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Sample records for current models predict

  1. Modelling earth current precursors in earthquake prediction

    Directory of Open Access Journals (Sweden)

    R. Di Maio

    1997-06-01

    Full Text Available This paper deals with the theory of earth current precursors of earthquake. A dilatancy-diffusion-polarization model is proposed to explain the anomalies of the electric potential, which are observed on the ground surface prior to some earthquakes. The electric polarization is believed to be the electrokinetic effect due to the invasion of fluids into new pores, which are opened inside a stressed-dilated rock body. The time and space variation of the distribution of the electric potential in a layered earth as well as in a faulted half-space is studied in detail. It results that the surface response depends on the underground conductivity distribution and on the relative disposition of the measuring dipole with respect to the buried bipole source. A field procedure based on the use of an areal layout of the recording sites is proposed, in order to obtain the most complete information on the time and space evolution of the precursory phenomena in any given seismic region.

  2. Modelling Monsoons: Understanding and Predicting Current and Future Behaviour

    Energy Technology Data Exchange (ETDEWEB)

    Turner, A; Sperber, K R; Slingo, J M; Meehl, G A; Mechoso, C R; Kimoto, M; Giannini, A

    2008-09-16

    including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Without aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting the current and future behavior of monsoons.

  3. Ion current prediction model considering columnar recombination in alpha radioactivity measurement using ionized air transportation

    International Nuclear Information System (INIS)

    Naito, Susumu; Hirata, Yosuke; Izumi, Mikio; Sano, Akira; Miyamoto, Yasuaki; Aoyama, Yoshio; Yamaguchi, Hiromi

    2007-01-01

    We present a reinforced ion current prediction model in alpha radioactivity measurement using ionized air transportation. Although our previous model explained the qualitative trend of the measured ion current values, the absolute values of the theoretical curves were about two times as large as the measured values. In order to accurately predict the measured values, we reinforced our model by considering columnar recombination and turbulent diffusion, which affects columnar recombination. Our new model explained the considerable ion loss in the early stage of ion diffusion and narrowed the gap between the theoretical and measured values. The model also predicted suppression of ion loss due to columnar recombination by spraying a high-speed air flow near a contaminated surface. This suppression was experimentally investigated and confirmed. In conclusion, we quantitatively clarified the theoretical relation between alpha radioactivity and ion current in laminar flow and turbulent pipe flow. (author)

  4. Seasonal prediction of the Leeuwin Current using the POAMA dynamical seasonal forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Hendon, Harry H.; Wang, Guomin [Centre for Australian Weather and Climate Research, Bureau of Meteorology, PO Box 1289, Melbourne (Australia)

    2010-06-15

    The potential for predicting interannual variations of the Leeuwin Current along the west coast of Australia is addressed. The Leeuwin Current flows poleward against the prevailing winds and transports warm-fresh tropical water southward along the coast, which has a great impact on local climate and ecosystems. Variations of the current are tightly tied to El Nino/La Nina (weak during El Nino and strong during La Nina). Skilful seasonal prediction of the Leeuwin Current to 9-month lead time is achieved by empirical downscaling of dynamical coupled model forecasts of El Nino and the associated upper ocean heat content anomalies off the north west coast of Australia from the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast system. Prediction of the Leeuwin Current is possible because the heat content fluctuations off the north west coast are the primary driver of interannual annual variations of the current and these heat content variations are tightly tied to the occurrence of El Nino/La Nina. POAMA can skilfully predict both the occurrence of El Nino/La Nina and the subsequent transmission of the heat content anomalies from the Pacific onto the north west coast. (orig.)

  5. Study on model current predictive control method of PV grid- connected inverters systems with voltage sag

    Science.gov (United States)

    Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.

    2016-08-01

    According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.

  6. Model Predictive Current Control for High-Power Grid-Connected Converters with Output LCL Filter

    DEFF Research Database (Denmark)

    Delpino, Hernan Anres Miranda; Teodorescu, Remus; Rodriguez, Pedro

    2009-01-01

    A model predictive control strategy for a highpower, grid connected 3-level neutral clamped point converter is presented. Power losses constraints set a limit on commutation losses so reduced switching frequency is required, thus producing low frequency current harmonics. To reduce these harmonics...

  7. Harmonic current prediction by impedance modeling of grid-tied inverters

    DEFF Research Database (Denmark)

    Pereira, Heverton A.; Freijedo, Francisco D.; Silva, M. M.

    2017-01-01

    and harmonic voltage profiles. Results reinforce that impedance models can represent with relatively accuracy the harmonic current emitted by the PV plants at the point of common coupling (PCC). Lastly, a stress test is performed to show how a variation in the harmonic voltage phase angle impacts the PV plant...... impedance models when used in harmonic integration studies. It is aimed to estimate the harmonic current contribution as a function of the background harmonic voltages components. Time domain simulations based on detailed and average models are compared with the impedance model developed in frequency domain....... In grids with harmonic voltages, impedance models can predict the current distortion for all active power injection scenarios. Furthermore, measurements in a 1.4 MW PV plant connected in a distributed grid are used to validate the simulation based on impedance models during different power injections...

  8. Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

    DEFF Research Database (Denmark)

    Preindl, Matthias; Schaltz, Erik

    2010-01-01

    In drive systems the most used control structure is the cascade control with an inner torque, i.e. current and an outer speed control loop. The fairly small converter switching frequency in high power applications, e.g. wind turbines lead to modest speed control performance. An improvement bring...... the use of a current controller which takes into account the discrete states of the inverter, e.g. DTC or a more modern approach: Model Predictive Direct Current Control (MPDCC). Moreover overshoots and oscillations in the speed are not desired in many applications, since they lead to mechanical stress...

  9. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

    NARCIS (Netherlands)

    Li, T.; Hasegawa, T.; Yin, X.; Zhu, Y.; Boote, K.; Adam, M.; Bregaglio, S.; Buis, S.; Confalonieri, R.; Fumoto, T.; Gaydon, D.; Marcaida III, M.; Nakagawa, H.; Oriol, P.; Ruane, A.C.; Ruget, F.; Singh, B.; Singh, U.; Tang, L.; Yoshida, H.; Zhang, Z.; Bouman, B.

    2015-01-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We

  10. Preface: Current perspectives in modelling, monitoring, and predicting geophysical fluid dynamics

    Science.gov (United States)

    Mancho, Ana M.; Hernández-García, Emilio; López, Cristóbal; Turiel, Antonio; Wiggins, Stephen; Pérez-Muñuzuri, Vicente

    2018-02-01

    The third edition of the international workshop Nonlinear Processes in Oceanic and Atmospheric Flows was held at the Institute of Mathematical Sciences (ICMAT) in Madrid from 6 to 8 July 2016. The event gathered oceanographers, atmospheric scientists, physicists, and applied mathematicians sharing a common interest in the nonlinear dynamics of geophysical fluid flows. The philosophy of this meeting was to bring together researchers from a variety of backgrounds into an environment that favoured a vigorous discussion of concepts across different disciplines. The present Special Issue on Current perspectives in modelling, monitoring, and predicting geophysical fluid dynamics contains selected contributions, mainly from attendants of the workshop, providing an updated perspective on modelling aspects of geophysical flows as well as issues on prediction and assimilation of observational data and novel tools for describing transport and mixing processes in these contexts. More details on these aspects are discussed in this preface.

  11. Can current models of accommodation and vergence predict accommodative behavior in myopic children?

    Science.gov (United States)

    Sreenivasan, Vidhyapriya; Irving, Elizabeth L; Bobier, William R

    2014-08-01

    Investigations into the progression of myopia in children have long considered the role of accommodation as a cause and solution. Myopic children show high levels of accommodative adaptation, coupled with accommodative lag and high response AC/A (accommodative convergence per diopter of accommodation). This pattern differs from that predicted by current models of interaction between accommodation and vergence, where weakened reflex responses and a high AC/A would be associated with a low not high levels of accommodative adaptation. However, studies of young myopes were limited to only part of the accommodative vergence synkinesis and the reciprocal components of vergence adaptation and convergence accommodation were not studied in tandem. Accordingly, we test the hypothesis that the accommodative behavior of myopic children is not predicted by current models and whether that departure is explained by differences in the accommodative plant of the myopic child. Responses to incongruent stimuli (-2D, +2D adds, 10 prism diopter base-out prism) were investigated in 28 myopic and 25 non-myopic children aged 7-15 years. Subjects were divided into phoria groups - exo, ortho and eso based upon their near phoria. The school aged myopes showed high levels of accommodative adaptation but with reduced accommodation and high AC/A. This pattern is not explained by current adult models and could reflect a sluggish gain of the accommodative plant (ciliary muscle and lens), changes in near triad innervation or both. Further, vergence adaptation showed a predictable reciprocal relationship with the high accommodative adaptation, suggesting that departures from adult models were limited to accommodation not vergence behavior. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

    DEFF Research Database (Denmark)

    Preindl, Matthias; Schaltz, Erik

    2011-01-01

    The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid speed overshoots and oscillations for lifetime considerations. Model Predictive...... Direct Current Control (MPDCC) leads to an increase of torque control performance taking into account the discrete nature of inverters but temporary offsets and poor responses to load torque variations are still issues in speed control. A load torque estimator is proposed in this paper in order...

  13. Model-Based Prediction of Pulsed Eddy Current Testing Signals from Stratified Conductive Structures

    International Nuclear Information System (INIS)

    Zhang, Jian Hai; Song, Sung Jin; Kim, Woong Ji; Kim, Hak Joon; Chung, Jong Duk

    2011-01-01

    Excitation and propagation of electromagnetic field of a cylindrical coil above an arbitrary number of conductive plates for pulsed eddy current testing(PECT) are very complex problems due to their complicated physical properties. In this paper, analytical modeling of PECT is established by Fourier series based on truncated region eigenfunction expansion(TREE) method for a single air-cored coil above stratified conductive structures(SCS) to investigate their integrity. From the presented expression of PECT, the coil impedance due to SCS is calculated based on analytical approach using the generalized reflection coefficient in series form. Then the multilayered structures manufactured by non-ferromagnetic (STS301L) and ferromagnetic materials (SS400) are investigated by the developed PECT model. Good prediction of analytical model of PECT not only contributes to the development of an efficient solver but also can be applied to optimize the conditions of experimental setup in PECT

  14. A Grid Connected Transformerless Inverter and its Model Predictive Control Strategy with Leakage Current Elimination Capability

    Directory of Open Access Journals (Sweden)

    J. Fallah Ardashir

    2017-06-01

    Full Text Available This paper proposes a new single phase transformerless Photovoltaic (PV inverter for grid connected systems. It consists of six power switches, two diodes, one capacitor and filter at the output stage. The neutral of the grid is directly connected to the negative terminal of the source. This results in constant common mode voltage and zero leakage current. Model Predictive Controller (MPC technique is used to modulate the converter to reduce the output current ripple and filter requirements. The main advantages of this inverter are compact size, low cost, flexible grounding configuration. Due to brevity, the operating principle and analysis of the proposed circuit are presented in brief. Simulation and experimental results of 200W prototype are shown at the end to validate the proposed topology and concept. The results obtained clearly verifies the performance of the proposed inverter and its practical application for grid connected PV systems.

  15. Analytical Modeling Of The Steinmetz Coefficient For Single-Phase Transformer Eddy Current Loss Prediction

    Directory of Open Access Journals (Sweden)

    T. Aly Saandy

    2015-08-01

    Full Text Available Abstract This article presents to an analytical calculation methodology of the Steinmetz coefficient applied to the prediction of Eddy current loss in a single-phase transformer. Based on the electrical circuit theory the active power consumed by the core is expressed analytically in function of the electrical parameters as resistivity and the geometrical dimensions of the core. The proposed modeling approach is established with the duality parallel series. The required coefficient is identified from the empirical Steinmetz data based on the experimented active power expression. To verify the relevance of the model validations both by simulations with two in two different frequencies and measurements were carried out. The obtained results are in good agreement with the theoretical approach and the practical results.

  16. Lower hybrid current drive: an overview of simulation models, benchmarking with experiment, and predictions for future devices

    International Nuclear Information System (INIS)

    Bonoli, P.T.; Barbato, E.; Imbeaux, F.

    2003-01-01

    This paper reviews the status of lower hybrid current drive (LHCD) simulation and modeling. We first discuss modules used for wave propagation, absorption, and current drive with particular emphasis placed on comparing exact numerical solutions of the Fokker Planck equation in 2-dimension with solution methods that employ 1-dimensional and adjoint approaches. We also survey model predictions for LHCD in past and present experiments showing detailed comparisons between simulated and observed current drive efficiencies and hard X-ray profiles. Finally we discuss several model predictions for lower hybrid current profile control in proposed next step reactor options. (authors)

  17. Individualized model predicts brain current flow during transcranial direct-current stimulation treatment in responsive stroke patient.

    Science.gov (United States)

    Datta, Abhishek; Baker, Julie M; Bikson, Marom; Fridriksson, Julius

    2011-07-01

    Although numerous published reports have demonstrated the beneficial effects of transcranial direct-current stimulation (tDCS) on task performance, fundamental questions remain regarding the optimal electrode configuration on the scalp. Moreover, it is expected that lesioned brain tissue will influence current flow and should therefore be considered (and perhaps leveraged) in the design of individualized tDCS therapies for stroke. The current report demonstrates how different electrode configurations influence the flow of electrical current through brain tissue in a patient who responded positively to a tDCS treatment targeting aphasia. The patient, a 60-year-old man, sustained a left hemisphere ischemic stroke (lesion size = 87.42 mL) 64 months before his participation. In this study, we present results from the first high-resolution (1 mm(3)) model of tDCS in a brain with considerable stroke-related damage; the model was individualized for the patient who received anodal tDCS to his left frontal cortex with the reference cathode electrode placed on his right shoulder. We modeled the resulting brain current flow and also considered three additional reference electrode positions: right mastoid, right orbitofrontal cortex, and a "mirror" configuration with the anode over the undamaged right cortex. Our results demonstrate the profound effect of lesioned tissue on resulting current flow and the ability to modulate current pattern through the brain, including perilesional regions, through electrode montage design. The complexity of brain current flow modulation by detailed normal and pathologic anatomy suggest: (1) That computational models are critical for the rational interpretation and design of individualized tDCS stroke-therapy; and (2) These models must accurately reproduce head anatomy as shown here. Copyright © 2011 Elsevier Inc. All rights reserved.

  18. A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Hemphill, Geralyn M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-27

    Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to be an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.

  19. Assessing allometric models to predict vegetative growth of mango (Mangifera indica; Anacardiaceae) at the current-year branch scale.

    Science.gov (United States)

    Normand, Frédéric; Lauri, Pierre-Éric

    2012-03-01

    Accurate and reliable predictive models are necessary to estimate nondestructively key variables for plant growth studies such as leaf area and leaf, stem, and total biomass. Predictive models are lacking at the current-year branch scale despite the importance of this scale in plant science. We calibrated allometric models to estimate leaf area and stem and branch (leaves + stem) mass of current-year branches, i.e., branches several months old studied at the end of the vegetative growth season, of four mango cultivars on the basis of their basal cross-sectional area. The effects of year, site, and cultivar were tested. Models were validated with independent data and prediction accuracy was evaluated with the appropriate statistics. Models revealed a positive allometry between dependent and independent variables, whose y-intercept but not the slope, was affected by the cultivar. The effects of year and site were negligible. For each branch characteristic, cultivar-specific models were more accurate than common models built with pooled data from the four cultivars. Prediction quality was satisfactory but with data dispersion around the models, particularly for large values. Leaf area and stem and branch mass of mango current-year branches could be satisfactorily estimated on the basis of branch basal cross-sectional area with cultivar-specific allometric models. The results suggested that, in addition to the heteroscedastic behavior of the variables studied, model accuracy was probably related to the functional plasticity of branches in relation to the light environment and/or to the number of growth units composing the branches.

  20. Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance.

    Science.gov (United States)

    Teubner, Wera; Mehling, Anette; Schuster, Paul Xaver; Guth, Katharina; Worth, Andrew; Burton, Julien; van Ravenzwaay, Bennard; Landsiedel, Robert

    2013-12-01

    National legislations for the assessment of the skin sensitization potential of chemicals are increasingly based on the globally harmonized system (GHS). In this study, experimental data on 55 non-sensitizing and 45 sensitizing chemicals were evaluated according to GHS criteria and used to test the performance of computer (in silico) models for the prediction of skin sensitization. Statistic models (Vega, Case Ultra, TOPKAT), mechanistic models (Toxtree, OECD (Q)SAR toolbox, DEREK) or a hybrid model (TIMES-SS) were evaluated. Between three and nine of the substances evaluated were found in the individual training sets of various models. Mechanism based models performed better than statistical models and gave better predictivities depending on the stringency of the domain definition. Best performance was achieved by TIMES-SS, with a perfect prediction, whereby only 16% of the substances were within its reliability domain. Some models offer modules for potency; however predictions did not correlate well with the GHS sensitization subcategory derived from the experimental data. In conclusion, although mechanistic models can be used to a certain degree under well-defined conditions, at the present, the in silico models are not sufficiently accurate for broad application to predict skin sensitization potentials. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Evaluation of current prediction models for Lynch syndrome: updating the PREMM5 model to identify PMS2 mutation carriers

    NARCIS (Netherlands)

    A. Goverde (Anne); M.C.W. Spaander (Manon); D. Nieboer (Daan); A.M.W. van den Ouweland (Ans); W.N.M. Dinjens (Winand); H.J. Dubbink (Erik Jan); C. Tops (Cmj); S.W. Ten Broeke (Sanne W.); M.J. Bruno (Marco); R.M.W. Hofstra (Robert); E.W. Steyerberg (Ewout); A. Wagner (Anja)

    2017-01-01

    textabstractUntil recently, no prediction models for Lynch syndrome (LS) had been validated for PMS2 mutation carriers. We aimed to evaluate MMRpredict and PREMM5 in a clinical cohort and for PMS2 mutation carriers specifically. In a retrospective, clinic-based cohort we calculated predictions for

  2. Electron cyclotron current drive predictions for ITER: Comparison of different models

    International Nuclear Information System (INIS)

    Marushchenko, N.B.; Maassberg, H.; Beidler, C.D.; Turkin, Yu.

