensive data amounts for driving the models and for parameterization of the land surface and subsurface. This study investigates the potential of applying remote sensing (RS) based input data in a hydrological model for the 350,000 km2 Senegal River basin...

  9. Knowledge-driven GIS modeling technique for gold exploration, Bulghah gold mine area, Saudi Arabia

    Directory of Open Access Journals (Sweden)

    Ahmed A. Madani

    2011-12-01

    Full Text Available This research aims to generate a favorability map for gold exploration at the Bulghah gold mine area using integration of geo-datasets within a GIS environment. Spatial data analyses and integration of different geo-datasets are carried out based on knowledge-driven and weighting technique. The integration process involves the weighting and scoring of different layers affecting the gold mineralization at the study area using the index overlay method within PCI Geomatica environment. Generation of the binary predictor maps for lithology, lineaments, faults and favorable contacts precede the construction of the favorability map. About 100 m buffer zones are generated for favorable contacts, lineaments and major faults layers. Internal weighting is assigned to each layer based on favorability for gold mineralization. The scores for lithology, major faults, lineaments and favorable contacts layers in the constructed favorability map are 50%, 25%, 10% and 15%, respectively. Final favorability map for the Bulghah gold mine area shows the recording of two new sites for gold mineralization located at the northern and southern extensions of tonalite–diorite intrusions. The northern new site is now exploited for gold from the Bulghah North mine. The southern new site is narrow and small; its rocks resemble those of the Bulghah gold mine.

  10. Theoretical modelling and optimization of bubble column dehumidifier for a solar driven humidification-dehumidification system

    Science.gov (United States)

    Ranjitha, P. Raj; Ratheesh, R.; Jayakumar, J. S.; Balakrishnan, Shankar

    2018-02-01

    Availability and utilization of energy and water are the top most global challenges being faced by the new millennium. At the present state water scarcity has become a global as well as a regional challenge. 40 % of world population faces water shortage. Challenge of water scarcity can be tackled only with increase in water supply beyond what is obtained from hydrological cycle. This can be achieved either by desalinating the sea water or by reusing the waste water. High energy requirement need to be overcome for either of the two processes. Of many desalination technologies, humidification dehumidification (HDH) technology powered by solar energy is widely accepted for small scale production. Detailed optimization studies on system have the potential to effectively utilize the solar energy for brackish water desalination. Dehumidification technology, specifically, require further study because the dehumidifier effectiveness control the energetic performance of the entire HDH system. The reason attributes to the high resistance involved to diffuse dilute vapor through air in a dehumidifier. The present work intends to optimize the design of a bubble column dehumidifier for a solar energy driven desalination process. Optimization is carried out using Matlab simulation. Design process will identify the unique needs of a bubble column dehumidifier in HDH system.

  11. Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure.

    Science.gov (United States)

    Zamora, Ruben; Vodovotz, Yoram; Mi, Qi; Barclay, Derek; Yin, Jinling; Horslen, Simon; Rudnick, David; Loomes, Kathleen M; Squires, Robert H

    2016-11-23

    Absence of early outcome biomarkers for Pediatric Acute Liver Failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all patient sub-groups. The networks in S and LTx were similar, and differed from NS. Dynamic Network Analysis suggested similar dynamic connectivity in S and LTx, but a more highly-interconnected network in NS that increased with time. A Dynamic Robustness Index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.

  12. Assessing antiangiogenic therapy response by DCE-MRI: development of a physiology driven multi-compartment model using population pharmacometrics.

    Directory of Open Access Journals (Sweden)