    2007-01-01

    Full text: Due to its high localization and operational flexibility, Electron Cyclotron Current Drive (ECCD) is envisaged for stabilizing the Neoclassical Tearing Mode (NTM) in tokamaks and correcting the rotational transform profile in stellarators. While the spatial location of the electron cyclotron resonant interaction is usually calculated by the ray-tracing technique, numerical tools for calculating the ECCD efficiency are not so common. Two different methods are often applied: i) direct calculation by Fokker-Planck modelling, and ii) by the adjoint approach technique. In the present report we analyze and compare different models used in the adjoint approach technique from the point of view of ITER applications. The numerical tools for calculating the ECCD efficiency developed to date do not completely cover the range of collisional regimes for the electrons involved in the current drive. Only two opposite limits are well developed, collisional and collisionless. Nevertheless, for the densities and temperatures expected for ECCD application in ITER, the collisionless limit model (with trapped particles taken into account) is quite suitable. We analyze the requisite ECCD scenarios with help of the new ray tracing code TRAVIS with the adjoint approach implemented. The (adjoint) Green's function applied for the current drive calculations is formulated with momentum conservation taken into account; this is especially important and even crucial for scenarios, in which mainly bulk electrons are responsible for absorption of the RF power. For comparison, the most common 'high speed limit' model in which the collision operator neglects the integral part and which is approximated by terms valid only for the tail electrons, produces an ECCD efficiency which is an underestimate for some cases by a factor of about 2. In order to select the appropriate model, a rough criterion of 'high speed limit' model applicability is formulated. The results are verified also by

  3. Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates.

    Science.gov (United States)

    Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P

    2017-03-01

    How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  4. Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

    Science.gov (United States)

    Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T.; Yahn, Jeremiah; Porter, Warren P.

    2017-01-01

    How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  5. Developing mathematical models to predict tensile properties of pulsed current gas tungsten arc welded Ti-6Al-4V alloy

    International Nuclear Information System (INIS)

    Balasubramanian, M.; Jayabalan, V.; Balasubramanian, V.

    2008-01-01

    Titanium (Ti-6Al-4V) alloy has gathered wide acceptance in the fabrication of light weight structures requiring a high strength-to-weight ratio, such as transportable bridge girders, military vehicles, road tankers and railway transport systems. The preferred welding process of titanium alloy is frequently gas tungsten arc (GTA) welding due to its comparatively easier applicability and better economy. In the case of single pass GTA welding of thinner section of this alloy, the pulsed current has been found beneficial due to its advantages over the conventional continuous current process. Many considerations come into the picture and one need to carefully balance various pulse current parameters to arrive at an optimum combination. Hence, in this investigation an attempt has been made to develop mathematical models to predict tensile properties of pulsed current GTA welded titanium alloy weldments. Four factors, five level, central composite, rotatable design matrix is used to optimise the required number of experiments. The mathematical models have been developed by response surface method (RSM). The adequacy of the models has been checked by ANOVA technique. By using the developed mathematical models, the tensile properties of the joints can be predicted with 99% confidence level

  6. Developing mathematical models to predict tensile properties of pulsed current gas tungsten arc welded Ti-6Al-4V alloy

    Energy Technology Data Exchange (ETDEWEB)

    Balasubramanian, M. [Department of Production Engineering, Sathyabama University, Old Mamallapuram Road, Chennai 600 119 (India)], E-mail: manianmb@rediffmail.com; Jayabalan, V. [Department of Manufacturing Engineering, Anna University, Guindy, Chennai 600 025 (India)], E-mail: jbalan@annauniv.edu; Balasubramanian, V. [Department of Manufacturing Engineering, Annamalai University, Annamalai Nagar 608 002 (India)], E-mail: visvabalu@yahoo.com

    2008-07-01

    Titanium (Ti-6Al-4V) alloy has gathered wide acceptance in the fabrication of light weight structures requiring a high strength-to-weight ratio, such as transportable bridge girders, military vehicles, road tankers and railway transport systems. The preferred welding process of titanium alloy is frequently gas tungsten arc (GTA) welding due to its comparatively easier applicability and better economy. In the case of single pass GTA welding of thinner section of this alloy, the pulsed current has been found beneficial due to its advantages over the conventional continuous current process. Many considerations come into the picture and one need to carefully balance various pulse current parameters to arrive at an optimum combination. Hence, in this investigation an attempt has been made to develop mathematical models to predict tensile properties of pulsed current GTA welded titanium alloy weldments. Four factors, five level, central composite, rotatable design matrix is used to optimise the required number of experiments. The mathematical models have been developed by response surface method (RSM). The adequacy of the models has been checked by ANOVA technique. By using the developed mathematical models, the tensile properties of the joints can be predicted with 99% confidence level.

  7. Modelling the current distribution and predicted spread of the flea species Ctenocephalides felis infesting outdoor dogs in Spain.

    Science.gov (United States)

    Gálvez, Rosa; Musella, Vicenzo; Descalzo, Miguel A; Montoya, Ana; Checa, Rocío; Marino, Valentina; Martín, Oihane; Cringoli, Giuseppe; Rinaldi, Laura; Miró, Guadalupe

    2017-09-19

    The cat flea, Ctenocephalides felis, is the most prevalent flea species detected on dogs and cats in Europe and other world regions. The status of flea infestation today is an evident public health concern because of their cosmopolitan distribution and the flea-borne diseases transmission. This study determines the spatial distribution of the cat flea C. felis infesting dogs in Spain. Using geospatial tools, models were constructed based on entomological data collected from dogs during the period 2013-2015. Bioclimatic zones, covering broad climate and vegetation ranges, were surveyed in relation to their size. The models builded were obtained by negative binomial regression of several environmental variables to show impacts on C. felis infestation prevalence: land cover, bioclimatic zone, mean summer and autumn temperature, mean summer rainfall, distance to urban settlement and normalized difference vegetation index. In the face of climate change, we also simulated the future distributions of C. felis for the global climate model (GCM) "GFDL-CM3" and for the representative concentration pathway RCP45, which predicts their spread in the country. Predictive models for current climate conditions indicated the widespread distribution of C. felis throughout Spain, mainly across the central northernmost zone of the mainland. Under predicted conditions of climate change, the risk of spread was slightly greater, especially in the north and central peninsula, than for the current situation. The data provided will be useful for local veterinarians to design effective strategies against flea infestation and the pathogens transmitted by these arthropods.

  8. Finite element model predicts current density distribution for clinical applications of tDCS and tACS

    Directory of Open Access Journals (Sweden)

    Toralf eNeuling

    2012-09-01

    Full Text Available Transcranial direct current stimulation (tDCS has been applied in numerous scientific studies over the past decade. However, the possibility to apply tDCS in therapy of neuropsychiatric disorders is still debated. While transcranial magnetic stimulation (TMS has been approved for treatment of major depression in the United States by the Food and Drug Administration (FDA, tDCS is not as widely accepted. One of the criticisms against tDCS is the lack of spatial specificity. Focality is limited by the electrode size (35 cm2 are commonly used and the bipolar arrangement. However, a current flow through the head directly from anode to cathode is an outdated view. Finite element (FE models have recently been used to predict the exact current flow during tDCS. These simulations have demonstrated that the current flow depends on tissue shape and conductivity. Toface the challenge to predict the location, magnitude and direction of the current flow induced by tDCS and transcranial alternating current stimulation (tACS, we used a refined realistic FE modeling approach. With respect to the literature on clinical tDCS and tACS, we analyzed two common setups for the location of the stimulation electrodes which target the frontal lobe and the occipital lobe, respectively. We compared lateral and medial electrode configuration with regard to theirusability. We were able to demonstrate that the lateral configurations yielded more focused stimulation areas as well as higher current intensities in the target areas. The high resolution of our simulation allows one to combine the modeled current flow with the knowledge of neuronal orientation to predict the consequences of tDCS and tACS. Our results not only offer a basis for a deeper understanding of the stimulation sites currently in use for clinical applications but also offer a better interpretation of observed effects.

  9. A Simple Analytical Model for Predicting the Detectable Ion Current in Ion Mobility Spectrometry Using Corona Discharge Ionization Sources

    Science.gov (United States)

    Kirk, Ansgar Thomas; Kobelt, Tim; Spehlbrink, Hauke; Zimmermann, Stefan

    2018-05-01

    Corona discharge ionization sources are often used in ion mobility spectrometers (IMS) when a non-radioactive ion source with high ion currents is required. Typically, the corona discharge is followed by a reaction region where analyte ions are formed from the reactant ions. In this work, we present a simple yet sufficiently accurate model for predicting the ion current available at the end of this reaction region when operating at reduced pressure as in High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) or most IMS-MS instruments. It yields excellent qualitative agreement with measurement results and is even able to calculate the ion current within an error of 15%. Additional interesting findings of this model are the ion current at the end of the reaction region being independent from the ion current generated by the corona discharge and the ion current in High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) growing quadratically when scaling down the length of the reaction region. [Figure not available: see fulltext.

  10. Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.

    Directory of Open Access Journals (Sweden)

    Hannah Slater

    Full Text Available Modelling the spatial distributions of human parasite species is crucial to understanding the environmental determinants of infection as well as for guiding the planning of control programmes. Here, we use ecological niche modelling to map the current potential distribution of the macroparasitic disease, lymphatic filariasis (LF, in Africa, and to estimate how future changes in climate and population could affect its spread and burden across the continent. We used 508 community-specific infection presence data collated from the published literature in conjunction with five predictive environmental/climatic and demographic variables, and a maximum entropy niche modelling method to construct the first ecological niche maps describing potential distribution and burden of LF in Africa. We also ran the best-fit model against climate projections made by the HADCM3 and CCCMA models for 2050 under A2a and B2a scenarios to simulate the likely distribution of LF under future climate and population changes. We predict a broad geographic distribution of LF in Africa extending from the west to the east across the middle region of the continent, with high probabilities of occurrence in the Western Africa compared to large areas of medium probability interspersed with smaller areas of high probability in Central and Eastern Africa and in Madagascar. We uncovered complex relationships between predictor ecological niche variables and the probability of LF occurrence. We show for the first time that predicted climate change and population growth will expand both the range and risk of LF infection (and ultimately disease in an endemic region. We estimate that populations at risk to LF may range from 543 and 804 million currently, and that this could rise to between 1.65 to 1.86 billion in the future depending on the climate scenario used and thresholds applied to signify infection presence.

  11. Rheumatology in the community of Madrid: current availability of rheumatologists and future needs using a predictive model.

    Science.gov (United States)

    Lázaro y De Mercado, Pablo; Blasco Bravo, Antonio Javier; Lázaro y De Mercado, Ignacio; Castañeda, Santos; López Robledillo, Juan Carlos

    2013-01-01

    To: 1) describe the distribution of the public sector rheumatologists; 2) identify variables on which the workload in Rheumatology depends; and 3) build a predictive model on the need of rheumatologists for the next 10 years, in the Community of Madrid (CM). The information was obtained through structured questionnaires sent to all services/units of Rheumatology of public hospitals in the CM. The population figures, current and forecasted, were obtained from the National Statistics Institute. A predictive model was built based on information about the current and foreseeable supply, current and foreseeable demand, and the assumptions and criteria used to match supply with demand. The underlying uncertainty in the model was assessed by sensitivity analysis. In the CM in 2011 there were 150 staff rheumatologists and 49 residents in 27 centers, which is equivalent to one rheumatologist for every 33,280 inhabitants in the general population, and one for every 4,996 inhabitants over 65 years. To keep the level of assistance of 2011 in 2021 in the general population, it would be necessary to train more residents or hire more rheumatologists in scenarios of demand higher than 15%. However, to keep the level of assistance in the population over 65 years of age it would be necessary to train more residents or hire more specialists even without increased demand. The model developed may be very useful for planning, with the CM policy makers, the needs of human resources in Rheumatology in the coming years. Copyright © 2012 Elsevier España, S.L. All rights reserved.

  12. Evaluation of current prediction models for Lynch syndrome: updating the PREMM5 model to identify PMS2 mutation carriers.

    Science.gov (United States)

    Goverde, A; Spaander, M C W; Nieboer, D; van den Ouweland, A M W; Dinjens, W N M; Dubbink, H J; Tops, C J; Ten Broeke, S W; Bruno, M J; Hofstra, R M W; Steyerberg, E W; Wagner, A

    2018-07-01

    Until recently, no prediction models for Lynch syndrome (LS) had been validated for PMS2 mutation carriers. We aimed to evaluate MMRpredict and PREMM5 in a clinical cohort and for PMS2 mutation carriers specifically. In a retrospective, clinic-based cohort we calculated predictions for LS according to MMRpredict and PREMM5. The area under the operator receiving characteristic curve (AUC) was compared between MMRpredict and PREMM5 for LS patients in general and for different LS genes specifically. Of 734 index patients, 83 (11%) were diagnosed with LS; 23 MLH1, 17 MSH2, 31 MSH6 and 12 PMS2 mutation carriers. Both prediction models performed well for MLH1 and MSH2 (AUC 0.80 and 0.83 for PREMM5 and 0.79 for MMRpredict) and fair for MSH6 mutation carriers (0.69 for PREMM5 and 0.66 for MMRpredict). MMRpredict performed fair for PMS2 mutation carriers (AUC 0.72), while PREMM5 failed to discriminate PMS2 mutation carriers from non-mutation carriers (AUC 0.51). The only statistically significant difference between PMS2 mutation carriers and non-mutation carriers was proximal location of colorectal cancer (77 vs. 28%, p PMS2 mutation carriers (AUC 0.77) and overall (AUC 0.81 vs. 0.72). We validated these results in an external cohort of 376 colorectal cancer patients, including 158 LS patients. MMRpredict and PREMM5 cannot adequately identify PMS2 mutation carriers. Adding location of colorectal cancer to PREMM5 may improve the performance of this model, which should be validated in larger cohorts.

  13. Axi-symmetric models of auroral current systems in Jupiter's magnetosphere with predictions for the Juno mission

    Directory of Open Access Journals (Sweden)

    S. W. H. Cowley

    2008-12-01

    Full Text Available We develop two related models of magnetosphere-ionosphere coupling in the jovian system by combining previous models defined at ionospheric heights with magnetospheric magnetic models that allow system parameters to be extended appropriately into the magnetosphere. The key feature of the combined models is thus that they allow direct connection to be made between observations in the magnetosphere, particularly of the azimuthal field produced by the magnetosphere-ionosphere coupling currents and the plasma angular velocity, and the auroral response in the ionosphere. The two models are intended to reflect typical steady-state sub-corotation conditions in the jovian magnetosphere, and transient super-corotation produced by sudden major solar wind-induced compressions, respectively. The key simplification of the models is that of axi-symmetry of the field, flow, and currents about the magnetic axis, limiting their validity to radial distances within ~30 RJ of the planet, though the magnetic axis is appropriately tilted relative to the planetary spin axis and rotates with the planet. The first exploration of the jovian polar magnetosphere is planned to be undertaken in 2016–2017 during the NASA New Frontiers Juno mission, with observations of the polar field, plasma, and UV emissions as a major goal. Evaluation of the models along Juno planning orbits thus produces predictive results that may aid in science mission planning. It is shown in particular that the low-altitude near-periapsis polar passes will generally occur underneath the corresponding auroral acceleration regions, thus allowing brief examination of the auroral primaries over intervals of ~1–3 min for the main oval and ~10 s for narrower polar arc structures, while the "lagging" field deflections produced by the auroral current systems on these passes will be ~0.1°, associated with azimuthal fields above the ionosphere of a few hundred nT.

  14. Current theoretical models fail to predict the topological complexity of the human genome

    Directory of Open Access Journals (Sweden)

    Javier eArsuaga

    2015-08-01

    Full Text Available Understanding the folding of the human genome is a key challenge of modern structural biology. The emergence of chromatin conformation capture assays ({it e.g.} Hi-C has revolutionized chromosome biology and provided new insights into the three dimensional structure of the genome. The experimental data are highly complex and need to be analyzed with quantitative tools. It has been argued that the data obtained from Hi-C assays are consistent with a fractal organization of the genome. A key characteristic textcolor{red}{of the fractal globule} is the lack of topological complexity (knotting or inter-linking. However, the absence of topological complexity contradicts results from polymer physics showing that the entanglement of long linear polymers in a confined volume increases rapidly with the length and with decreasing volume. textcolor{red}{{it In vivo} and {it in vitro} assays support this claim in some biological systems. We simulate knotted lattice polygons confined inside a sphere and demonstrate that their contact frequencies agree with the human Hi-C data.} We conclude that the topological complexity of the human genome cannot be inferred from current Hi-C data.

  15. Real Time Hybrid Model Predictive Control for the Current Profile of the Tokamak à Configuration Variable (TCV

    Directory of Open Access Journals (Sweden)

    Izaskun Garrido

    2016-08-01

    Full Text Available Plasma stability is one of the obstacles in the path to the successful operation of fusion devices. Numerical control-oriented codes as it is the case of the widely accepted RZIp may be used within Tokamak simulations. The novelty of this article relies in the hierarchical development of a dynamic control loop. It is based on a current profile Model Predictive Control (MPC algorithm within a multiloop structure, where a MPC is developed at each step so as to improve the Proportional Integral Derivative (PID global scheme. The inner control loop is composed of a PID-based controller that acts over the Multiple Input Multiple Output (MIMO system resulting from the RZIp plasma model of the Tokamak à Configuration Variable (TCV. The coefficients of this PID controller are initially tuned using an eigenmode reduction over the passive structure model. The control action corresponding to the state of interest is then optimized in the outer MPC loop. For the sake of comparison, both the traditionally used PID global controller as well as the multiloop enhanced MPC are applied to the same TCV shot. The results show that the proposed control algorithm presents a superior performance over the conventional PID algorithm in terms of convergence. Furthermore, this enhanced MPC algorithm contributes to extend the discharge length and to overcome the limited power availability restrictions that hinder the performance of advanced tokamaks.

  16. A study on the influence of corona on currents and electromagnetic fields predicted by a nonlinear lightning return-stroke model

    Science.gov (United States)

    De Conti, Alberto; Silveira, Fernando H.; Visacro, Silvério

    2014-05-01

    This paper investigates the influence of corona on currents and electromagnetic fields predicted by a return-stroke model that represents the lightning channel as a nonuniform transmission line with time-varying (nonlinear) resistance. The corona model used in this paper allows the calculation of corona currents as a function of the radial electric field in the vicinity of the channel. A parametric study is presented to investigate the influence of corona parameters, such as the breakdown electric field and the critical electric field for the stable propagation of streamers, on predicted currents and electromagnetic fields. The results show that, regardless of the assumed corona parameters, the incorporation of corona into the nonuniform and nonlinear transmission line model under investigation modifies the model predictions so that they consistently reproduce most of the typical features of experimentally observed lightning electromagnetic fields and return-stroke speed profiles. In particular, it is shown that the proposed model leads to close vertical electric fields presenting waveforms, amplitudes, and decay with distance in good agreement with dart leader electric field changes measured in triggered lightning experiments. A comparison with popular engineering return-stroke models further confirms the model's ability to predict consistent electric field waveforms in the close vicinity of the channel. Some differences observed in the field amplitudes calculated with the different models can be related to the fact that current distortion, while present in the proposed model, is ultimately neglected in the considered engineering return-stroke models.

  17. On Verifying Currents and Other Features in the Hawaiian Islands Region Using Fully Coupled Ocean/Atmosphere Mesoscale Prediction System Compared to Global Ocean Model and Ocean Observations

    Science.gov (United States)

    Jessen, P. G.; Chen, S.

    2014-12-01

    This poster introduces and evaluates features concerning the Hawaii, USA region using the U.S. Navy's fully Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS-OS™) coupled to the Navy Coastal Ocean Model (NCOM). It also outlines some challenges in verifying ocean currents in the open ocean. The system is evaluated using in situ ocean data and initial forcing fields from the operational global Hybrid Coordinate Ocean Model (HYCOM). Verification shows difficulties in modelling downstream currents off the Hawaiian islands (Hawaii's wake). Comparing HYCOM to NCOM current fields show some displacement of small features such as eddies. Generally, there is fair agreement from HYCOM to NCOM in salinity and temperature fields. There is good agreement in SSH fields.

  18. The Best Prediction Model for Trauma Outcomes of the Current Korean Population: a Comparative Study of Three Injury Severity Scoring Systems

    Directory of Open Access Journals (Sweden)

    Kyoungwon Jung

    2016-08-01

    Full Text Available Background: Injury severity scoring systems that quantify and predict trauma outcomes have not been established in Korea. This study was designed to determine the best system for use in the Korean trauma population. Methods: We collected and analyzed the data from trauma patients admitted to our institution from January 2010 to December 2014. Injury Severity Score (ISS, Revised Trauma Score (RTS, and Trauma and Injury Severity Score (TRISS were calculated based on the data from the enrolled patients. Area under the receiver operating characteristic (ROC curve (AUC for the prediction ability of each scoring system was obtained, and a pairwise comparison of ROC curves was performed. Additionally, the cut-off values were estimated to predict mortality, and the corresponding accuracy, positive predictive value, and negative predictive value were obtained. Results: A total of 7,120 trauma patients (6,668 blunt and 452 penetrating injuries were enrolled in this study. The AUCs of ISS, RTS, and TRISS were 0.866, 0.894, and 0.942, respectively, and the prediction ability of the TRISS was significantly better than the others (p < 0.001, respectively. The cut-off value of the TRISS was 0.9082, with a sensitivity of 81.9% and specificity of 92.0%; mortality was predicted with an accuracy of 91.2%; its positive predictive value was the highest at 46.8%. Conclusions: The results of our study were based on the data from one institution and suggest that the TRISS is the best prediction model of trauma outcomes in the current Korean population. Further study is needed with more data from multiple centers in Korea.

  19. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  20. Hydrological model calibration for flood prediction in current and future climates using probability distributions of observed peak flows and model based rainfall

    Science.gov (United States)

    Haberlandt, Uwe; Wallner, Markus; Radtke, Imke

    2013-04-01

    Derived flood frequency analysis based on continuous hydrological modelling is very demanding regarding the required length and temporal resolution of precipitation input data. Often such flood predictions are obtained using long precipitation time series from stochastic approaches or from regional climate models as input. However, the calibration of the hydrological model is usually done using short time series of observed data. This inconsistent employment of different data types for calibration and application of a hydrological model increases its uncertainty. Here, it is proposed to calibrate a hydrological model directly on probability distributions of observed peak flows using model based rainfall in line with its later application. Two examples are given to illustrate the idea. The first one deals with classical derived flood frequency analysis using input data from an hourly stochastic rainfall model. The second one concerns a climate impact analysis using hourly precipitation from a regional climate model. The results show that: (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated on extreme conditions works quite well for average conditions but not vice versa, (III) the calibration of the hydrological model using regional climate model data works as an implicit bias correction method and (IV) the best performance for flood estimation is usually obtained when model based precipitation and observed probability distribution of peak flows are used for model calibration.

  1. The Current State of Predicting Furrow Irrigation Erosion

    Science.gov (United States)

    There continues to be a need to predict furrow irrigation erosion to estimate on- and off-site impacts of irrigation management. The objective of this paper is to review the current state of furrow erosion prediction technology considering four models: SISL, WEPP, WinSRFR and APEX. SISL is an empiri...

  2. Numerical modeling of transformer inrush currents

    Energy Technology Data Exchange (ETDEWEB)

    Cardelli, E. [Department of Industrial Engineering, University of Perugia, I-06125 Perugia (Italy); Center for Electric and Magnetic Applied Research (Italy); Faba, A., E-mail: faba@unipg.it [Department of Industrial Engineering, University of Perugia, I-06125 Perugia (Italy); Center for Electric and Magnetic Applied Research (Italy)

    2014-02-15

    This paper presents an application of a vector hysteresis model to the prediction of the inrush current due the arbitrary initial excitation of a transformer after a fault. The approach proposed seems promising in order to predict the transient overshoot in current and the optimal time to close the circuit after the fault.

  3. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  4. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  5. Predicting the importance of current papers.

    Energy Technology Data Exchange (ETDEWEB)

    Klavans, Richard (SciTech Strategies, Inc., Berwyn, PA); Boyack, Kevin W.

    2005-01-01

    This article examines how well one can predict the importance of a current paper (a paper that is recently published in the literature). We look at three factors--journal importance, reference importance and author reputation. Citation-based measures of importance are used for all variables. We find that journal importance is the best predictor (explaining 22.3% out of a potential 29.1% of the variance in the data), and that this correlation value varies significantly by discipline. Journal importance is a better predictor of citation in Computer Science than in any other discipline. While the finding supports the present policy of using journal impact statistics as a surrogate for the importance of current papers, it calls into question the present policy of equally weighting current documents in text-based analyses. We suggest that future researchers take into account the expected importance of a document when attempting to describe the cognitive structure of a field.

  6. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  7. Helicopter Rotor Noise Prediction: Background, Current Status, and Future Direction

    Science.gov (United States)

    Brentner, Kenneth S.

    1997-01-01

    Helicopter noise prediction is increasingly important. The purpose of this viewgraph presentation is to: 1) Put into perspective the recent progress; 2) Outline current prediction capabilities; 3) Forecast direction of future prediction research; 4) Identify rotorcraft noise prediction needs. The presentation includes an historical perspective, a description of governing equations, and the current status of source noise prediction.

  8. Computer modelling of superconductive fault current limiters

    Energy Technology Data Exchange (ETDEWEB)

    Weller, R.A.; Campbell, A.M.; Coombs, T.A.; Cardwell, D.A.; Storey, R.J. [Cambridge Univ. (United Kingdom). Interdisciplinary Research Centre in Superconductivity (IRC); Hancox, J. [Rolls Royce, Applied Science Division, Derby (United Kingdom)

    1998-05-01

    Investigations are being carried out on the use of superconductors for fault current limiting applications. A number of computer programs are being developed to predict the behavior of different `resistive` fault current limiter designs under a variety of fault conditions. The programs achieve solution by iterative methods based around real measured data rather than theoretical models in order to achieve accuracy at high current densities. (orig.) 5 refs.

  9. Climate-Induced Boreal Forest Change: Predictions versus Current Observations

    Science.gov (United States)

    Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart, III; Stackhouse, Paul W., Jr.

    2007-01-01

    For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.

  10. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    CR cultural resource CRM cultural resource management CRPM Cultural Resource Predictive Modeling DoD Department of Defense ESTCP Environmental...resource management ( CRM ) legal obligations under NEPA and the NHPA, military installations need to demonstrate that CRM decisions are based on objective...maxim “one size does not fit all,” and demonstrate that DoD installations have many different CRM needs that can and should be met through a variety

  11. The prediction of the hydrodynamic performance of tidal current turbines

    International Nuclear Information System (INIS)

    Xiao, B Y; Zhou, L J; Xiao, Y X; Wang, Z W

    2013-01-01

    Nowadays tidal current energy is considered to be one of the most promising alternative green energy resources and tidal current turbines are used for power generation. Prediction of the open water performance around tidal turbines is important for the reason that it can give some advice on installation and array of tidal current turbines. This paper presents numerical computations of tidal current turbines by using a numerical model which is constructed to simulate an isolated turbine. This paper aims at studying the installation of marine current turbine of which the hydro-environmental impacts influence by means of numerical simulation. Such impacts include free-stream velocity magnitude, seabed and inflow direction of velocity. The results of the open water performance prediction show that the power output and efficiency of marine current turbine varies from different marine environments. The velocity distribution should be clearly and the suitable unit installation depth and direction be clearly chosen, which can ensure the most effective strategy for energy capture before installing the marine current turbine. The findings of this paper are expected to be beneficial in developing tidal current turbines and array in the future

  12. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

  13. State of the art in the insight into experimentally observed mesons and baryons predicted by current fundamental models of particle physics

    International Nuclear Information System (INIS)

    Bednar, M.; Hron, J.

    2001-01-01

    Current insight into mesons and baryons is highlighted, their classification is given, and their arrangement into multiplets is described. A brief chronological overview of discoveries of the most important mesons and baryons is presented, followed by a description of the attempts to find fundamental particles between mesons and baryons, which could be composed of other mesons and baryons (Fermi-Yang model, Sakata model, Ikea-Ohnuki-Ogawa model). The attempts ultimately led to the formulation of the quark model. The quark structure of some mesons and baryons is presented. The quark model foresees the existence of mesons and baryons that have not yet been discovered. (Z.J.)

  14. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  15. Development of a Nomogram Model Predicting Current Bone Scan Positivity in Patients Treated with Androgen-Deprivation Therapy for Prostate Cancer

    Science.gov (United States)

    Gotto, Geoffrey T.; Yu, Changhong; Bernstein, Melanie; Eastham, James A.; Kattan, Michael W.

    2014-01-01

    Purpose: To develop a nomogram predictive of current bone scan positivity in patients receiving androgen-deprivation therapy (ADT) for advanced prostate cancer; to augment clinical judgment and highlight patients in need of additional imaging investigations. Materials and methods: A retrospective chart review of bone scan records (conventional 99mTc-scintigraphy) of 1,293 patients who received ADT at the Memorial Sloan-Kettering Cancer Center from 2000 to 2011. Multivariable logistic regression analysis was used to identify variables suitable for inclusion in the nomogram. The probability of current bone scan positivity was determined using these variables and the predictive accuracy of the nomogram was quantified by concordance index. Results: In total, 2,681 bone scan records were analyzed and 636 patients had a positive result. Overall, the median pre-scan prostate-specific antigen (PSA) level was 2.4 ng/ml; median PSA doubling time (PSADT) was 5.8 months. At the time of a positive scan, median PSA level was 8.2 ng/ml; 53% of patients had PSA <10 ng/ml; median PSADT was 4.0 months. Five variables were included in the nomogram: number of previous negative bone scans after initiating ADT, PSA level, Gleason grade sum, and history of radical prostatectomy and radiotherapy. A concordance index value of 0.721 was calculated for the nomogram. This was a retrospective study based on limited data in patients treated in a large cancer center who underwent conventional 99mTc bone scans, which themselves have inherent limitations. Conclusion: This is the first nomogram to predict current bone scan positivity in ADT-treated prostate cancer patients, providing high predictive accuracy. PMID:25386410

  16. Development of a nomogram model predicting current bone scan positivity in patients treated with androgen-deprivation therapy for prostate cancer

    Directory of Open Access Journals (Sweden)

    Michael eKattan

    2014-10-01

    Full Text Available Purpose: To develop a nomogram predictive of current bone scan positivity in patients receiving androgen-deprivation therapy (ADT for advanced prostate cancer; to augment clinical judgment and highlight patients in need of additional imaging investigations.Materials and Methods: A retrospective chart review of bone scan records (conventional 99mTc-scintigraphy of 1,293 patients who received ADT at the Memorial Sloan-Kettering Cancer Center from 2000 to 2011. Multivariable logistic regression analysis was used to identify variables suitable for inclusion in the nomogram. The probability of current bone scan positivity was determined using these variables and the predictive accuracy of the nomogram was quantified by concordance index.Results: In total, 2,681 bone scan records were analyzed and 636 patients had a positive result. Overall, the median pre-scan prostate-specific antigen (PSA level was 2.4 ng/ml; median PSA doubling time (PSADT was 5.8 months. At the time of a positive scan, median PSA level was 8.2 ng/ml; 53% of patients had PSA <10 ng/ml; median PSADT was 4.0 months. Five variables were included in the nomogram: number of previous negative bone scans after initiating ADT, PSA level, Gleason grade sum, and history of radical prostatectomy and radiotherapy. A concordance index value of 0.721 was calculated for the nomogram. This was a retrospective study based on limited data in patients treated in a large cancer centre who underwent conventional 99mTc bone scans, which themselves have inherent limitations. Conclusions: This is the first nomogram to predict current bone scan positivity in ADT-treated prostate cancer patients, providing high predictive accuracy.

  17. Workshop on Current Issues in Predictive Approaches to Intelligence and Security Analytics: Fostering the Creation of Decision Advantage through Model Integration and Evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.

    2010-05-23

    The increasing asymmetric nature of threats to the security, health and sustainable growth of our society requires that anticipatory reasoning become an everyday activity. Currently, the use of anticipatory reasoning is hindered by the lack of systematic methods for combining knowledge- and evidence-based models, integrating modeling algorithms, and assessing model validity, accuracy and utility. The workshop addresses these gaps with the intent of fostering the creation of a community of interest on model integration and evaluation that may serve as an aggregation point for existing efforts and a launch pad for new approaches.

  18. Analytic modeling of axisymmetric disruption halo currents

    International Nuclear Information System (INIS)

    Humphreys, D.A.; Kellman, A.G.

    1999-01-01

    Currents which can flow in plasma facing components during disruptions pose a challenge to the design of next generation tokamaks. Induced toroidal eddy currents and both induced and conducted poloidal ''halo'' currents can produce design-limiting electromagnetic loads. While induction of toroidal and poloidal currents in passive structures is a well-understood phenomenon, the driving terms and scalings for poloidal currents flowing on open field lines during disruptions are less well established. A model of halo current evolution is presented in which the current is induced in the halo by decay of the plasma current and change in enclosed toroidal flux while being convected into the halo from the core by plasma motion. Fundamental physical processes and scalings are described in a simplified analytic version of the model. The peak axisymmetric halo current is found to depend on halo and core plasma characteristics during the current quench, including machine and plasma dimensions, resistivities, safety factor, and vertical stability growth rate. Two extreme regimes in poloidal halo current amplitude are identified depending on the minimum halo safety factor reached during the disruption. A 'type I' disruption is characterized by a minimum safety factor that remains relatively high (typically 2 - 3, comparable to the predisruption safety factor), and a relatively low poloidal halo current. A 'type II' disruption is characterized by a minimum safety factor comparable to unity and a relatively high poloidal halo current. Model predictions for these two regimes are found to agree well with halo current measurements from vertical displacement event disruptions in DIII-D [T. S. Taylor, K. H. Burrell, D. R. Baker, G. L. Jackson, R. J. La Haye, M. A. Mahdavi, R. Prater, T. C. Simonen, and A. D. Turnbull, open-quotes Results from the DIII-D Scientific Research Program,close quotes in Proceedings of the 17th IAEA Fusion Energy Conference, Yokohama, 1998, to be published in

  19. On tridimensional rip current modeling

    Science.gov (United States)

    Marchesiello, Patrick; Benshila, Rachid; Almar, Rafael; Uchiyama, Yusuke; McWilliams, James C.; Shchepetkin, Alexander

    2015-12-01

    Do lateral shear instabilities of nearshore circulation account for a substantial part of Very Low-Frequency (VLF) variability? If yes, it would promote stirring and mixing of coastal waters and surf-shelf exchanges. Another question is whether tridimensional transient processes are important for instability generation. An innovative modeling system with tridimensional wave-current interactions was designed to investigate transient nearshore currents and interactions between nearshore and innershelf circulations. We present here some validation of rip current modeling for the Aquitanian coast of France, using in-situ and remote video sensing. We then proceed to show the benefits of 3D versus 2D (depth-mean flow) modeling of rip currents and their low-frequency variability. It appears that a large part of VLF motions is due to intrinsic variability of the tridimensional flow. 3D models may thus provide a valuable, only marginally more expensive alternative to conventional 2D approaches that miss the vertical flow structure and its nonlinear interaction with the depth-averaged flow.

  20. A novel modeling to predict the critical current behavior of Nb$_{3}$Sn PIT strand under transverse load based on a scaling law and Finite Element Analysis

    CERN Document Server

    Wang, Tiening; Takayasu, Makoto; Bordini, Bernardo

    2014-01-01

    Superconducting Nb$_{3}$Sn Powder-In-Tube (PIT) strands could be used for the superconducting magnets of the next generation Large Hadron Collider. The strands are cabled into the typical flat Rutherford cable configuration. During the assembly of a magnet and its operation the strands experience not only longitudinal but also transverse load due to the pre-compression applied during the assembly and the Lorentz load felt when the magnets are energized. To properly design the magnets and guarantee their safe operation, mechanical load effects on the strand superconducting properties are studied extensively; particularly, many scaling laws based on tensile load experiments have been established to predict the critical current dependence on strain. However, the dependence of the superconducting properties on transverse load has not been extensively studied so far. One of the reasons is that transverse loading experiments are difficult to conduct due to the small diameter of the strand (about 1 mm) and the data ...