    Andreas Steingoetter

    Full Text Available Dynamic contrast enhanced (DCE- MRI is commonly applied for the monitoring of antiangiogenic therapy in oncology. Established pharmacokinetic (PK analysis methods of DCE-MRI data do not sufficiently reflect the complex anatomical and physiological constituents of the analyzed tissue. Hence, accepted endpoints such as Ktrans reflect an unknown multitude of local and global physiological effects often rendering an understanding of specific local drug effects impossible. In this work a novel multi-compartment PK model is presented, which for the first time allows the separation of local and systemic physiological effects. DCE-MRI data sets from multiple, simultaneously acquired tissues, i.e. spinal muscle, liver and tumor tissue, of hepatocellular carcinoma (HCC bearing rats were applied for model development. The full Markov chain Monte Carlo (MCMC Bayesian analysis method was applied for model parameter estimation and model selection was based on histological and anatomical considerations and numerical criteria. A population PK model (MTL3 model consisting of 3 measured and 6 latent (unobserved compartments was selected based on Bayesian chain plots, conditional weighted residuals, objective function values, standard errors of model parameters and the deviance information criterion. Covariate model building, which was based on the histology of tumor tissue, demonstrated that the MTL3 model was able to identify and separate tumor specific, i.e. local, and systemic, i.e. global, effects in the DCE-MRI data. The findings confirm the feasibility to develop physiology driven multi-compartment PK models from DCE-MRI data. The presented MTL3 model allowed the separation of a local, tumor specific therapy effect and thus has the potential for identification and specification of effectors of vascular and tissue physiology in antiangiogenic therapy monitoring.

  13. A watershed scale spatially-distributed model for streambank erosion rate driven by channel curvature

    Science.gov (United States)

    McMillan, Mitchell; Hu, Zhiyong

    2017-10-01

    Streambank erosion is a major source of fluvial sediment, but few large-scale, spatially distributed models exist to quantify streambank erosion rates. We introduce a spatially distributed model for streambank erosion applicable to sinuous, single-thread channels. We argue that such a model can adequately characterize streambank erosion rates, measured at the outsides of bends over a 2-year time period, throughout a large region. The model is based on the widely-used excess-velocity equation and comprised three components: a physics-based hydrodynamic model, a large-scale 1-dimensional model of average monthly discharge, and an empirical bank erodibility parameterization. The hydrodynamic submodel requires inputs of channel centerline, slope, width, depth, friction factor, and a scour factor A; the large-scale watershed submodel utilizes watershed-averaged monthly outputs of the Noah-2.8 land surface model; bank erodibility is based on tree cover and bank height as proxies for root density. The model was calibrated with erosion rates measured in sand-bed streams throughout the northern Gulf of Mexico coastal plain. The calibrated model outperforms a purely empirical model, as well as a model based only on excess velocity, illustrating the utility of combining a physics-based hydrodynamic model with an empirical bank erodibility relationship. The model could be improved by incorporating spatial variability in channel roughness and the hydrodynamic scour factor, which are here assumed constant. A reach-scale application of the model is illustrated on ∼1 km of a medium-sized, mixed forest-pasture stream, where the model identifies streambank erosion hotspots on forested and non-forested bends.

  14. A model-Driven Approach to Customize the Vocabulary of Communication Boards: Towards More Humanization of Health Care.

    Science.gov (United States)

    Franco, Natália M; Medeiros, Gabriel F; Silva, Edson A; Murta, Angela S; Machado, Aydano P; Fidalgo, Robson N

    2015-01-01

    This work presents a Modeling Language and its technological infrastructure to customize the vocabulary of Communication Boards (CB), which are important tools to provide more humanization of health care. Using a technological infrastructure based on Model-Driven Development (MDD) approach, our Modelin Language (ML) creates an abstraction layer between users (e.g., health professionals such as an audiologist or speech therapist) and application code. Moreover, the use of a metamodel enables a syntactic corrector for preventing creation of wrong models. Our ML and metamodel enable more autonomy for health professionals in creating customized CB because it abstracts complexities and permits them to deal only with the domain concepts (e.g., vocabulary and patient needs). Additionally, our infrastructure provides a configuration file that can be used to share and reuse models. This way, the vocabulary modelling effort will decrease our time since people share vocabulary models. Our study provides an infrastructure that aims to abstract the complexity of CB vocabulary customization, giving more autonomy to health professionals when they need customizing, sharing and reusing vocabularies for CB.