  1. New horizons in predictive toxicology: current status and application

    National Research Council Canada - National Science Library

    Wilson, A. G. E

    2012-01-01

    "In this comprehensive discussion of predictive toxicology and its applications, leading experts express their views on the technologies currently available and the potential for future developments...

  2. Circulation-based Modeling of Gravity Currents

    Science.gov (United States)

    Meiburg, E. H.; Borden, Z.

    2013-05-01

    Atmospheric and oceanic flows driven by predominantly horizontal density differences, such as sea breezes, thunderstorm outflows, powder snow avalanches, and turbidity currents, are frequently modeled as gravity currents. Efforts to develop simplified models of such currents date back to von Karman (1940), who considered a two-dimensional gravity current in an inviscid, irrotational and infinitely deep ambient. Benjamin (1968) presented an alternative model, focusing on the inviscid, irrotational flow past a gravity current in a finite-depth channel. More recently, Shin et al. (2004) proposed a model for gravity currents generated by partial-depth lock releases, considering a control volume that encompasses both fronts. All of the above models, in addition to the conservation of mass and horizontal momentum, invoke Bernoulli's law along some specific streamline in the flow field, in order to obtain a closed system of equations that can be solved for the front velocity as function of the current height. More recent computational investigations based on the Navier-Stokes equations, on the other hand, reproduce the dynamics of gravity currents based on the conservation of mass and momentum alone. We propose that it should therefore be possible to formulate a fundamental gravity current model without invoking Bernoulli's law. The talk will show that the front velocity of gravity currents can indeed be predicted as a function of their height from mass and momentum considerations alone, by considering the evolution of interfacial vorticity. This approach does not require information on the pressure field and therefore avoids the need for an energy closure argument such as those invoked by the earlier models. Predictions by the new theory are shown to be in close agreement with direct numerical simulation results. References Von Karman, T. 1940 The engineer grapples with nonlinear problems, Bull. Am. Math Soc. 46, 615-683. Benjamin, T.B. 1968 Gravity currents and related

  3. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

  4. Predictability of the California Current System

    Science.gov (United States)

    Miller, Arthur J.; Chereskin, T.; Cornuelle, B. D.; Niiler, P. P.; Moisan, J. R.; Lindstrom, Eric (Technical Monitor)

    2001-01-01

    The physical and biological oceanography of the Southern California Bight (SCB), a highly productive subregion of the California Current System (CCS) that extends from Point Conception, California, south to Ensenada, Mexico, continues to be extensively studied. For example, the California Cooperative Oceanic Fisheries Investigations (CalCOFI) program has sampled this region for over 50 years, providing an unparalleled time series of physical and biological data. However, our understanding of what physical processes control the large-scale and mesoscale variations in these properties is incomplete. In particular, the non-synoptic and relatively coarse spatial sampling (70km) of the hydrographic grid does not completely resolve the mesoscale eddy field (Figure 1a). Moreover, these unresolved physical variations exert a dominant influence on the evolution of the ecosystem. In recent years, additional datasets that partially sample the SCB have become available. Acoustic Doppler Current Profiler (ADCP) measurements, which now sample upper-ocean velocity between stations, and sea level observations along TOPEX tracks give a more complete picture of the mesoscale variability. However, both TOPEX and ADCP are well-sampled only along the cruise or orbit tracks and coarsely sampled in time and between tracks. Surface Lagrangian drifters also sample the region, although irregularly in time and space. SeaWiFS provides estimates of upper-ocean chlorophyll-a (chl-alpha), usually giving nearly complete coverage for week-long intervals, depending on cloud coverage. Historical ocean color data from the Coastal Zone Color Scanner (CZCS) has been used extensively to determine phytoplankton patterns and variability, characterize the primary production across the SCB coastal fronts, and describe the seasonal and interannual variability in pigment concentrations. As in CalCOFI, these studies described much of the observed structures and their variability over relatively large space and

  5. Applying machine learning to predict patient-specific current CD 4 ...

    African Journals Online (AJOL)

    This work shows the application of machine learning to predict current CD4 cell count of an HIV-positive patient using genome sequences, viral load and time. A regression model predicting actual CD4 cell counts and a classification model predicting if a patient's CD4 cell count is less than 200 was built using a support ...

  6. Application of the Min-Projection and the Model Predictive Strategies for Current Control of Three-Phase Grid-Connected Converters: a Comparative Study

    Directory of Open Access Journals (Sweden)

    M. Oloumi

    2015-06-01

    Full Text Available This paper provides a detailed comparative study concerning the performance of min-projection strategy (MPS and model predictive control (MPC systems to control the three-phase grid connected converters. To do so, first, the converter is modeled as a switched linear system. Then, the feasibility of the MPS technique is investigated and its stability criterion is derived as a lower limit on the DC link voltage. Next, the fundamental equations of the MPS to control a VSC are obtained in the stationary reference frame. The mathematical analysis reveals that the MPS is independent of the load, grid, filter and converter parameters. This feature is a great advantage of MPS over the MPC approach. However, the latter is a well-known model-based control technique, has already developed for controlling the VSC in the stationary reference frame. To control the grid connected VSC, both MPS and MPC approaches are simulated in the PSCAD/EMTDC environment. Simulation results illustrate that the MPS is functioning well and is less sensitive to grid and filter inductances as well as the DC link voltage level. However, the MPC approach renders slightly a better performance in the steady state conditions.

  7. Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

    Directory of Open Access Journals (Sweden)

    Wenbiao Li

    2016-04-01

    Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

  8. Predictive Toxicology: Current Status and Future Outlook (EBI ...

    Science.gov (United States)

    Slide presentation at the EBI-EMBL Industry Programme Workshop on Predictive Toxicology and the currently status of Computational Toxicology activities at the US EPA. Slide presentation at the EBI-EMBL Industry Programme Workshop on Predictive Toxicology and the currently status of Computational Toxicology activities at the US EPA.

  9. Physiologically based pharmacokinetic modeling of human exposure to perfluorooctanoic acid suggests historical non drinking-water exposures are important for predicting current serum concentrations.

    Science.gov (United States)

    Worley, Rachel Rogers; Yang, Xiaoxia; Fisher, Jeffrey

    2017-09-01

    Manufacturing of perfluorooctanoic acid (PFOA), a synthetic chemical with a long half-life in humans, peaked between 1970 and 2002, and has since diminished. In the United States, PFOA is detected in the blood of >99% of people tested, but serum concentrations have decreased since 1999. Much is known about exposure to PFOA in drinking water; however, the impact of non-drinking water PFOA exposure on serum PFOA concentrations is not well characterized. The objective of this research is to apply physiologically based pharmacokinetic (PBPK) modeling and Monte Carlo analysis to evaluate the impact of historic non-drinking water PFOA exposure on serum PFOA concentrations. In vitro to in vivo extrapolation was utilized to inform descriptions of PFOA transport in the kidney. Monte Carlo simulations were incorporated to evaluate factors that account for the large inter-individual variability of serum PFOA concentrations measured in individuals from North Alabama in 2010 and 2016, and the Mid-Ohio River Valley between 2005 and 2008. Predicted serum PFOA concentrations were within two-fold of experimental data. With incorporation of Monte Carlo simulations, the model successfully tracked the large variability of serum PFOA concentrations measured in populations from the Mid-Ohio River Valley. Simulation of exposure in a population of 45 adults from North Alabama successfully predicted 98% of individual serum PFOA concentrations measured in 2010 and 2016, respectively, when non-drinking water ingestion of PFOA exposure was included. Variation in serum PFOA concentrations may be due to inter-individual variability in the disposition of PFOA and potentially elevated historical non-drinking water exposures. Published by Elsevier Inc.

  10. PREDICTED PERCENTAGE DISSATISFIED (PPD) MODEL ...

    African Journals Online (AJOL)

    HOD

    their low power requirements, are relatively cheap and are environment friendly. ... PREDICTED PERCENTAGE DISSATISFIED MODEL EVALUATION OF EVAPORATIVE COOLING ... The performance of direct evaporative coolers is a.

  11. Extended Range Prediction of Indian Summer Monsoon: Current status

    Science.gov (United States)

    Sahai, A. K.; Abhilash, S.; Borah, N.; Joseph, S.; Chattopadhyay, R.; S, S.; Rajeevan, M.; Mandal, R.; Dey, A.

    2014-12-01

    The main focus of this study is to develop forecast consensus in the extended range prediction (ERP) of monsoon Intraseasonal oscillations using a suit of different variants of Climate Forecast system (CFS) model. In this CFS based Grand MME prediction system (CGMME), the ensemble members are generated by perturbing the initial condition and using different configurations of CFSv2. This is to address the role of different physical mechanisms known to have control on the error growth in the ERP in the 15-20 day time scale. The final formulation of CGMME is based on 21 ensembles of the standalone Global Forecast System (GFS) forced with bias corrected forecasted SST from CFS, 11 low resolution CFST126 and 11 high resolution CFST382. Thus, we develop the multi-model consensus forecast for the ERP of Indian summer monsoon (ISM) using a suite of different variants of CFS model. This coordinated international effort lead towards the development of specific tailor made regional forecast products over Indian region. Skill of deterministic and probabilistic categorical rainfall forecast as well the verification of large-scale low frequency monsoon intraseasonal oscillations has been carried out using hindcast from 2001-2012 during the monsoon season in which all models are initialized at every five days starting from 16May to 28 September. The skill of deterministic forecast from CGMME is better than the best participating single model ensemble configuration (SME). The CGMME approach is believed to quantify the uncertainty in both initial conditions and model formulation. Main improvement is attained in probabilistic forecast which is because of an increase in the ensemble spread, thereby reducing the error due to over-confident ensembles in a single model configuration. For probabilistic forecast, three tercile ranges are determined by ranking method based on the percentage of ensemble members from all the participating models falls in those three categories. CGMME further

  12. Mental models accurately predict emotion transitions.

    Science.gov (United States)

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  13. Mental models accurately predict emotion transitions

    Science.gov (United States)

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  14. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  15. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... signal based on a process model, coping with constraints on inputs and ... paper, we will present an introduction to the theory and application of MPC with Matlab codes ... section 5 presents the simulation results and section 6.

  16. QCD predictions for weak neutral current structure functions

    International Nuclear Information System (INIS)

    Wu Jimin

    1987-01-01

    Employing the analytic expression (to the next leading order) for non-singlet component of structure function which the author got from QCD theory and putting recent experiment result of neutral current structure function at Q 2 = 11 (GeV/C) 2 as input, the QCD prediction for neutral current structure function of their scaling violation behaviours was given

  17. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  19. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  20. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  1. Predictive Modeling in Race Walking

    Directory of Open Access Journals (Sweden)

    Krzysztof Wiktorowicz

    2015-01-01

    Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

  2. Current meter data from moored current meter casts in the Northeast Pacific Ocean as part of the Ocean Prediction Through Observation Modeling and Analysis (OPTOMA) project, 1984-09-26 to 1985-07-16 (NODC Accession 9600075)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Current meter data were collected using moored current meter casts in the Northeast Pacific Ocean from September 26, 1984 to July 16, 1985. Data were submitted by...

  3. Computer modelling of eddy current probes

    International Nuclear Information System (INIS)

    Sullivan, S.P.

    1992-01-01

    Computer programs have been developed for modelling impedance and transmit-receive eddy current probes in two-dimensional axis-symmetric configurations. These programs, which are based on analytic equations, simulate bobbin probes in infinitely long tubes and surface probes on plates. They calculate probe signal due to uniform variations in conductor thickness, resistivity and permeability. These signals depend on probe design and frequency. A finite element numerical program has been procured to calculate magnetic permeability in non-linear ferromagnetic materials. Permeability values from these calculations can be incorporated into the above analytic programs to predict signals from eddy current probes with permanent magnets in ferromagnetic tubes. These programs were used to test various probe designs for new testing applications. Measurements of magnetic permeability in magnetically biased ferromagnetic materials have been performed by superimposing experimental signals, from special laboratory ET probes, on impedance plane diagrams calculated using these programs. (author). 3 refs., 2 figs

  4. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  5. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  6. A THICK CURRENT SHELL MODEL

    African Journals Online (AJOL)

    2005-08-25

    Aug 25, 2005 ... control ling the current distribution latitudinally and vertically ... horizontal plane (v 3 constant) as a result oF the ... optimisation subroutine of licensed MATLAB 6.0 ... electrojct axis does not coincide with the dip equator.

  7. Predictability of surface currents and fronts off the Mississippi Delta

    International Nuclear Information System (INIS)

    Walker, N.D.; Rouse, L.J.; Wiseman, W.J.

    2001-01-01

    The dynamic coastal region of the lower Mississippi River was examined under varying conditions of wind, river discharge and circulation patterns of the Gulf of Mexico. Nearly 7,000 deep-sea merchant vessels enter the port complex each year and the area boasts the highest concentration of offshore drilling rigs, rendering the Mississippi delta and adjacent coastal areas vulnerable to risk from oil spills. Satellite imagery has been useful in tracking movements of the Mississippi river plume as recognizable turbidity and temperature fronts are formed where river waters encounter ambient shelf waters. Oil spill modelers often base their predictions of oil movement on the surface wind field and surface currents, but past studies have indicated that this can be overly simplistic in regions affected by river flow because river fronts have significant control over the movement of oil in opposition to prevailing winds. Frontal zones, such as those found where river waters meet oceanic waters, are characterized by strong convergence of surface flow. These frontal zones can provide large and efficient traps or natural booms for spilled oil. In an effort to facilitate cleanup operations, this study made use of the National Ocean and Atmospheric Administration (NOAA) AVHRR satellite imagery of temperature and reflectance to study front locations and their variability in space and time. The main objectives were to quantify surface temperature structure and locations of fronts throughout the year using satellite image data, to map the structure of the Mississippi sediment plume and to assess the forcing factors responsible for its variability over space and time. The final objective was to use in-situ measurements of surface currents together with satellite image data to better understand surface flow in this region of strong and variable currents. It was concluded that the main factors controlling circulation in the Mississippi River outflow region are river discharge and

  8. Electric current model of magnetosphere

    International Nuclear Information System (INIS)

    Alfen, H.

    1979-05-01

    A dualism between the field and the particle approach exists also in plasma physics. A number of phenomena, such as the formation of double layers and the energy transport form one region to another, can be understood only by the particle (electric current) description. Hence a translation of the traditional field description into a particle (electric current) description is essential. Such a translation has earlier been made for the heliosphere. The purpose of this paper is to outline a similar application to the magnetosphere, focussing on the energy transfer from the solar wind. As a first approximation a magnetic field consisting of a dipole field and homogeneous magnetic field is used whereas in a second approximation the configuration is more realistic. (author)

  9. Modeling of Tsunami Currents in Harbors

    Science.gov (United States)

    Lynett, P. J.

    2010-12-01

    Extreme events, such as large wind waves and tsunamis, are well recognized as a damaging hazard to port and harbor facilities. Wind wave events, particularly those with long period spectral components or infragravity wave generation, can excite resonance inside harbors leading to both large vertical motions and strong currents. Tsunamis can cause great damage as well. The geometric amplification of these very long waves can create large vertical motions in the interior of a harbor. Additionally, if the tsunami is composed of a train of long waves, which it often is, resonance can be easily excited. These long wave motions create strong currents near the node locations of resonant motions, and when interacting with harbor structures such as breakwaters, can create intense turbulent rotational structures, typical in the form of large eddies or gyres. These gyres have tremendous transport potential, and have been observed to break mooring lines, and even cause ships to be trapped inside the rotation, moving helplessly with the flow until collision, grounding, or dissipation of the eddy (e.g. Okal et al., 2006). This presentation will introduce the traditional theory used to predict wave impacts on harbors, discussing both how these models are practically useful and in what types of situations require a more accurate tool. State-of-the-art numerical models will be introduced, with a focus on recent developments in Boussinesq-type modeling. The Boussinesq equations model can account the dispersive, turbulent and rotational flow properties frequently observed in nature. Also they have the ability to coupling currents and waves and can predict nonlinear wave propagation over uneven bottom from deep (or intermediate) water area to shallow water area. However, during the derivation of a 2D-horizontal equation set, some 3D flow features, such those driven by as the dispersive stresses and the effects of the unresolved small scale 3D turbulence, are excluded. Consequently

  10. Computational prediction of chemical reactions: current status and outlook.

    Science.gov (United States)

    Engkvist, Ola; Norrby, Per-Ola; Selmi, Nidhal; Lam, Yu-Hong; Peng, Zhengwei; Sherer, Edward C; Amberg, Willi; Erhard, Thomas; Smyth, Lynette A

    2018-06-01

    Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Prediction of Tidal Elevations and Barotropic Currents in the Gulf of Bone

    Science.gov (United States)

    Purnamasari, Rika; Ribal, Agustinus; Kusuma, Jeffry

    2018-03-01

    Tidal elevation and barotropic current predictions in the gulf of Bone have been carried out in this work based on a two-dimensional, depth-integrated Advanced Circulation (ADCIRC-2DDI) model for 2017. Eight tidal constituents which were obtained from FES2012 have been imposed along the open boundary conditions. However, even using these very high-resolution tidal constituents, the discrepancy between the model and the data from tide gauge is still very high. In order to overcome such issues, Green’s function approach has been applied which reduced the root-mean-square error (RMSE) significantly. Two different starting times are used for predictions, namely from 2015 and 2016. After improving the open boundary conditions, RMSE between observation and model decreased significantly. In fact, RMSEs for 2015 and 2016 decreased 75.30% and 88.65%, respectively. Furthermore, the prediction for tidal elevations as well as tidal current, which is barotropic current, is carried out. This prediction was compared with the prediction conducted by Geospatial Information Agency (GIA) of Indonesia and we found that our prediction is much better than one carried out by GIA. Finally, since there is no tidal current observation available in this area, we assume that, when tidal elevations have been fixed, then the tidal current will approach the actual current velocity.

  12. Measurement of neoclassically predicted edge current density at ASDEX Upgrade

    Science.gov (United States)

    Dunne, M. G.; McCarthy, P. J.; Wolfrum, E.; Fischer, R.; Giannone, L.; Burckhart, A.; the ASDEX Upgrade Team

    2012-12-01

    Experimental confirmation of neoclassically predicted edge current density in an ELMy H-mode plasma is presented. Current density analysis using the CLISTE equilibrium code is outlined and the rationale for accuracy of the reconstructions is explained. Sample profiles and time traces from analysis of data at ASDEX Upgrade are presented. A high time resolution is possible due to the use of an ELM-synchronization technique. Additionally, the flux-surface-averaged current density is calculated using a neoclassical approach. Results from these two separate methods are then compared and are found to validate the theoretical formula. Finally, several discharges are compared as part of a fuelling study, showing that the size and width of the edge current density peak at the low-field side can be explained by the electron density and temperature drives and their respective collisionality modifications.

  13. Measurement of neoclassically predicted edge current density at ASDEX Upgrade

    International Nuclear Information System (INIS)

    Dunne, M.G.; McCarthy, P.J.; Wolfrum, E.; Fischer, R.; Giannone, L.; Burckhart, A.

    2012-01-01

    Experimental confirmation of neoclassically predicted edge current density in an ELMy H-mode plasma is presented. Current density analysis using the CLISTE equilibrium code is outlined and the rationale for accuracy of the reconstructions is explained. Sample profiles and time traces from analysis of data at ASDEX Upgrade are presented. A high time resolution is possible due to the use of an ELM-synchronization technique. Additionally, the flux-surface-averaged current density is calculated using a neoclassical approach. Results from these two separate methods are then compared and are found to validate the theoretical formula. Finally, several discharges are compared as part of a fuelling study, showing that the size and width of the edge current density peak at the low-field side can be explained by the electron density and temperature drives and their respective collisionality modifications. (paper)

  14. Historical Trust Levels Predict Current Welfare State Design

    DEFF Research Database (Denmark)

    Bergh, Andreas; Bjørnskov, Christian

    Using cross-sectional data for 76 countries, we apply instrumental variable techniques based on pronoun drop, temperature and monarchies to demonstrate that historical trust levels predict several indicators of current welfare state design, including universalism and high levels of regulatory...... freedom. We argue that high levels of trust and trustworthiness are necessary, but not sufficient, conditions for societies to develop successful universal welfare states that would otherwise be highly vulnerable to free riding and fraudulent behavior. Our results do not exclude positive feedback from...... welfare state universalism to individual trust, although we claim that the important causal link runs from historically trust levels to current welfare state design....

  15. Towards an Airframe Noise Prediction Methodology: Survey of Current Approaches

    Science.gov (United States)

    Farassat, Fereidoun; Casper, Jay H.

    2006-01-01

    In this paper, we present a critical survey of the current airframe noise (AFN) prediction methodologies. Four methodologies are recognized. These are the fully analytic method, CFD combined with the acoustic analogy, the semi-empirical method and fully numerical method. It is argued that for the immediate need of the aircraft industry, the semi-empirical method based on recent high quality acoustic database is the best available method. The method based on CFD and the Ffowcs William- Hawkings (FW-H) equation with penetrable data surface (FW-Hpds ) has advanced considerably and much experience has been gained in its use. However, more research is needed in the near future particularly in the area of turbulence simulation. The fully numerical method will take longer to reach maturity. Based on the current trends, it is predicted that this method will eventually develop into the method of choice. Both the turbulence simulation and propagation methods need to develop more for this method to become useful. Nonetheless, the authors propose that the method based on a combination of numerical and analytical techniques, e.g., CFD combined with FW-H equation, should also be worked on. In this effort, the current symbolic algebra software will allow more analytical approaches to be incorporated into AFN prediction methods.

  16. Ecological models and pesticide risk assessment: current modeling practice.

    Science.gov (United States)

    Schmolke, Amelie; Thorbek, Pernille; Chapman, Peter; Grimm, Volker

    2010-04-01

    Ecological risk assessments of pesticides usually focus on risk at the level of individuals, and are carried out by comparing exposure and toxicological endpoints. However, in most cases the protection goal is populations rather than individuals. On the population level, effects of pesticides depend not only on exposure and toxicity, but also on factors such as life history characteristics, population structure, timing of application, presence of refuges in time and space, and landscape structure. Ecological models can integrate such factors and have the potential to become important tools for the prediction of population-level effects of exposure to pesticides, thus allowing extrapolations, for example, from laboratory to field. Indeed, a broad range of ecological models have been applied to chemical risk assessment in the scientific literature, but so far such models have only rarely been used to support regulatory risk assessments of pesticides. To better understand the reasons for this situation, the current modeling practice in this field was assessed in the present study. The scientific literature was searched for relevant models and assessed according to nine characteristics: model type, model complexity, toxicity measure, exposure pattern, other factors, taxonomic group, risk assessment endpoint, parameterization, and model evaluation. The present study found that, although most models were of a high scientific standard, many of them would need modification before they are suitable for regulatory risk assessments. The main shortcomings of currently available models in the context of regulatory pesticide risk assessments were identified. When ecological models are applied to regulatory risk assessments, we recommend reviewing these models according to the nine characteristics evaluated here. (c) 2010 SETAC.

  17. Superconducting current in a bisoliton superconductivity model

    International Nuclear Information System (INIS)

    Ermakov, V.N.; Kruchinin, S.P.; Ponezha, E.A.

    1991-01-01

    It is shown that the transition into a superconducting state with the current which is described by a bisoliton superconductivity model is accompanied by the deformation of the spectrum of one-particle states of the current carriers. The deformation value is proportional to the conducting current force. The residuaby resistance in such state is absent

  18. Combined kinetic and transport modeling of radiofrequency current drive

    International Nuclear Information System (INIS)

    Dumont, R.; Giruzzi, G.; Barbato, E.

    2000-07-01

    A numerical model for predictive simulations of radiofrequency current drive in magnetically confined plasmas is developed. It includes the minimum requirements for a self consistent description of such regimes, i.e., a 3-D ,kinetic equation for the electron distribution function, 1-D heat and current transport equations, and resonant coupling between velocity space and configuration space dynamics, through suitable wave propagation equations. The model finds its full application in predictive studies of complex current profile control scenarios in tokamaks, aiming at the establishment of internal transport barriers by the simultaneous use of various radiofrequency current drive methods. The basic properties of this non-linear numerical system are investigated and illustrated by simulations applied to reversed magnetic shear regimes obtained by Lower Hybrid and Electron Cyclotron current drive for parameters typical of the Tore Supra tokamak. (authors)

  19. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  20. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  1. Where the wild things are: Predicting hotspots of seabird aggregations in the California Current System

    Science.gov (United States)

    Nur, N.; Jahncke, J.; Herzog, M.P.; Howar, J.; Hyrenbach, K.D.; Zamon, J.E.; Ainley, D.G.; Wiens, J.A.; Morgan, K.; Balance, L.T.; Stralberg, D.

    2011-01-01

    Marine Protected Areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds ("hotspot????). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ("core area????) to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots

  2. Unified model of current-hadronic interactions. II

    International Nuclear Information System (INIS)

    Moffat, J.W.; Wright, A.C.D.

    1975-01-01

    An analytic model of current-hadronic interactions is used to make predictions which are compared with recent data for vector-meson electroproduction and for the spin density matrix of photoproduced rho 0 mesons. The rho 0 and ω electroproduction cross sections are predicted to behave differently as the mass of the virtual photon varies; the diffraction peak broadens with increasing -q 2 at fixed ν and narrows with increasing energy. The predicted rho 0 density matrix elements do not possess the approximate s-channel helicity conservation seen experimentally. The model is continued to the inclusive electron-positron annihilation region, where parameter-free predictions are given for the inclusive prosess e + + e - → p + hadrons. The annihilation structure functions are found to have nontrivial scale-invariance limits. By using total cross-section data for e + e - annihilation into hardrons, we predict the mean multiplicity for the production of nucleons

  3. Transport critical current density in flux creep model

    International Nuclear Information System (INIS)

    Wang, J.; Taylor, K.N.R.; Russell, G.J.; Yue, Y.

    1992-01-01

    The magnetic flux creep model has been used to derive the temperature dependence of the critical current density in high temperature superconductors. The generally positive curvature of the J c -T diagram is predicted in terms of two interdependent dimensionless fitting parameters. In this paper, the results are compared with both SIS and SNS junction models of these granular materials, neither of which provides a satisfactory prediction of the experimental data. A hybrid model combining the flux creep and SNS mechanisms is shown to be able to account for the linear regions of the J c -T behavior which are observed in some materials

  4. Disturbance estimator based predictive current control of grid-connected inverters

    OpenAIRE

    Al-Khafaji, Ahmed Samawi Ghthwan

    2013-01-01

    ABSTRACT: The work presented in my thesis considers one of the modern discrete-time control approaches based on digital signal processing methods, that have been developed to improve the performance control of grid-connected three-phase inverters. Disturbance estimator based predictive current control of grid-connected inverters is proposed. For inverter modeling with respect to the design of current controllers, we choose the d-q synchronous reference frame to make it easier to understand an...

  5. Predictive Models for Carcinogenicity and Mutagenicity ...

    Science.gov (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  6. Students’ mental model in electric current

    Science.gov (United States)

    Pramesti, Y. S.; Setyowidodo, I.

    2018-05-01

    Electricity is one of essential topic in learning physics. This topic was studied in elementary until university level. Although electricity was related to our daily activities, but it doesn’t ensure that students have the correct concept. The aim of this research was to investigate and then categorized the students’ mental model. Subject consisted of 59 students of mechanical engineering that studied Physics for Engineering. This study was used a qualitative approach that used in this research is phenomenology. Data were analyzed qualitatively by using pre-test, post-test, and investigation for discovering further information. Three models were reported, showing a pattern which related to individual way of thinking about electric current. The mental model that was discovered in this research are: 1) electric current as a flow; 2) electric current as a source of energy, 3) electric current as a moving charge.

  7. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  8. Experimental modeling of eddy current inspection capabilities

    International Nuclear Information System (INIS)

    Junker, W.R.; Clark, W.G.

    1984-01-01

    This chapter examines the experimental modeling of eddy current inspection capabilities based upon the use of liquid mercury samples designed to represent metal components containing discontinuities. A brief summary of past work with mercury modeling and a detailed discussion of recent experiments designed to further evaluate the technique are presented. The main disadvantages of the mercury modeling concept are that mercury is toxic and must be handled carefully, liquid mercury can only be used to represent nonferromagnetic materials, and wetting and meniscus problems can distort the effective size of artificial discontinuities. Artificial discontinuities placed in a liquid mercury sample can be used to represent discontinuities in solid metallic structures. Discontinuity size and type cannot be characterized from phase angle and signal amplitude data developed with a surface scanning, pancake-type eddy current probe. It is concluded that the mercury model approach can greatly enhance the overall understanding and applicability of eddy current inspection techniques

  9. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  10. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  11. Current density and continuity in discretized models

    International Nuclear Information System (INIS)

    Boykin, Timothy B; Luisier, Mathieu; Klimeck, Gerhard

    2010-01-01

    Discrete approaches have long been used in numerical modelling of physical systems in both research and teaching. Discrete versions of the Schroedinger equation employing either one or several basis functions per mesh point are often used by senior undergraduates and beginning graduate students in computational physics projects. In studying discrete models, students can encounter conceptual difficulties with the representation of the current and its divergence because different finite-difference expressions, all of which reduce to the current density in the continuous limit, measure different physical quantities. Understanding these different discrete currents is essential and requires a careful analysis of the current operator, the divergence of the current and the continuity equation. Here we develop point forms of the current and its divergence valid for an arbitrary mesh and basis. We show that in discrete models currents exist only along lines joining atomic sites (or mesh points). Using these results, we derive a discrete analogue of the divergence theorem and demonstrate probability conservation in a purely localized-basis approach.

  12. Climate Prediction Center - Outlooks: Current UV Index Forecast Map

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Service NOAA Center for Weather and Climate Prediction Climate Prediction Center 5830 University Research Court College Park, Maryland 20740 Page Author: Climate Prediction Center Internet Team Disclaimer

  13. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    NARCIS (Netherlands)

    Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

    2014-01-01

    In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

  14. MASCOTTE: analytical model of eddy current signals

    International Nuclear Information System (INIS)

    Delsarte, G.; Levy, R.

    1992-01-01

    Tube examination is a major application of the eddy current technique in the nuclear and petrochemical industries. Such examination configurations being specially adapted to analytical modes, a physical model is developed on portable computers. It includes simple approximations made possible by the effective conditions of the examinations. The eddy current signal is described by an analytical formulation that takes into account the tube dimensions, the sensor conception, the physical characteristics of the defect and the examination parameters. Moreover, the model makes it possible to associate real signals and simulated signals

  15. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  16. Adaptive Maneuvering Frequency Method of Current Statistical Model

    Institute of Scientific and Technical Information of China (English)

    Wei Sun; Yongjian Yang

    2017-01-01

    Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.

  17. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  18. Current definition and a generalized federbush model

    International Nuclear Information System (INIS)

    Singh, L.P.S.; Hagen, C.R.

    1978-01-01

    The Federbush model is studied, with particular attention being given to the definition of currents. Inasmuch as there is no a priori restriction of local gauge invariance, the currents in the interacting case can be defined more generally than in Q.E.D. It is found that two arbitrary parameters are thereby introduced into the theory. Lowest order perturbation calculations for the current correlation functions and the Fermion propagators indicate that the theory admits a whole class of solutions dependent upon these parameters with the closed solution of Federbush emerging as a special case. The theory is shown to be locally covariant, and a conserved energy--momentum tensor is displayed. One finds in addition that the generators of gauge transformations for the fields are conserved. Finally it is shown that the general theory yields the Federbush solution if suitable Thirring model type counterterms are added

  19. Virtual Universities: Current Models and Future Trends.

    Science.gov (United States)

    Guri-Rosenblit, Sarah

    2001-01-01

    Describes current models of distance education (single-mode distance teaching universities, dual- and mixed-mode universities, extension services, consortia-type ventures, and new technology-based universities), including their merits and problems. Discusses future trends in potential student constituencies, faculty roles, forms of knowledge…

  20. Microscopic models for bridging electrostatics and currents

    Science.gov (United States)

    Borghi, L.; DeAmbrosis, A.; Mascheretti, P.

    2007-03-01

    A teaching sequence based on the use of microscopic models to link electrostatic phenomena with direct currents is presented. The sequence, devised for high school students, was designed after initial work carried out with student teachers attending a school of specialization for teaching physics at high school, at the University of Pavia. The results obtained with them are briefly presented, because they directed our steps for the development of the teaching sequence. For both the design of the experiments and their interpretation, we drew inspiration from the original works of Alessandro Volta; in addition, a structural model based on the particular role of electrons as elementary charges both in electrostatic phenomena and in currents was proposed. The teaching sequence starts from experiments on charging objects by rubbing and by induction, and engages students in constructing microscopic models to interpret their observations. By using these models and by closely examining the ideas of tension and capacitance, the students acknowledge that a charging (or discharging) process is due to the motion of electrons that, albeit for short time intervals, represent a current. Finally, they are made to see that the same happens in transients of direct current circuits.

  1. One-Dimensional Modelling of Marine Current Turbine Runaway Behaviour

    Directory of Open Access Journals (Sweden)

    Staffan Lundin

    2016-04-01

    Full Text Available If a turbine loses its electrical load, it will rotate freely and increase speed, eventually achieving that rotational speed which produces zero net torque. This is known as a runaway situation. Unlike many other types of turbine, a marine current turbine will typically overshoot the final runaway speed before slowing down and settling at the runaway speed. Since the hydrodynamic forces acting on the turbine are dependent on rotational speed and acceleration, turbine behaviour during runaway becomes important for load analyses during turbine design. In this article, we consider analytical and numerical models of marine current turbine runaway behaviour in one dimension. The analytical model is found not to capture the overshoot phenomenon, while still providing useful estimates of acceleration at the onset of runaway. The numerical model incorporates turbine wake build-up and predicts a rotational speed overshoot. The predictions of the models are compared against measurements of runaway of a marine current turbine. The models are also used to recreate previously-published results for a tidal turbine and applied to a wind turbine. It is found that both models provide reasonable estimates of maximum accelerations. The numerical model is found to capture the speed overshoot well.

  2. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

    Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

  3. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  4. Predictive user modeling with actionable attributes

    NARCIS (Netherlands)

    Zliobaite, I.; Pechenizkiy, M.

    2013-01-01

    Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target

  5. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain, which...

  6. Biodiversity in environmental assessment-current practice and tools for prediction

    International Nuclear Information System (INIS)

    Gontier, Mikael; Balfors, Berit; Moertberg, Ulla

    2006-01-01

    Habitat loss and fragmentation are major threats to biodiversity. Environmental impact assessment and strategic environmental assessment are essential instruments used in physical planning to address such problems. Yet there are no well-developed methods for quantifying and predicting impacts of fragmentation on biodiversity. In this study, a literature review was conducted on GIS-based ecological models that have potential as prediction tools for biodiversity assessment. Further, a review of environmental impact statements for road and railway projects from four European countries was performed, to study how impact prediction concerning biodiversity issues was addressed. The results of the study showed the existing gap between research in GIS-based ecological modelling and current practice in biodiversity assessment within environmental assessment

  7. The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics

    Directory of Open Access Journals (Sweden)

    Ronald de Vlaming

    2015-01-01

    Full Text Available In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i the theoretical foundations of ridge regression, (ii its link to commonly used methods in animal breeding, (iii the computational feasibility, and (iv the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis. Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N<10,000 the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP. However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.

  8. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  9. Model predictive control for Z-source power converter

    DEFF Research Database (Denmark)

    Mo, W.; Loh, P.C.; Blaabjerg, Frede

    2011-01-01

    This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...

  10. Robust predictions of the interacting boson model

    International Nuclear Information System (INIS)

    Casten, R.F.; Koeln Univ.

    1994-01-01

    While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

  11. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  12. Current algebra, statistical mechanics and quantum models

    Science.gov (United States)

    Vilela Mendes, R.

    2017-11-01

    Results obtained in the past for free boson systems at zero and nonzero temperatures are revisited to clarify the physical meaning of current algebra reducible functionals which are associated to systems with density fluctuations, leading to observable effects on phase transitions. To use current algebra as a tool for the formulation of quantum statistical mechanics amounts to the construction of unitary representations of diffeomorphism groups. Two mathematical equivalent procedures exist for this purpose. One searches for quasi-invariant measures on configuration spaces, the other for a cyclic vector in Hilbert space. Here, one argues that the second approach is closer to the physical intuition when modelling complex systems. An example of application of the current algebra methodology to the pairing phenomenon in two-dimensional fermion systems is discussed.