  15. Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

    International Nuclear Information System (INIS)

    Abler, Daniel; Kanellopoulos, Vassiliki; Dosanjh, Manjit; Davies, Jim; Peach, Ken; Jena, Raj; Kirkby, Norman

    2013-01-01

    Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of 'general Markov models', providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results. (author)

  16. State-space modelling for the ejector-based refrigeration system driven by low grade energy

    International Nuclear Information System (INIS)

    Xue, Binqiang; Cai, Wenjian; Wang, Xinli

    2015-01-01

    This paper presents a novel global state-space model to describe the ejector-based refrigeration system, which includes the dynamics of the two heat exchangers and the static properties of ejector, compressor and expansion valve. Different from the existing methods, the proposed method introduces some intermediate variables into the dynamic modelling in developing reduced order models of the heat exchangers (evaporator and condenser) based on the Number of Transfer Units (NTU) method. This global model with fewer dimensions is much simpler and can be more convenient for the real-time control system design, compared with other dynamic models. Finally, the proposed state-space model has been validated by dynamic response experiments on the ejector-based refrigeration cycle with refrigerant R134a.The experimental results indicate that the proposed model can predict well the dynamics of the ejector-based refrigeration system. - Highlights: • A low-order state-space model of ejector-based refrigeration system is presented. • Reduced-order models of heat exchangers are developed based on NTU method. • The variations of mass flow rates are introduced in multiple fluid phase regions. • Experimental results show the proposed model has a good performance

  17. Evaluation of Stakeholder-Driven Groundwater Management through Integrated Modeling and Remote Sensing in the US High Plains Aquifer

    Science.gov (United States)

    Deines, J. M.; Kendall, A. D.; Butler, J. J., Jr.; Hyndman, D. W.

    2017-12-01

    Irrigation greatly enhances agricultural yields and stabilizes farmer incomes, but overexploitation of water resources has depleted groundwater aquifers around the globe. In much of the High Plains Aquifer (HPA) in the United States, water-level declines threaten the continued viability of agricultural operations reliant on irrigation. Policy and management institutions to address this sustainability challenge differ widely across the HPA and the world. In Kansas, grassroots-driven legislation in 2012 allowed local stakeholder groups to establish Local Enhanced Management Areas (LEMAs) and work with state officials to generate enforceable and monitored water use reduction programs. The pioneering LEMA was formed in 2013, following a popular vote by farmers within a 256 km2 region in northwestern Kansas. The group sought to reduce groundwater pumping by 20% through 2017 in order to stabilize water levels while minimally reducing crop productivity. Initial statistical estimates indicate the LEMA has been successful; planning is underway to extend it for five years (2018-2022) and to implement additional LEMAs in the wider groundwater management district. Here, we assess the efficacy of this first LEMA with coupled crop-hydrology models to quantify water budget impacts and any associated trade-offs in crop productivity. We drive these models with a novel data fusion of water use data and our recent remotely sensed Annual Irrigation Maps (AIM) dataset, allowing detailed tracking of irrigation water in space and time. Results from these process-based models provide detailed insights into changes in the physical system resulting from the LEMA program that can inform future stakeholder-driven management in Kansas and in stressed aquifers around the world.

  18. Origin of inflation in CFT driven cosmology. R{sup 2}-gravity and non-minimally coupled inflaton models

    Energy Technology Data Exchange (ETDEWEB)

    Barvinsky, A.O. [Lebedev Physics Institute, Theory Department, Moscow (Russian Federation); Tomsk State University, Department of Physics, Tomsk (Russian Federation); UBC, Department of Physics and Astronomy, Pacific Institute for Theoretical Physics, Vancouver, BC (Canada); Kamenshchik, A.Yu. [Universita di Bologna, Dipartimento di Fisica e Astronomia, Bologna (Italy); L.D. Landau Institute for Theoretical Physics, Moscow (Russian Federation); INFN, Bologna (Italy); Nesterov, D.V. [Lebedev Physics Institute, Theory Department, Moscow (Russian Federation)

    2015-12-15

    We present a detailed derivation of the recently suggested new type of hill-top inflation [arXiv:1509.07270] originating from the microcanonical density matrix initial conditions in cosmology driven by conformal field theory (CFT). The cosmological instantons of topology S{sup 1} x S{sup 3}, which set up these initial conditions, have the shape of a garland with multiple periodic oscillations of the scale factor of the spatial S{sup 3}-section. They describe underbarrier oscillations of the inflaton and scale factor in the vicinity of the inflaton potential maximum, which gives a sufficient amount of inflation required by the known CMB data. We build the approximation of two coupled harmonic oscillators for these garland instantons and show that they can generate inflation consistent with the parameters of the CMB primordial power spectrum in the non-minimal Higgs inflation model and in R{sup 2} gravity. In particular, the instanton solutions provide smallness of inflationary slow-roll parameters ε and η < 0 and their relation ε ∝ η{sup 2} characteristic of these two models. We present the mechanism of formation of hill-like inflaton potentials, which is based on logarithmic loop corrections to the asymptotically shift-invariant tree-level potentials of these models in the Einstein frame. We also discuss the role of R{sup 2}-gravity as an indispensable finite renormalization tool in the CFT driven cosmology, which guarantees the nondynamical (ghost free) nature of its scale factor and special properties of its cosmological garland-type instantons. Finally, as a solution to the problem of hierarchy between the Planckian scale and the inflation scale we discuss the concept of a hidden sector of conformal higher spin fields. (orig.)

  19. Origin of inflation in CFT driven cosmology: R{sup 2}-gravity and non-minimally coupled inflaton models

    Energy Technology Data Exchange (ETDEWEB)

    Barvinsky, A. O., E-mail: barvin@td.lpi.ru [Theory Department, Lebedev Physics Institute, Leninsky Prospect 53, 119991, Moscow (Russian Federation); Department of Physics, Tomsk State University, Lenin Ave. 36, 634050, Tomsk (Russian Federation); Department of Physics and Astronomy, Pacific Institute for Theoretical Physics, UBC, 6224 Agricultural Road, V6T1Z1, Vancouver, BC (Canada); Kamenshchik, A. Yu., E-mail: kamenshchik@bo.infn.it [Dipartimento di Fisica e Astronomia, Università di Bologna and INFN, Via Irnerio 46, 40126, Bologna (Italy); L. D. Landau Institute for Theoretical Physics, 119334, Moscow (Russian Federation); Nesterov, D. V., E-mail: nesterov@td.lpi.it [Theory Department, Lebedev Physics Institute, Leninsky Prospect 53, 119991, Moscow (Russian Federation)

    2015-12-11

    We present a detailed derivation of the recently suggested new type of hill-top inflation originating from the microcanonical density matrix initial conditions in cosmology driven by conformal field theory (CFT). The cosmological instantons of topology S{sup 1}×S{sup 3}, which set up these initial conditions, have the shape of a garland with multiple periodic oscillations of the scale factor of the spatial S{sup 3}-section. They describe underbarrier oscillations of the inflaton and scale factor in the vicinity of the inflaton potential maximum, which gives a sufficient amount of inflation required by the known CMB data. We build the approximation of two coupled harmonic oscillators for these garland instantons and show that they can generate inflation consistent with the parameters of the CMB primordial power spectrum in the non-minimal Higgs inflation model and in R{sup 2} gravity. In particular, the instanton solutions provide smallness of inflationary slow-roll parameters ϵ and η<0 and their relation ϵ∼η{sup 2} characteristic of these two models. We present the mechanism of formation of hill-like inflaton potentials, which is based on logarithmic loop corrections to the asymptotically shift-invariant tree-level potentials of these models in the Einstein frame. We also discuss the role of R{sup 2}-gravity as an indispensable finite renormalization tool in the CFT driven cosmology, which guarantees the non-dynamical (ghost free) nature of its scale factor and special properties of its cosmological garland-type instantons. Finally, as a solution to the problem of hierarchy between the Planckian scale and the inflation scale we discuss the concept of a hidden sector of conformal higher spin fields.

  20. Origin of inflation in CFT driven cosmology: R^2-gravity and non-minimally coupled inflaton models

    Science.gov (United States)

    Barvinsky, A. O.; Kamenshchik, A. Yu.; Nesterov, D. V.