  13. Current error vector based prediction control of the section winding permanent magnet linear synchronous motor

    Energy Technology Data Exchange (ETDEWEB)

    Hong Junjie, E-mail: hongjjie@mail.sysu.edu.cn [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China); Li Liyi, E-mail: liliyi@hit.edu.cn [Dept. Electrical Engineering, Harbin Institute of Technology, Harbin 150000 (China); Zong Zhijian; Liu Zhongtu [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China)

    2011-10-15

    Highlights: {yields} The structure of the permanent magnet linear synchronous motor (SW-PMLSM) is new. {yields} A new current control method CEVPC is employed in this motor. {yields} The sectional power supply method is different to the others and effective. {yields} The performance gets worse with voltage and current limitations. - Abstract: To include features such as greater thrust density, higher efficiency without reducing the thrust stability, this paper proposes a section winding permanent magnet linear synchronous motor (SW-PMLSM), whose iron core is continuous, whereas winding is divided. The discrete system model of the motor is derived. With the definition of the current error vector and selection of the value function, the theory of the current error vector based prediction control (CEVPC) for the motor currents is explained clearly. According to the winding section feature, the motion region of the mover is divided into five zones, in which the implementation of the current predictive control method is proposed. Finally, the experimental platform is constructed and experiments are carried out. The results show: the current control effect has good dynamic response, and the thrust on the mover remains constant basically.

  14. Predictive integrated modelling for ITER scenarios

    International Nuclear Information System (INIS)

    Artaud, J.F.; Imbeaux, F.; Aniel, T.; Basiuk, V.; Eriksson, L.G.; Giruzzi, G.; Hoang, G.T.; Huysmans, G.; Joffrin, E.; Peysson, Y.; Schneider, M.; Thomas, P.

    2005-01-01

    The uncertainty on the prediction of ITER scenarios is evaluated. 2 transport models which have been extensively validated against the multi-machine database are used for the computation of the transport coefficients. The first model is GLF23, the second called Kiauto is a model in which the profile of dilution coefficient is a gyro Bohm-like analytical function, renormalized in order to get profiles consistent with a given global energy confinement scaling. The package of codes CRONOS is used, it gives access to the dynamics of the discharge and allows the study of interplay between heat transport, current diffusion and sources. The main motivation of this work is to study the influence of parameters such plasma current, heat, density, impurities and toroidal moment transport. We can draw the following conclusions: 1) the target Q = 10 can be obtained in ITER hybrid scenario at I p = 13 MA, using either the DS03 two terms scaling or the GLF23 model based on the same pedestal; 2) I p = 11.3 MA, Q = 10 can be reached only assuming a very peaked pressure profile and a low pedestal; 3) at fixed Greenwald fraction, Q increases with density peaking; 4) achieving a stationary q-profile with q > 1 requires a large non-inductive current fraction (80%) that could be provided by 20 to 40 MW of LHCD; and 5) owing to the high temperature the q-profile penetration is delayed and q = 1 is reached about 600 s in ITER hybrid scenario at I p = 13 MA, in the absence of active q-profile control. (A.C.)

  15. Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

    Science.gov (United States)

    Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

    2016-01-01

    Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

  16. Dual resonance models and their currents

    International Nuclear Information System (INIS)

    Johnson, E.A.

    1978-01-01

    It is shown how dual resonance models were rederived from the concept of a string tracing out a surface in space-time. Thus, interacting strings reproduce the dual amplitudes. A scheme for tackling the unitarity problem began to develop. As a consistent theory of hadronic processes began to be built, workers at the same time were naturally led to expect that leptons could be included with hadrons in a unified dual theory. Thus, there is a search for dual amplitudes which would describe interactions between hadrons and currents (for example, electrons), as well as interactions involving only hadrons. Such amplitudes, it is believed, will be the correct ones, describing the real world. Such amplitudes will provide valuable information concerning such things as hadronic form factors. The great difficulties in building current-amplitudes with the required properties of proper factorization on a good spectrum, duality, current algebra, and proper asymptotic behavior are described. Dual models at the present time require for consistency, an intercept value of α 0 = 1 and a dimension value of d = 26 (or d = 10). There have been speculations that the unphysical dimension may be made physical by associating the ''extra dimensions'' with certain internal degrees of freedom. However, it is desired that the theory itself, force the dimension d = 4. It is quite possible that the dimension problem and the intercept problem are tied together and that resolving either problem will resolve the other. Order by order, a new dual current is constructed that is manifestly factorizable and which appears to be valid for arbitrary space-time dimension. The fact that this current is not bound at d = 26, leads to interesting speculations on the nature of dual currents

  17. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

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

    2017-04-01

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

  18. Current Status and Prediction on Development of PE Market

    Institute of Scientific and Technical Information of China (English)

    Yu Jiao

    2003-01-01

    This article comprehensively analyzes the status of market demand/supply and import/export volumes of PE in the world and in China, and predicts the future development trends in the fields of PE production and consumption.

  19. Extracting falsifiable predictions from sloppy models.

    Science.gov (United States)

    Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

    2007-12-01

    Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

  20. The prediction of epidemics through mathematical modeling.

    Science.gov (United States)

    Schaus, Catherine

    2014-01-01

    Mathematical models may be resorted to in an endeavor to predict the development of epidemics. The SIR model is one of the applications. Still too approximate, the use of statistics awaits more data in order to come closer to reality.

  1. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  2. Modelling of current loads on aquaculture net cages

    Science.gov (United States)

    Kristiansen, Trygve; Faltinsen, Odd M.

    2012-10-01

    In this paper we propose and discuss a screen type of force model for the viscous hydrodynamic load on nets. The screen model assumes that the net is divided into a number of flat net panels, or screens. It may thus be applied to any kind of net geometry. In this paper we focus on circular net cages for fish farms. The net structure itself is modelled by an existing truss model. The net shape is solved for in a time-stepping procedure that involves solving a linear system of equations for the unknown tensions at each time step. We present comparisons to experiments with circular net cages in steady current, and discuss the sensitivity of the numerical results to a set of chosen parameters. Satisfactory agreement between experimental and numerical prediction of drag and lift as function of the solidity ratio of the net and the current velocity is documented.

  3. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

    Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

    2017-12-01

    Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

  4. High performance predictive current control of a three phase VSI: An ...

    Indian Academy of Sciences (India)

    ... current control of a three phase VSI: An experimental assessment ... Voltage source inverter; two level inverter; predictive current control; weighting factor ... Conventionally, for reference current tracking control in a two level VSI, the objective ...

  5. Neutrino nucleosynthesis in supernovae: Shell model predictions

    International Nuclear Information System (INIS)

    Haxton, W.C.

    1989-01-01

    Almost all of the 3 · 10 53 ergs liberated in a core collapse supernova is radiated as neutrinos by the cooling neutron star. I will argue that these neutrinos interact with nuclei in the ejected shells of the supernovae to produce new elements. It appears that this nucleosynthesis mechanism is responsible for the galactic abundances of 7 Li, 11 B, 19 F, 138 La, and 180 Ta, and contributes significantly to the abundances of about 15 other light nuclei. I discuss shell model predictions for the charged and neutral current allowed and first-forbidden responses of the parent nuclei, as well as the spallation processes that produce the new elements. 18 refs., 1 fig., 1 tab

  6. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing

  7. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  8. General Potential-Current Model and Validation for Electrocoagulation

    International Nuclear Information System (INIS)

    Dubrawski, Kristian L.; Du, Codey; Mohseni, Madjid

    2014-01-01

    A model relating potential and current in continuous parallel plate iron electrocoagulation (EC) was developed for application in drinking water treatment. The general model can be applied to any EC parallel plate system relying only on geometric and tabulated input variables without the need of system-specific experimentally derived constants. For the theoretical model, the anode and cathode were vertically divided into n equipotential segments in a single pass, upflow, and adiabatic EC reactor. Potential and energy balances were simultaneously solved at each vertical segment, which included the contribution of ionic concentrations, solution temperature and conductivity, cathodic hydrogen flux, and gas/liquid ratio. We experimentally validated the numerical model with a vertical upflow EC reactor using a 24 cm height 99.99% pure iron anode divided into twelve 2 cm segments. Individual experimental currents from each segment were summed to determine total current, and compared with the theoretically derived value. Several key variables were studied to determine their impact on model accuracy: solute type, solute concentration, current density, flow rate, inter-electrode gap, and electrode surface condition. Model results were in good agreement with experimental values at cell potentials of 2-20 V (corresponding to a current density range of approximately 50-800 A/m 2 ), with mean relative deviation of 9% for low flow rate, narrow electrode gap, polished electrodes, and 150 mg/L NaCl. Highest deviation occurred with a large electrode gap, unpolished electrodes, and Na 2 SO 4 electrolyte, due to parasitic H 2 O oxidation and less than unity current efficiency. This is the first general model which can be applied to any parallel plate EC system for accurate electrochemical voltage or current prediction

  9. Prediction of SFL Interruption Performance from the Results of Arc Simulation during High-Current Phase

    Science.gov (United States)

    Lee, Jong-Chul; Lee, Won-Ho; Kim, Woun-Jea

    2015-09-01

    The design and development procedures of SF6 gas circuit breakers are still largely based on trial and error through testing although the development costs go higher every year. The computation cannot cover the testing satisfactorily because all the real processes arc not taken into account. But the knowledge of the arc behavior and the prediction of the thermal-flow inside the interrupters by numerical simulations are more useful than those by experiments due to the difficulties to obtain physical quantities experimentally and the reduction of computational costs in recent years. In this paper, in order to get further information into the interruption process of a SF6 self-blast interrupter, which is based on a combination of thermal expansion and the arc rotation principle, gas flow simulations with a CFD-arc modeling are performed during the whole switching process such as high-current period, pre-current zero period, and current-zero period. Through the complete work, the pressure-rise and the ramp of the pressure inside the chamber before current zero as well as the post-arc current after current zero should be a good criterion to predict the short-line fault interruption performance of interrupters.

  10. Validation of a multi-objective, predictive urban traffic model

    NARCIS (Netherlands)

    Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.

    2013-01-01

    This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a

  11. Predicting scour beneath subsea pipelines from existing small free span depths under steady currents

    Directory of Open Access Journals (Sweden)

    Jun Y. Lee

    2017-06-01

    Full Text Available An equation was developed to predict current-induced scour beneath subsea pipelines in areas with small span depths, S. Current equations for scour prediction are only applicable to partially buried pipelines. The existence of small span depths (i.e. S/D < 0.3 are of concern because the capacity for scour is higher at smaller span depths. Furthermore, it is impractical to perform rectification works, such as installing grout bags, under a pipeline with a small S/D. Full-scale two-dimensional computational fluid dynamics (CFD simulations were performed using the Reynolds-averaged Navier–Stokes approach and the Shear stress transport k–ω turbulence model. To predict the occurrence of scour, the computed maximum bed shear stress beneath the pipe was converted to the dimensionless Shields parameter, and compared with the critical Shields parameter based on the mean sediment grain size. The numerical setup was verified, and a good agreement was found between model-scale CFD data and experimental data. Field data were obtained to determine the mean grain size, far field current velocity and to measure the span depths along the surveyed pipe length. A trend line equation was fitted to the full-scale CFD data, whereby the maximum Shields parameter beneath the pipe can be calculated based on the undisturbed Shields parameter and S/D.

  12. Incorporating uncertainty in predictive species distribution modelling.

    Science.gov (United States)

    Beale, Colin M; Lennon, Jack J

    2012-01-19

    Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

  13. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

  14. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  15. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  16. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  17. Deep Predictive Models in Interactive Music

    OpenAIRE

    Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

    2018-01-01

    Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

  18. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  19. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....

  20. Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

    OpenAIRE

    David Ebert

    2006-01-01

    One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

  1. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  2. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  3. Multi-model analysis in hydrological prediction

    Science.gov (United States)

    Lanthier, M.; Arsenault, R.; Brissette, F.

    2017-12-01

    Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

  4. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

  16. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  17. Predicting the behavioural impact of transcranial direct current stimulation: issues and limitations

    Directory of Open Access Journals (Sweden)

    Archy Otto De Berker

    2013-10-01

    Full Text Available The transcranial application of weak currents to the human brain has enjoyed a decade of success, providing a simple and powerful tool for non-invasively altering human brain function. However, our understanding of current delivery and its impact upon neural circuitry leaves much to be desired. We argue that the credibility of conclusions drawn with tDCS is contingent upon realistic explanations of how tDCS works, and that our present understanding of tDCS limits the technique’s use to localize function in the human brain. We outline two central issues where progress is required: the localization of currents, and predicting their functional consequence. We encourage experimenters to eschew simplistic explanations of mechanisms of transcranial current stimulation. We suggest the use of individualized current modelling, together with computational neurostimulation to inform mechanistic frameworks in which to interpret the physiological impact of tDCS. We hope that through mechanistically richer descriptions of current flow and action, insight into the biological processes by which transcranial currents influence behaviour can be gained, leading to more effective stimulation protocols and empowering conclusions drawn with tDCS.

  18. Spent fuel: prediction model development

    International Nuclear Information System (INIS)

    Almassy, M.Y.; Bosi, D.M.; Cantley, D.A.

    1979-07-01

    The need for spent fuel disposal performance modeling stems from a requirement to assess the risks involved with deep geologic disposal of spent fuel, and to support licensing and public acceptance of spent fuel repositories. Through the balanced program of analysis, diagnostic testing, and disposal demonstration tests, highlighted in this presentation, the goal of defining risks and of quantifying fuel performance during long-term disposal can be attained

  19. Navy Recruit Attrition Prediction Modeling

    Science.gov (United States)

    2014-09-01

    have high correlation with attrition, such as age, job characteristics, command climate, marital status, behavior issues prior to recruitment, and the...the additive model. glm(formula = Outcome ~ Age + Gender + Marital + AFQTCat + Pay + Ed + Dep, family = binomial, data = ltraining) Deviance ...0.1 ‘ ‘ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance : 105441 on 85221 degrees of freedom Residual deviance

  20. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  1. Predictive Models and Computational Toxicology (II IBAMTOX)

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  2. Finding furfural hydrogenation catalysts via predictive modelling

    NARCIS (Netherlands)

    Strassberger, Z.; Mooijman, M.; Ruijter, E.; Alberts, A.H.; Maldonado, A.G.; Orru, R.V.A.; Rothenberg, G.

    2010-01-01

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes

  3. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    African Journals Online (AJOL)

    FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL STRESSES IN ... the transverse residual stress in the x-direction (σx) had a maximum value of 375MPa ... the finite element method are in fair agreement with the experimental results.

  4. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico; Kryshtafovych, Andriy; Tramontano, Anna

    2009-01-01

    established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic

  5. MULTIVARIATE MODEL FOR CORPORATE BANKRUPTCY PREDICTION IN ROMANIA

    OpenAIRE

    Daniel BRÎNDESCU – OLARIU

    2016-01-01

    The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, ...

  6. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

  7. Predictions of of fast wave heating, current drive, and current drive antenna arrays for advanced tokamaks

    International Nuclear Information System (INIS)

    Batchelor, D.B.; Baity, F.W.; Carter, M.D.

    1995-01-01

    The objective of the advanced tokamak program is to optimize plasma performance leading to a compact tokamak reactor through active, steady state control of the current profile using non-inductive current drive and profile control. To achieve this objective requires compatibility and flexibility in the use of available heating and current drive systems - ion cyclotron radio frequency (ICRF), neutral beams, and lower hybrid. For any advanced tokamak, the following are important challenges to effective use of fast waves in various role of direct electron heating, minority ion heating, and current drive: (1) to employ the heating and current drive systems to give self-consistent pressure and current profiles leading to the desired advanced tokamak operating modes; (2) to minimize absorption of the fast waves by parasitic resonances, which limit current drive; (3) to optimize and control the spectrum of fast waves launched by the antenna array for the required mix of simultaneous heating and current drive. The paper addresses these issues using theoretical and computational tools developed at a number of institutions by benchmarking the computations against available experimental data and applying them to the specific case of TPX. (author). 6 refs, 3 figs

  8. Predictions of fast wave heating, current drive, and current drive antenna arrays for advanced tokamaks

    International Nuclear Information System (INIS)

    Batchelor, D.B.; Baity, F.W.; Carter, M.D.

    1994-01-01

    The objective of the advanced tokamak program is to optimize plasma performance leading to a compact tokamak reactor through active, steady state control of the current profile using non-inductive current drive and profile control. To achieve these objectives requires compatibility and flexibility in the use of available heating and current drive systems--ion cyclotron radio frequency (ICRF), neutral beams, and lower hybrid. For any advanced tokamak, the following are important challenges to effective use of fast waves in various roles of direct electron heating, minority ion heating, and current drive: (1) to employ the heating and current drive systems to give self-consistent pressure and current profiles leading to the desired advanced tokamak operating modes; (2) to minimize absorption of the fast waves by parasitic resonances, which limit current drive; (3) to optimize and control the spectrum of fast waves launched by the antenna array for the required mix of simultaneous heating and current drive. The authors have addressed these issues using theoretical and computational tools developed at a number of institutions by benchmarking the computations against available experimental data and applying them to the specific case of TPX

  9. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  10. Model predictive Controller for Mobile Robot

    OpenAIRE

    Alireza Rezaee

    2017-01-01

    This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

  11. A Real-Time Nearshore Wave and Current Prediction System

    Science.gov (United States)

    2008-01-01

    this component of the The Dclfl3D system, developed by Delft Hydraulics nearshorc modeling system was tailored specifically tbr ( htp :,’www.wldelft.nl...Walstra. DA-., 2004. Keeping it simple by using Tolman. ILL., 2002. U.ser Manual and Systrn [Documentation of complex models, The 6th tnt. Conf. an...Stelling, 6.S., Van Kester, J.A.TM., 1994. Otil the approximation of WL D)elft I lvdratifis. 2001. User Manual l)MIUD-FLIAW. horizontal gradients in

  12. Capabilities of current wildfire models when simulating topographical flow

    Science.gov (United States)

    Kochanski, A.; Jenkins, M.; Krueger, S. K.; McDermott, R.; Mell, W.