    2015-12-01

    We present a detailed derivation of the recently suggested new type of hill-top inflation [arXiv:1509.07270] originating from the microcanonical density matrix initial conditions in cosmology driven by conformal field theory (CFT). The cosmological instantons of topology S^1× S^3, which set up these initial conditions, have the shape of a garland with multiple periodic oscillations of the scale factor of the spatial S^3-section. They describe underbarrier oscillations of the inflaton and scale factor in the vicinity of the inflaton potential maximum, which gives a sufficient amount of inflation required by the known CMB data. We build the approximation of two coupled harmonic oscillators for these garland instantons and show that they can generate inflation consistent with the parameters of the CMB primordial power spectrum in the non-minimal Higgs inflation model and in R^2 gravity. In particular, the instanton solutions provide smallness of inflationary slow-roll parameters ɛ and η <0 and their relation ɛ ˜ η ^2 characteristic of these two models. We present the mechanism of formation of hill-like inflaton potentials, which is based on logarithmic loop corrections to the asymptotically shift-invariant tree-level potentials of these models in the Einstein frame. We also discuss the role of R^2-gravity as an indispensable finite renormalization tool in the CFT driven cosmology, which guarantees the non-dynamical (ghost free) nature of its scale factor and special properties of its cosmological garland-type instantons. Finally, as a solution to the problem of hierarchy between the Planckian scale and the inflation scale we discuss the concept of a hidden sector of conformal higher spin fields.

  1. DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Daifeng Wang

    2016-10-01

    Full Text Available Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs, cellular growth factors and microRNAs. A subsystem's gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally-e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the "state" and "control" in the model refer to its own (internal and another subsystem's (external gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model's parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs, seeing the degree to which these can be accounted for by orthologous (internal versus species-specific (external TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with

  2. Data Driven Modeling for Monitoring and Control of Industrial Fed-Batch Cultivations

    DEFF Research Database (Denmark)

    Bonné, Dennis; Alvarez, María Antonieta; Jørgensen, Sten Bay

    2014-01-01

    A systematic methodology for development of a set of discrete-time sequence models for batch control based on historical and online operating data is presented and investigated experimentally. The modeling is based on the two independent characteristic time dimensions of batch processing, being...... convergence of iterative learning control is combined with the closed-loop performance of model predictive control to form an optimal controller aiming to ensure reliable and reproducible operation of the batch process. This learning model predictive controller may also be used for optimizing control through...... optimization of the bioreactor operations model. The modeling and preliminary control performance is demonstrated on an industrial fed-batch protein cultivation production process. The presented methods lend themselves directly for application as Process Analytical Technologies....

  3. Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

    CERN Document Server

    Abler, Daniel; Davies, Jim; Dosanjh, Manjit; Jena, Raj; Kirkby, Norman; Peach, Ken

    2013-01-01

    Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of ‘general Markov models’, providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy ...

  4. Modeling cytokinesis of eukaryotic cells driven by the actomyosin contractile ring.

    Science.gov (United States)

    Zhao, Jia; Wang, Qi

    2016-12-01

    A three-dimensional (3D) hydrodynamic model for cytokinesis of eukaryotic cells is developed, in which we model dynamics of actomyosins in the cell cortex, in particular, along the cytokinetic ring formed in the cortex and in the neighborhood of the cell's division plane explicitly. Specifically, the active force actuated by the actomyosin's activity along the cytokinetic ring is modeled by a surface force whose strength is proportional to the actomyosin concentration while the cell morphology is tracked by a phase field model. The model is then solved in 3D space and time using a finite difference method on graphic processing units. Dynamical morphological patterns of eukaryotic cells during cytokinesis are numerically simulated with the model. These simulated morphological patterns agree quantitatively with experimental observations. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  5. An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health

    Directory of Open Access Journals (Sweden)

    Rebecca Mancy

    2017-09-01

    Full Text Available Process models that focus on explicitly representing biological mechanisms are increasingly important in disease ecology and animal health research. However, the large number of process modelling approaches makes it difficult to decide which is most appropriate for a given disease system and research question. Here, we discuss different motivations for using process models and present an integrated conceptual analysis that can be used to guide the construction of infectious disease process models and comparisons between them. Our presentation complements existing work by clarifying the major differences between modelling approaches and their relationship with the biological characteristics of the epidemiological system. We first discuss distinct motivations for using process models in epidemiological research, identifying the key steps in model design and use associated with each. We then present a conceptual framework for guiding model construction and comparison, organised according to key aspects of epidemiological systems. Specifically, we discuss the number and type of disease states, whether to focus on individual hosts (e.g., cows or groups of hosts (e.g., herds or farms, how space or host connectivity affect disease transmission, whether demographic and epidemiological processes are periodic or can occur at any time, and the extent to which stochasticity is important. We use foot-and-mouth disease and bovine tuberculosis in cattle to illustrate our discussion and support explanations of cases in which different models are used to address similar problems. The framework should help those constructing models to structure their approach to modelling decisions and facilitate comparisons between models in the literature.

  6. Modeling density-driven flow in porous media principles, numerics, software

    CERN Document Server

    Holzbecher, Ekkehard O

    1998-01-01

    Modeling of flow and transport in groundwater has become an important focus of scientific research in recent years. Most contributions to this subject deal with flow situations, where density and viscosity changes in the fluid are neglected. This restriction may not always be justified. The models presented in the book demonstrate immpressingly that the flow pattern may be completely different when density changes are taken into account. The main applications of the models are: thermal and saline convection, geothermal flow, saltwater intrusion, flow through salt formations etc. This book not only presents basic theory, but the reader can also test his knowledge by applying the included software and can set up own models.

  7. Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

    Science.gov (United States)

    Abler, Daniel; Kanellopoulos, Vassiliki; Davies, Jim; Dosanjh, Manjit; Jena, Raj; Kirkby, Norman; Peach, Ken

    2013-01-01

    Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of ‘general Markov models’, providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results. PMID:23824126

  8. Forecasting monthly inflow discharge of the Iffezheim reservoir using data-driven models

    Science.gov (United States)

    Zhang, Qing; Aljoumani, Basem; Hillebrand, Gudrun; Hoffmann, Thomas; Hinkelmann, Reinhard

    2017-04-01

    River stream flow is an essential element in hydrology study fields, especially for reservoir management, since it defines input into reservoirs. Forecasting this stream flow plays an important role in short or long-term planning and management in the reservoir, e.g. optimized reservoir and hydroelectric operation or agricultural irrigation. Highly accurate flow forecasting can significantly reduce economic losses and is always pursued by reservoir operators. Therefore, hydrologic time series forecasting has received tremendous attention of researchers. Many models have been proposed to improve the hydrological forecasting. Due to the fact that most natural phenomena occurring in environmental systems appear to behave in random or probabilistic ways, different cases may need a different methods to forecast the inflow and even a unique treatment to improve the forecast accuracy. The purpose of this study is to determine an appropriate model for forecasting monthly inflow to the Iffezheim reservoir in Germany, which is the last of the barrages in the Upper Rhine. Monthly time series of discharges, measured from 1946 to 2001 at the Plittersdorf station, which is located 6 km downstream of the Iffezheim reservoir, were applied. The accuracies of the used stochastic models - Fiering model and Auto-Regressive Integrated Moving Average models (ARIMA) are compared with Artificial Intelligence (AI) models - single Artificial Neural Network (ANN) and Wavelet ANN models (WANN). The Fiering model is a linear stochastic model and used for generating synthetic monthly data. The basic idea in modeling time series using ARIMA is to identify a simple model with as few model parameters as possible in order to provide a good statistical fit to the data. To identify and fit the ARIMA models, four phase approaches were used: identification, parameter estimation, diagnostic checking, and forecasting. An automatic selection criterion, such as the Akaike information criterion, is utilized

  9. A data-driven soft sensor for needle deflection in heterogeneous tissue using just-in-time modelling.

    Science.gov (United States)

    Rossa, Carlos; Lehmann, Thomas; Sloboda, Ronald; Usmani, Nawaid; Tavakoli, Mahdi

    2017-08-01

    Global modelling has traditionally been the approach taken to estimate needle deflection in soft tissue. In this paper, we propose a new method based on local data-driven modelling of needle deflection. External measurement of needle-tissue interactions is collected from several insertions in ex vivo tissue to form a cloud of data. Inputs to the system are the needle insertion depth, axial rotations, and the forces and torques measured at the needle base by a force sensor. When a new insertion is performed, the just-in-time learning method estimates the model outputs given the current inputs to the needle-tissue system and the historical database. The query is compared to every observation in the database and is given weights according to some similarity criteria. Only a subset of historical data that is most relevant to the query is selected and a local linear model is fit to the selected points to estimate the query output. The model outputs the 3D deflection of the needle tip and the needle insertion force. The proposed approach is validated in ex vivo multilayered biological tissue in different needle insertion scenarios. Experimental results in five different case studies indicate an accuracy in predicting needle deflection of 0.81 and 1.24 mm in the horizontal and vertical lanes, respectively, and an accuracy of 0.5 N in predicting the needle insertion force over 216 needle insertions.