    2009-12-01

    Accurate predictions of the growth, spread and suppression of wild fires rely heavily on the correct prediction of the local wind conditions and the interactions between the fire and the local ambient airflow. Resolving local flows, often strongly affected by topographical features like hills, canyons and ridges, is a prerequisite for accurate simulation and prediction of fire behaviors. In this study, we present the results of high-resolution numerical simulations of the flow over a smooth hill, performed using (1) the NIST WFDS (WUI or Wildland-Urban-Interface version of the FDS or Fire Dynamic Simulator), and (2) the LES version of the NCAR Weather Research and Forecasting (WRF-LES) model. The WFDS model is in the initial stages of development for application to wind flow and fire spread over complex terrain. The focus of the talk is to assess how well simple topographical flow is represented by WRF-LES and the current version of WFDS. If sufficient progress has been made prior to the meeting then the importance of the discrepancies between the predicted and measured winds, in terms of simulated fire behavior, will be examined.

  13. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  14. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  15. Predicting the extent of succulent thicket under current and future ...

    African Journals Online (AJOL)

    Using data from the distribution records of the facultative CAM succulent shrub Portulacaria afra, and high resolution climate response surfaces, we developed a spatially explicit model of the potential distribution of the species in the Thicket Biome of the eastern and southern Cape, South Africa. The resultant map shows a ...

  16. Prediction of burnout of a conduction-cooled BSCCO current lead

    International Nuclear Information System (INIS)

    Seol, S.Y.; Cha, Y.S.; Niemann, R.C.; Hull, J.R.

    1996-01-01

    A one-dimensional heat conduction model is employed to predict burnout of a Bi 2 Sr 2 CaCu 2 O 8 current lead. The upper end of the lead is assumed to be at 77 K and the lower end is at 4 K. The results show that burnout always occurs at the warmer end of the lead. The lead reaches its burnout temperature in two distinct stage. Initially, the temperature rises slowly when part of the lead is in flux-flow state. As the local temperature reaches the critical temperature, it begins to increase sharply. Burnout time depends strongly on flux-flow resistivity

  17. Global vegetation change predicted by the modified Budyko model

    Energy Technology Data Exchange (ETDEWEB)

    Monserud, R.A.; Tchebakova, N.M.; Leemans, R. (US Department of Agriculture, Moscow, ID (United States). Intermountain Research Station, Forest Service)

    1993-09-01

    A modified Budyko global vegetation model is used to predict changes in global vegetation patterns resulting from climate change (CO[sub 2] doubling). Vegetation patterns are predicted using a model based on a dryness index and potential evaporation determined by solving radiation balance equations. Climate change scenarios are derived from predictions from four General Circulation Models (GCM's) of the atmosphere (GFDL, GISS, OSU, and UKMO). All four GCM scenarios show similar trends in vegetation shifts and in areas that remain stable, although the UKMO scenario predicts greater warming than the others. Climate change maps produced by all four GCM scenarios show good agreement with the current climate vegetation map for the globe as a whole, although over half of the vegetation classes show only poor to fair agreement. The most stable areas are Desert and Ice/Polar Desert. Because most of the predicted warming is concentrated in the Boreal and Temperate zones, vegetation there is predicted to undergo the greatest change. Most vegetation classes in the Subtropics and Tropics are predicted to expand. Any shift in the Tropics favouring either Forest over Savanna, or vice versa, will be determined by the magnitude of the increased precipitation accompanying global warming. Although the model predicts equilibrium conditions to which many plant species cannot adjust (through migration or microevolution) in the 50-100 y needed for CO[sub 2] doubling, it is not clear if projected global warming will result in drastic or benign vegetation change. 72 refs., 3 figs., 3 tabs.

  18. Plant water potential improves prediction of empirical stomatal models.

    Directory of Open Access Journals (Sweden)

    William R L Anderegg

    Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

  19. Performance prediction model for distributed applications on multicore clusters

    CSIR Research Space (South Africa)

    Khanyile, NP

    2012-07-01

    Full Text Available discusses some of the short comings of this law in the current age. We propose a theoretical model for predicting the behavior of a distributed algorithm given the network restrictions of the cluster used. The paper focuses on the impact of latency...

  20. Using Pareto points for model identification in predictive toxicology

    Science.gov (United States)

    2013-01-01

    Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

  1. The current status of biomarkers for predicting toxicity

    Science.gov (United States)

    Campion, Sarah; Aubrecht, Jiri; Boekelheide, Kim; Brewster, David W; Vaidya, Vishal S; Anderson, Linnea; Burt, Deborah; Dere, Edward; Hwang, Kathleen; Pacheco, Sara; Saikumar, Janani; Schomaker, Shelli; Sigman, Mark; Goodsaid, Federico

    2013-01-01

    Introduction There are significant rates of attrition in drug development. A number of compounds fail to progress past preclinical development due to limited tools that accurately monitor toxicity in preclinical studies and in the clinic. Research has focused on improving tools for the detection of organ-specific toxicity through the identification and characterization of biomarkers of toxicity. Areas covered This article reviews what we know about emerging biomarkers in toxicology, with a focus on the 2012 Northeast Society of Toxicology meeting titled ‘Translational Biomarkers in Toxicology.’ The areas covered in this meeting are summarized and include biomarkers of testicular injury and dysfunction, emerging biomarkers of kidney injury and translation of emerging biomarkers from preclinical species to human populations. The authors also provide a discussion about the biomarker qualification process and possible improvements to this process. Expert opinion There is currently a gap between the scientific work in the development and qualification of novel biomarkers for nonclinical drug safety assessment and how these biomarkers are actually used in drug safety assessment. A clear and efficient path to regulatory acceptance is needed so that breakthroughs in the biomarker toolkit for nonclinical drug safety assessment can be utilized to aid in the drug development process. PMID:23961847

  2. Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-09-10

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k(H):k(D)=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R(2)=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model's predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.

  3. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.

    1994-01-01

    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  4. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  5. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  6. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  7. Evaluation of the Current State of Integrated Water Quality Modelling

    Science.gov (United States)

    Arhonditsis, G. B.; Wellen, C. C.; Ecological Modelling Laboratory

    2010-12-01

    Environmental policy and management implementation require robust methods for assessing the contribution of various point and non-point pollution sources to water quality problems as well as methods for estimating the expected and achieved compliance with the water quality goals. Water quality models have been widely used for creating the scientific basis for management decisions by providing a predictive link between restoration actions and ecosystem response. Modelling water quality and nutrient transport is challenging due a number of constraints associated with the input data and existing knowledge gaps related to the mathematical description of landscape and in-stream biogeochemical processes. While enormous effort has been invested to make watershed models process-based and spatially-distributed, there has not been a comprehensive meta-analysis of model credibility in watershed modelling literature. In this study, we evaluate the current state of integrated water quality modeling across the range of temporal and spatial scales typically utilized. We address several common modeling questions by providing a quantitative assessment of model performance and by assessing how model performance depends on model development. The data compiled represent a heterogeneous group of modeling studies, especially with respect to complexity, spatial and temporal scales and model development objectives. Beginning from 1992, the year when Beven and Binley published their seminal paper on uncertainty analysis in hydrological modelling, and ending in 2009, we selected over 150 papers fitting a number of criteria. These criteria involved publications that: (i) employed distributed or semi-distributed modelling approaches; (ii) provided predictions on flow and nutrient concentration state variables; and (iii) reported fit to measured data. Model performance was quantified with the Nash-Sutcliffe Efficiency, the relative error, and the coefficient of determination. Further, our

  8. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  9. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  10. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-01-01

    Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

  11. Prevalence of current patterns and predictive trends of multidrug-resistant Salmonella Typhi in Sudan

    Directory of Open Access Journals (Sweden)

    Ayman A. Elshayeb

    2017-11-01

    Full Text Available Abstract Background Enteric fever has persistence of great impact in Sudanese public health especially during rainy season when the causative agent Salmonella enterica serovar Typhi possesses pan endemic patterns in most regions of Sudan - Khartoum. Objectives The present study aims to assess the recent state of antibiotics susceptibility of Salmonella Typhi with special concern to multidrug resistance strains and predict the emergence of new resistant patterns and outbreaks. Methods Salmonella Typhi strains were isolated and identified according to the guidelines of the International Standardization Organization and the World Health Organization. The antibiotics susceptibilities were tested using the recommendations of the Clinical Laboratories Standards Institute. Predictions of emerging resistant bacteria patterns and outbreaks in Sudan were done using logistic regression, forecasting linear equations and in silico simulations models. Results A total of 124 antibiotics resistant Salmonella Typhi strains categorized in 12 average groups were isolated, different patterns of resistance statistically calculated by (y = ax − b. Minimum bactericidal concentration’s predication of resistance was given the exponential trend (y = n ex and the predictive coefficient R2 > 0 < 1 are approximately alike. It was assumed that resistant bacteria occurred with a constant rate of antibiotic doses during the whole experimental period. Thus, the number of sensitive bacteria decreases at the same rate as resistant occur following term to the modified predictive model which solved computationally. Conclusion This study assesses the prediction of multi-drug resistance among S. Typhi isolates by applying low cost materials and simple statistical methods suitable for the most frequently used antibiotics as typhoid empirical therapy. Therefore, bacterial surveillance systems should be implemented to present data on the aetiology and current

  12. Current Challenges in the First Principle Quantitative Modelling of the Lower Hybrid Current Drive in Tokamaks

    Science.gov (United States)

    Peysson, Y.; Bonoli, P. T.; Chen, J.; Garofalo, A.; Hillairet, J.; Li, M.; Qian, J.; Shiraiwa, S.; Decker, J.; Ding, B. J.; Ekedahl, A.; Goniche, M.; Zhai, X.

    2017-10-01

    The Lower Hybrid (LH) wave is widely used in existing tokamaks for tailoring current density profile or extending pulse duration to steady-state regimes. Its high efficiency makes it particularly attractive for a fusion reactor, leading to consider it for this purpose in ITER tokamak. Nevertheless, if basics of the LH wave in tokamak plasma are well known, quantitative modeling of experimental observations based on first principles remains a highly challenging exercise, despite considerable numerical efforts achieved so far. In this context, a rigorous methodology must be carried out in the simulations to identify the minimum number of physical mechanisms that must be considered to reproduce experimental shot to shot observations and also scalings (density, power spectrum). Based on recent simulations carried out for EAST, Alcator C-Mod and Tore Supra tokamaks, the state of the art in LH modeling is reviewed. The capability of fast electron bremsstrahlung, internal inductance li and LH driven current at zero loop voltage to constrain all together LH simulations is discussed, as well as the needs of further improvements (diagnostics, codes, LH model), for robust interpretative and predictive simulations.

  13. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)

    2002-06-01

    This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)

  14. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2011-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  15. Resistive switching near electrode interfaces: Estimations by a current model

    Science.gov (United States)

    Schroeder, Herbert; Zurhelle, Alexander; Stemmer, Stefanie; Marchewka, Astrid; Waser, Rainer

    2013-02-01

    The growing resistive switching database is accompanied by many detailed mechanisms which often are pure hypotheses. Some of these suggested models can be verified by checking their predictions with the benchmarks of future memory cells. The valence change memory model assumes that the different resistances in ON and OFF states are made by changing the defect density profiles in a sheet near one working electrode during switching. The resulting different READ current densities in ON and OFF states were calculated by using an appropriate simulation model with variation of several important defect and material parameters of the metal/insulator (oxide)/metal thin film stack such as defect density and its profile change in density and thickness, height of the interface barrier, dielectric permittivity, applied voltage. The results were compared to the benchmarks and some memory windows of the varied parameters can be defined: The required ON state READ current density of 105 A/cm2 can only be achieved for barriers smaller than 0.7 eV and defect densities larger than 3 × 1020 cm-3. The required current ratio between ON and OFF states of at least 10 requests defect density reduction of approximately an order of magnitude in a sheet of several nanometers near the working electrode.

  16. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  17. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  18. Model Predictive Control Algorithms for Pen and Pump Insulin Administration

    DEFF Research Database (Denmark)

    Boiroux, Dimitri

    at mealtime, and the case where the insulin sensitivity increases during the night. This thesis consists of a summary report, glucose and insulin proles of the clinical studies and research papers submitted, peer-reviewed and/or published in the period September 2009 - September 2012....... of current closed-loop controllers. In this thesis, we present different control strategies based on Model Predictive Control (MPC) for an artificial pancreas. We use Nonlinear Model Predictive Control (NMPC) in order to determine the optimal insulin and blood glucose profiles. The optimal control problem...

  19. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  20. Alcator C-Mod predictive modeling

    International Nuclear Information System (INIS)

    Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas

    2001-01-01

    Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles

  1. Design and implementation of predictive filtering system for current reference generation of active power filter

    Energy Technology Data Exchange (ETDEWEB)

    Kilic, Tomislav; Milun, Stanko; Petrovic, Goran [FESB University of Split, Faculty of Electrical Engineering, Machine Engineering and Naval Architecture, R. Boskovica bb, 21000, Split (Croatia)

    2007-02-15

    The shunt active power filters are used to attenuate the harmonic currents in power systems by injecting equal but opposite compensating currents. Successful control of the active filters requires an accurate current reference. In this paper the current reference determination based on predictive filtering structure is presented. Current reference was obtained by taking the difference of load current and its fundamental harmonic. For fundamental harmonic determination with no time delay a combination of digital predictive filter and low pass filter is used. The proposed method was implemented on a laboratory prototype of a three-phase active power filter. The algorithm for current reference determination was adapted and implemented on DSP controller. Simulation and experimental results show that the active power filter with implemented predictive filtering structure gives satisfactory performance in power system harmonic attenuation. (author)

  2. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

    Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  3. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  4. Cross-Validation of Aerobic Capacity Prediction Models in Adolescents.

    Science.gov (United States)

    Burns, Ryan Donald; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Saint-Maurice, Pedro F; Welk, Greg J; Mahar, Matthew T

    2015-08-01

    Cardiorespiratory endurance is a component of health-related fitness. FITNESSGRAM recommends the Progressive Aerobic Cardiovascular Endurance Run (PACER) or One mile Run/Walk (1MRW) to assess cardiorespiratory endurance by estimating VO2 Peak. No research has cross-validated prediction models from both PACER and 1MRW, including the New PACER Model and PACER-Mile Equivalent (PACER-MEQ) using current standards. The purpose of this study was to cross-validate prediction models from PACER and 1MRW against measured VO2 Peak in adolescents. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years (Mean = 14.7 ± 1.3 years; 32 girls, 52 boys) who completed the PACER and 1MRW in addition to a laboratory maximal treadmill test to measure VO2 Peak. Multiple correlations among various models with measured VO2 Peak were considered moderately strong (R = .74-0.78), and prediction error (RMSE) ranged from 5.95 ml·kg⁻¹,min⁻¹ to 8.27 ml·kg⁻¹.min⁻¹. Criterion-referenced agreement into FITNESSGRAM's Healthy Fitness Zones was considered fair-to-good among models (Kappa = 0.31-0.62; Agreement = 75.5-89.9%; F = 0.08-0.65). In conclusion, prediction models demonstrated moderately strong linear relationships with measured VO2 Peak, fair prediction error, and fair-to-good criterion referenced agreement with measured VO2 Peak into FITNESSGRAM's Healthy Fitness Zones.

  5. Comparison of Predictive Modeling Methods of Aircraft Landing Speed

    Science.gov (United States)

    Diallo, Ousmane H.

    2012-01-01

    Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

  6. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  7. Improving Saliency Models by Predicting Human Fixation Patches

    KAUST Repository

    Dubey, Rachit

    2015-04-16

    There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

  8. Improving Saliency Models by Predicting Human Fixation Patches

    KAUST Repository

    Dubey, Rachit; Dave, Akshat; Ghanem, Bernard

    2015-01-01

    There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

  9. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  10. Predictive performance models and multiple task performance

    Science.gov (United States)

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  11. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  12. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  13. Underwater Sound Propagation Modeling Methods for Predicting Marine Animal Exposure.

    Science.gov (United States)

    Hamm, Craig A; McCammon, Diana F; Taillefer, Martin L

    2016-01-01

    The offshore exploration and production (E&P) industry requires comprehensive and accurate ocean acoustic models for determining the exposure of marine life to the high levels of sound used in seismic surveys and other E&P activities. This paper reviews the types of acoustic models most useful for predicting the propagation of undersea noise sources and describes current exposure models. The severe problems caused by model sensitivity to the uncertainty in the environment are highlighted to support the conclusion that it is vital that risk assessments include transmission loss estimates with statistical measures of confidence.

  14. Prevalence of current patterns and predictive trends of multidrug-resistant Salmonella Typhi in Sudan.

    Science.gov (United States)

    Elshayeb, Ayman A; Ahmed, Abdelazim A; El Siddig, Marmar A; El Hussien, Adil A

    2017-11-14

    Enteric fever has persistence of great impact in Sudanese public health especially during rainy season when the causative agent Salmonella enterica serovar Typhi possesses pan endemic patterns in most regions of Sudan - Khartoum. The present study aims to assess the recent state of antibiotics susceptibility of Salmonella Typhi with special concern to multidrug resistance strains and predict the emergence of new resistant patterns and outbreaks. Salmonella Typhi strains were isolated and identified according to the guidelines of the International Standardization Organization and the World Health Organization. The antibiotics susceptibilities were tested using the recommendations of the Clinical Laboratories Standards Institute. Predictions of emerging resistant bacteria patterns and outbreaks in Sudan were done using logistic regression, forecasting linear equations and in silico simulations models. A total of 124 antibiotics resistant Salmonella Typhi strains categorized in 12 average groups were isolated, different patterns of resistance statistically calculated by (y = ax - b). Minimum bactericidal concentration's predication of resistance was given the exponential trend (y = n e x ) and the predictive coefficient R 2  > 0 current antimicrobial drug resistance patterns of community-acquired agents causing outbreaks.

  15. A stepwise model to predict monthly streamflow

    Science.gov (United States)

    Mahmood Al-Juboori, Anas; Guven, Aytac

    2016-12-01

    In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

  16. Generalized Veneziano model for pion scattering off isovector currents and the scaling limit

    CERN Document Server

    Rothe, H J; Rolhe, K D

    1972-01-01

    Starting from a local one-particle approximation scheme for the commutator of two conserved currents, the authors construct a generalized Veneziano model for pion scattering off neutral and charged isovector currents, satisfying the constraints of current conservation and current algebra. The model factorizes correctly on the leading Regge trajectories and incorporates the proper Regge behaviour for strong amplitudes. Fixed poles are found to be present in the s and t channels of the one- and two-current amplitudes. Furthermore, the model makes definite predictions about the structure of Schwinger terms and of the 'seagull' terms in the retarded commutator. (13 refs).