  10. Investigating melting induced mantle heterogeneities in plate driven mantle convection models

    Science.gov (United States)

    Price, M.; Davies, H.; Panton, J.

    2017-12-01

    Observations from geochemistry and seismology continue to suggest a range of complex heterogeneity in Earth's mantle. In the deep mantle, two large low velocity provinces (LLVPs) have been regularly observed in seismic studies, with their longevity, composition and density compared to the surrounding mantle debated. The cause of these observed LLVPs is equally uncertain, with previous studies advocating either thermal or thermo-chemical causes. There is also evidence that these structures could provide chemically distinct reservoirs within the mantle, with recent studies also suggesting there may be additional reservoirs in the mantle, such as bridgmanite-enriched ancient mantle structures (BEAMS). One way to test these hypotheses is using computational models of the mantle, with models that capture the full 3D system being both complex and computationally expensive. Here we present results from our global mantle model TERRA. Using our model, we can track compositional variations in the convecting mantle that are generated by self-consistent, evolving melting zones. Alongside the melting, we track trace elements and other volatiles which can be partitioned during melting events, and expelled and recycled at the surface. Utilising plate reconstruction models as a boundary condition, the models generate the tectonic features observed at Earth's surface, while also organising the lower mantle into recognisable degree-two structures. This results in our models generating basaltic `oceanic' crusts which are then brought into the mantle at tectonic boundaries, providing additional chemical heterogeneity in the mantle volume. Finally, by utilising thermodynamic lookup tables to convert the final outputs from the model to seismic structures, together with resolution filters for global tomography models, we are able to make direct comparisons between our results and observations. By varying the parameters of the model, we investigate a range of current hypotheses for

  11. A simple topography-driven, calibration-free runoff generation model

    Science.gov (United States)

    Gao, H.; Birkel, C.; Hrachowitz, M.; Tetzlaff, D.; Soulsby, C.; Savenije, H. H. G.

    2017-12-01

    Determining the amount of runoff generation from rainfall occupies a central place in rainfall-runoff modelling. Moreover, reading landscapes and developing calibration-free runoff generation models that adequately reflect land surface heterogeneities remains the focus of much hydrological research. In this study, we created a new method to estimate runoff generation - HAND-based Storage Capacity curve (HSC) which uses a topographic index (HAND, Height Above the Nearest Drainage) to identify hydrological similarity and partially the saturated areas of catchments. We then coupled the HSC model with the Mass Curve Technique (MCT) method to estimate root zone storage capacity (SuMax), and obtained the calibration-free runoff generation model HSC-MCT. Both the two models (HSC and HSC-MCT) allow us to estimate runoff generation and simultaneously visualize the spatial dynamic of saturated area. We tested the two models in the data-rich Bruntland Burn (BB) experimental catchment in Scotland with an unusual time series of the field-mapped saturation area extent. The models were subsequently tested in 323 MOPEX (Model Parameter Estimation Experiment) catchments in the United States. HBV and TOPMODEL were used as benchmarks. We found that the HSC performed better in reproducing the spatio-temporal pattern of the observed saturated areas in the BB catchment compared with TOPMODEL which is based on the topographic wetness index (TWI). The HSC also outperformed HBV and TOPMODEL in the MOPEX catchments for both calibration and validation. Despite having no calibrated parameters, the HSC-MCT model also performed comparably well with the calibrated HBV and TOPMODEL, highlighting the robustness of the HSC model to both describe the spatial distribution of the root zone storage capacity and the efficiency of the MCT method to estimate the SuMax. Moreover, the HSC-MCT model facilitated effective visualization of the saturated area, which has the potential to be used for broader

  12. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

    International Nuclear Information System (INIS)

    Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias; Zhang, Jie

    2017-01-01

    Highlights: • An ensemble model is developed to produce both deterministic and probabilistic wind forecasts. • A deep feature selection framework is developed to optimally determine the inputs to the forecasting methodology. • The developed ensemble methodology has improved the forecasting accuracy by up to 30%. - Abstract: With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

  13. Multivariate modeling of complications with data driven variable selection: Guarding against overfitting and effects of data set size

    International Nuclear Information System (INIS)

    Schaaf, Arjen van der; Xu Chengjian; Luijk, Peter van; Veld, Aart A. van’t; Langendijk, Johannes A.; Schilstra, Cornelis

    2012-01-01

    Purpose: Multivariate modeling of complications after radiotherapy is frequently used in conjunction with data driven variable selection. This study quantifies the risk of overfitting in a data driven modeling method using bootstrapping for data with typical clinical characteristics, and estimates the minimum amount of data needed to obtain models with relatively high predictive power. Materials and methods: To facilitate repeated modeling and cross-validation with independent datasets for the assessment of true predictive power, a method was developed to generate simulated data with statistical properties similar to real clinical data sets. Characteristics of three clinical data sets from radiotherapy treatment of head and neck cancer patients were used to simulate data with set sizes between 50 and 1000 patients. A logistic regression method using bootstrapping and forward variable selection was used for complication modeling, resulting for each simulated data set in a selected number of variables and an estimated predictive power. The true optimal number of variables and true predictive power were calculated using cross-validation with very large independent data sets. Results: For all simulated data set sizes the number of variables selected by the bootstrapping method was on average close to the true optimal number of variables, but showed considerable spread. Bootstrapping is more accurate in selecting the optimal number of variables than the AIC and BIC alternatives, but this did not translate into a significant difference of the true predictive power. The true predictive power asymptotically converged toward a maximum predictive power for large data sets, and the estimated predictive power converged toward the true predictive power. More than half of the potential predictive power is gained after approximately 200 samples. Our simulations demonstrated severe overfitting (a predicative power lower than that of predicting 50% probability) in a number of small

  14. Data-driven urban drainage analysis : An alternative to hydrodynamic models?

    NARCIS (Netherlands)

    ten Veldhuis, J.A.E.; Tait, S.J.

    2011-01-01

    In the past, there has been an emphasis on the use of hydrodynamic models as a tool for urban drainage analysis. Limited availability of monitoring data and the perceived more limited resource requirements of models led to a preference for this approach. The last decade has seen a gradual

  15. Quantum measurement as a driven phase transition: An exactly solvable model

    NARCIS (Netherlands)

    Allahverdyan, A.; Balian, R.

    2001-01-01

    A model of quantum measurement is proposed, which aims to describe statistical mechanical aspects of this phenomenon, starting from a purely Hamiltonian formulation. The macroscopic measurement apparatus is modeled as an ideal Bose gas, the order parameter of which, that is, the amplitude of the

  16. Research needs for developing a commodity-driven freight modeling approach.

    Science.gov (United States)

    2003-01-01

    It is well known that better freight forecasting models and data are needed, but the literature does not clearly indicate which components of the modeling methodology are most in need of improvement, which is a critical need in an era of limited rese...

  17. Data-Driven Modeling of Target Human Behavior in Military Operations (Briefing Charts)

    Science.gov (United States)

    2012-03-12

    is to develop data-based general approaches to modeling and simulation of human behavior and quantitative methods of verification and validation. Crowd...behavior data were collected under controlled laboratory conditions. Mathematical models of human behavior were derived which were then coded into

  18. Data-Driven Modeling of Target Human Behavior in Military Operations

    Science.gov (United States)

    2014-03-12

    to develop data-based general approaches to modeling and simulation of human behavior and quantitative methods of verification and validation. Crowd...behavior data were collected under controlled laboratory conditions. Mathematical models of human behavior were derived which were then coded into

  19. Combining prior knowledge with data driven modeling of a batch distillation column including start-up

    NARCIS (Netherlands)

    van Lith, PF; Betlem, BHL; Roffel, B

    2003-01-01

    This paper presents the development of a simple model which describes the product quality and production over time of an experimental batch distillation column, including start-up. The model structure is based on a simple physical framework, which is augmented with fuzzy logic. This provides a way

  20. The Matrix model, a driven state variables approach to non-equilibrium thermodynamics

    NARCIS (Netherlands)

    Jongschaap, R.J.J.

    2001-01-01

    One of the new approaches in non-equilibrium thermodynamics is the so-called matrix model of Jongschaap. In this paper some features of this model are discussed. We indicate the differences with the more common approach based upon internal variables and the more sophisticated Hamiltonian and GENERIC