  17. Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties

    Directory of Open Access Journals (Sweden)

    Ruixian Fang

    2016-09-01

    Full Text Available This work uses the adjoint sensitivity model of the counter-flow cooling tower derived in the accompanying PART I to obtain the expressions and relative numerical rankings of the sensitivities, to all model parameters, of the following model responses: (i outlet air temperature; (ii outlet water temperature; (iii outlet water mass flow rate; and (iv air outlet relative humidity. These sensitivities are subsequently used within the “predictive modeling for coupled multi-physics systems” (PM_CMPS methodology to obtain explicit formulas for the predicted optimal nominal values for the model responses and parameters, along with reduced predicted standard deviations for the predicted model parameters and responses. These explicit formulas embody the assimilation of experimental data and the “calibration” of the model’s parameters. The results presented in this work demonstrate that the PM_CMPS methodology reduces the predicted standard deviations to values that are smaller than either the computed or the experimentally measured ones, even for responses (e.g., the outlet water flow rate for which no measurements are available. These improvements stem from the global characteristics of the PM_CMPS methodology, which combines all of the available information simultaneously in phase-space, as opposed to combining it sequentially, as in current data assimilation procedures.

  18. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  19. An Intelligent Model for Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    IbrahimM. Hamed

    2012-08-01

    Full Text Available This paper presents an intelligent model for stock market signal prediction using Multi-Layer Perceptron (MLP Artificial Neural Networks (ANN. Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD is used, as a learning algorithm, because it converges fast and provides generalization in the learning mechanism. Both accuracy and efficiency of the proposed model were confirmed through the Microsoft stock, from wall-street market, and various data sets, from different sectors of the Egyptian stock market. In addition, sensitivity analysis was conducted on the various parameters of the model to ensure the coverage of the generalization issue. Finally, statistical significance was examined using ANOVA test.

  20. Applying machine learning to predict patient-specific current CD4 ...

    African Journals Online (AJOL)

    Apple apple

    This work shows the application of machine learning to predict current CD4 cell count of an HIV- .... Pre-processing ... remaining data elements of the PR and RT datasets. ... technique based on the structure of the human brain's neuron.

  1. Enhancing pavement performance prediction models for the Illinois Tollway System

    Directory of Open Access Journals (Sweden)

    Laxmikanth Premkumar

    2016-01-01

    Full Text Available Accurate pavement performance prediction represents an important role in prioritizing future maintenance and rehabilitation needs, and predicting future pavement condition in a pavement management system. The Illinois State Toll Highway Authority (Tollway with over 2000 lane miles of pavement utilizes the condition rating survey (CRS methodology to rate pavement performance. Pavement performance models developed in the past for the Illinois Department of Transportation (IDOT are used by the Tollway to predict the future condition of its network. The model projects future CRS ratings based on pavement type, thickness, traffic, pavement age and current CRS rating. However, with time and inclusion of newer pavement types there was a need to calibrate the existing pavement performance models, as well as, develop models for newer pavement types.This study presents the results of calibrating the existing models, and developing new models for the various pavement types in the Illinois Tollway network. The predicted future condition of the pavements is used in estimating its remaining service life to failure, which is of immediate use in recommending future maintenance and rehabilitation requirements for the network. Keywords: Pavement performance models, Remaining life, Pavement management

  2. Predictive Models, How good are they?

    DEFF Research Database (Denmark)

    Kasch, Helge

    The WAD grading system has been used for more than 20 years by now. It has shown long-term viability, but with strengths and limitations. New bio-psychosocial assessment of the acute whiplash injured subject may provide better prediction of long-term disability and pain. Furthermore, the emerging......-up. It is important to obtain prospective identification of the relevant risk underreported disability could, if we were able to expose these hidden “risk-factors” during our consultations, provide us with better predictive models. New data from large clinical studies will present exciting new genetic risk markers...

  3. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    SILVA R. G.

    1999-01-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  4. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  5. Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis.

    Science.gov (United States)

    Ferrer, Jordi; Prats, Clara; López, Daniel; Vives-Rego, Josep

    2009-08-31

    Predictive microbiology is the area of food microbiology that attempts to forecast the quantitative evolution of microbial populations over time. This is achieved to a great extent through models that include the mechanisms governing population dynamics. Traditionally, the models used in predictive microbiology are whole-system continuous models that describe population dynamics by means of equations applied to extensive or averaged variables of the whole system. Many existing models can be classified by specific criteria. We can distinguish between survival and growth models by seeing whether they tackle mortality or cell duplication. We can distinguish between empirical (phenomenological) models, which mathematically describe specific behaviour, and theoretical (mechanistic) models with a biological basis, which search for the underlying mechanisms driving already observed phenomena. We can also distinguish between primary, secondary and tertiary models, by examining their treatment of the effects of external factors and constraints on the microbial community. Recently, the use of spatially explicit Individual-based Models (IbMs) has spread through predictive microbiology, due to the current technological capacity of performing measurements on single individual cells and thanks to the consolidation of computational modelling. Spatially explicit IbMs are bottom-up approaches to microbial communities that build bridges between the description of micro-organisms at the cell level and macroscopic observations at the population level. They provide greater insight into the mesoscale phenomena that link unicellular and population levels. Every model is built in response to a particular question and with different aims. Even so, in this research we conducted a SWOT (Strength, Weaknesses, Opportunities and Threats) analysis of the different approaches (population continuous modelling and Individual-based Modelling), which we hope will be helpful for current and future

  6. Modeling geomagnetic induced currents in Australian power networks

    Science.gov (United States)

    Marshall, R. A.; Kelly, A.; Van Der Walt, T.; Honecker, A.; Ong, C.; Mikkelsen, D.; Spierings, A.; Ivanovich, G.; Yoshikawa, A.

    2017-07-01

    Geomagnetic induced currents (GICs) have been considered an issue for high-latitude power networks for some decades. More recently, GICs have been observed and studied in power networks located in lower latitude regions. This paper presents the results of a model aimed at predicting and understanding the impact of geomagnetic storms on power networks in Australia, with particular focus on the Queensland and Tasmanian networks. The model incorporates a "geoelectric field" determined using a plane wave magnetic field incident on a uniform conducting Earth, and the network model developed by Lehtinen and Pirjola (1985). Model results for two intense geomagnetic storms of solar cycle 24 are compared with transformer neutral monitors at three locations within the Queensland network and one location within the Tasmanian network. The model is then used to assess the impacts of the superintense geomagnetic storm of 29-31 October 2003 on the flow of GICs within these networks. The model results show good correlation with the observations with coefficients ranging from 0.73 to 0.96 across the observing sites. For Queensland, modeled GIC magnitudes during the superstorm of 29-31 October 2003 exceed 40 A with the larger GICs occurring in the south-east section of the network. Modeled GICs in Tasmania for the same storm do not exceed 30 A. The larger distance spans and general east-west alignment of the southern section of the Queensland network, in conjunction with some relatively low branch resistance values, result in larger modeled GICs despite Queensland being a lower latitude network than Tasmania.

  7. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  8. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  9. Enhanced pid vs model predictive control applied to bldc motor

    Science.gov (United States)

    Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

    2018-01-01

    BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

  10. Prediction models and control algorithms for predictive applications of setback temperature in cooling systems

    International Nuclear Information System (INIS)

    Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung

    2017-01-01

    Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature

  11. MULTIVARIATE MODEL FOR CORPORATE BANKRUPTCY PREDICTION IN ROMANIA

    Directory of Open Access Journals (Sweden)

    Daniel BRÎNDESCU – OLARIU

    2016-06-01

    Full Text Available The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, developed through discriminant analysis. Financial ratios are employed as explanatory variables within the model. The study has included 53,252 yearly financial statements from the period 2007 – 2010, with the state of the companies being monitored until the end of 2012. It thus employs the largest sample ever used in Romanian research in the field of bankruptcy prediction, not targeting high levels of accuracy over isolated samples, but reliability and ease of use over the entire population.

  12. Validated predictive modelling of the environmental resistome.

    Science.gov (United States)

    Amos, Gregory C A; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

    2015-06-01

    Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

  13. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  14. Baryogenesis model predicting antimatter in the Universe

    International Nuclear Information System (INIS)

    Kirilova, D.

    2003-01-01

    Cosmic ray and gamma-ray data do not rule out antimatter domains in the Universe, separated at distances bigger than 10 Mpc from us. Hence, it is interesting to analyze the possible generation of vast antimatter structures during the early Universe evolution. We discuss a SUSY-condensate baryogenesis model, predicting large separated regions of matter and antimatter. The model provides generation of the small locally observed baryon asymmetry for a natural initial conditions, it predicts vast antimatter domains, separated from the matter ones by baryonically empty voids. The characteristic scale of antimatter regions and their distance from the matter ones is in accordance with observational constraints from cosmic ray, gamma-ray and cosmic microwave background anisotropy data

  15. Surface CUrrents from a Diagnostic model (SCUD): Pacific

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The SCUD data product is an estimate of upper-ocean velocities computed from a diagnostic model (Surface CUrrents from a Diagnostic model). This model makes daily...

  16. Current Density and Continuity in Discretized Models

    Science.gov (United States)

    Boykin, Timothy B.; Luisier, Mathieu; Klimeck, Gerhard

    2010-01-01

    Discrete approaches have long been used in numerical modelling of physical systems in both research and teaching. Discrete versions of the Schrodinger equation employing either one or several basis functions per mesh point are often used by senior undergraduates and beginning graduate students in computational physics projects. In studying…

  17. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

    OpenAIRE

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-01-01

    Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre t...

  18. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  19. A Sensorless Predictive Current Controlled Boost Converter by Using an EKF with Load Variation Effect Elimination Function.

    Science.gov (United States)

    Tong, Qiaoling; Chen, Chen; Zhang, Qiao; Zou, Xuecheng

    2015-04-28

    To realize accurate current control for a boost converter, a precise measurement of the inductor current is required to achieve high resolution current regulating. Current sensors are widely used to measure the inductor current. However, the current sensors and their processing circuits significantly contribute extra hardware cost, delay and noise to the system. They can also harm the system reliability. Therefore, current sensorless control techniques can bring cost effective and reliable solutions for various boost converter applications. According to the derived accurate model, which contains a number of parasitics, the boost converter is a nonlinear system. An Extended Kalman Filter (EKF) is proposed for inductor current estimation and output voltage filtering. With this approach, the system can have the same advantages as sensored current control mode. To implement EKF, the load value is necessary. However, the load may vary from time to time. This can lead to errors of current estimation and filtered output voltage. To solve this issue, a load variation elimination effect elimination (LVEE) module is added. In addition, a predictive average current controller is used to regulate the current. Compared with conventional voltage controlled system, the transient response is greatly improved since it only takes two switching cycles for the current to reach its reference. Finally, experimental results are presented to verify the stable operation and output tracking capability for large-signal transients of the proposed algorithm.

  20. Tectonic predictions with mantle convection models

    Science.gov (United States)

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  1. Higher-spin currents in the Gross-Neveu model at 1/n"2

    International Nuclear Information System (INIS)

    Manashov, A.N.

    2016-10-01

    We calculate the anomalous dimensions of higher-spin currents, both singlet and non-singlet, in the Gross - Neveu model at the 1/n"2 order. It was conjectured that in the critical regime this model is dual to a higher-spin gauge theory on AdS_4. The AdS/CFT correspondence predicts that the masses of higher-spin fields correspond to the scaling dimensions of the singlet currents in the Gross - Neveu model.

  2. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  3. PEEX Modelling Platform for Seamless Environmental Prediction

    Science.gov (United States)

    Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku

    2017-04-01

    The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.

  4. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2018-01-01

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Predicting extinction rates in stochastic epidemic models

    International Nuclear Information System (INIS)

    Schwartz, Ira B; Billings, Lora; Dykman, Mark; Landsman, Alexandra

    2009-01-01

    We investigate the stochastic extinction processes in a class of epidemic models. Motivated by the process of natural disease extinction in epidemics, we examine the rate of extinction as a function of disease spread. We show that the effective entropic barrier for extinction in a susceptible–infected–susceptible epidemic model displays scaling with the distance to the bifurcation point, with an unusual critical exponent. We make a direct comparison between predictions and numerical simulations. We also consider the effect of non-Gaussian vaccine schedules, and show numerically how the extinction process may be enhanced when the vaccine schedules are Poisson distributed

  6. An efficient numerical target strength prediction model: Validation against analysis solutions

    NARCIS (Netherlands)

    Fillinger, L.; Nijhof, M.J.J.; Jong, C.A.F. de

    2014-01-01

    A decade ago, TNO developed RASP (Rapid Acoustic Signature Prediction), a numerical model for the prediction of the target strength of immersed underwater objects. The model is based on Kirchhoff diffraction theory. It is currently being improved to model refraction, angle dependent reflection and

  7. Charged-current inclusive neutrino cross sections in the SuperScaling model

    Energy Technology Data Exchange (ETDEWEB)

    Ivanov, M. V., E-mail: martin.inrne@gmail.com [Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia 1784 (Bulgaria); Grupo de Física Nuclear, Departamento de Física Atómica, Molecular y Nuclear, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid E-28040 (Spain); Megias, G. D.; Caballero, J. A. [Departamento de Física Atómica, Molecular y Nuclear, Universidad de Sevilla, 41080 Sevilla (Spain); González-Jiménez, R. [Department of Physics and Astronomy, Ghent University, Proeftuinstraat 86, B-9000 Gent (Belgium); Moreno, O.; Donnelly, T. W. [Center for Theoretical Physics, Laboratory for Nuclear Science and Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Barbaro, M. B. [Dipartimento di Fisica, Università di Torino and INFN, Sezione di Torino, Via P. Giuria 1, 10125 Torino (Italy); Antonov, A. N. [Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia 1784 (Bulgaria); Moya de Guerra, E.; Udías, J. M. [Grupo de Física Nuclear, Departamento de Física Atómica, Molecular y Nuclear, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid E-28040 (Spain)

    2016-03-25

    SuperScaling model (SuSA) predictions to neutrino-induced charged-current π{sup +} production in the Δ-resonance region are explored under MiniBooNE experimental conditions. The SuSA charged-current π{sup +} results are in good agreement with data on neutrino flux-averaged double-differential cross sections. The SuSA model for quasielastic scattering and its extension to the pion production region are used for predictions of charged-current inclusive neutrino-nucleus cross sections. Results are also compared with the T2K experimental data for inclusive scattering.

  8. Predictive modeling of pedestal structure in KSTAR using EPED model

    Energy Technology Data Exchange (ETDEWEB)

    Han, Hyunsun; Kim, J. Y. [National Fusion Research Institute, Daejeon 305-806 (Korea, Republic of); Kwon, Ohjin [Department of Physics, Daegu University, Gyeongbuk 712-714 (Korea, Republic of)

    2013-10-15

    A predictive calculation is given for the structure of edge pedestal in the H-mode plasma of the KSTAR (Korea Superconducting Tokamak Advanced Research) device using the EPED model. Particularly, the dependence of pedestal width and height on various plasma parameters is studied in detail. The two codes, ELITE and HELENA, are utilized for the stability analysis of the peeling-ballooning and kinetic ballooning modes, respectively. Summarizing the main results, the pedestal slope and height have a strong dependence on plasma current, rapidly increasing with it, while the pedestal width is almost independent of it. The plasma density or collisionality gives initially a mild stabilization, increasing the pedestal slope and height, but above some threshold value its effect turns to a destabilization, reducing the pedestal width and height. Among several plasma shape parameters, the triangularity gives the most dominant effect, rapidly increasing the pedestal width and height, while the effect of elongation and squareness appears to be relatively weak. Implication of these edge results, particularly in relation to the global plasma performance, is discussed.

  9. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

    Science.gov (United States)

    Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

    2018-01-01

    Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

  10. An integrated model for estimating energy cost of a tidal current turbine farm

    International Nuclear Information System (INIS)

    Li, Ye; Lence, Barbara J.; Calisal, Sander M.

    2011-01-01

    A tidal current turbine is a device for harnessing energy from tidal currents and functions in a manner similar to a wind turbine. A tidal current turbine farm consists of a group of tidal current turbines distributed in a site where high-speed current is available. The accurate prediction of energy cost of a tidal current turbine farm is important to the justification of planning and constructing such a farm. However, the existing approaches used to predict energy cost of tidal current turbine farms oversimplify the hydrodynamic interactions between turbines in energy prediction and oversimplify the operation and maintenance strategies involved in cost estimation as well as related fees. In this paper, we develop a model, which integrates a marine hydrodynamic model with high accuracy for predicting energy output and a comprehensive cost-effective operation and maintenance model for estimating the cost that may be incurred in producing the energy, to predict energy cost from a tidal current turbine farm. This model is expected to be able to simulate more complicated cases and generate more accurate results than existing models. As there is no real tidal current turbine farm, we validate this model with offshore wind studies. Finally, case studies about Vancouver are conducted with a scenario-based analysis. We minimize the energy cost by minimizing the total cost and maximizing the total power output under constraints related to the local conditions (e.g., geological and labor information) and the turbine specifications. The results suggest that tidal current energy is about ready to penetrate the electricity market in some major cities in North America if learning curve for the operational and maintenance is minimum. (author)

  11. Early experiences building a software quality prediction model

    Science.gov (United States)

    Agresti, W. W.; Evanco, W. M.; Smith, M. C.

    1990-01-01

    Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

  12. Data Driven Economic Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Masoud Kheradmandi

    2018-04-01

    Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

  13. Predicting turns in proteins with a unified model.

    Directory of Open Access Journals (Sweden)

    Qi Song

    Full Text Available MOTIVATION: Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. RESULTS: In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i using newly exploited features of structural evolution information (secondary structure and shape string of protein based on structure homologies, (ii considering all types of turns in a unified model, and (iii practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.

  14. Model for the resistive critical current transition in composite superconductors

    International Nuclear Information System (INIS)

    Warnes, W.H.

    1988-01-01

    Much of the research investigating technological type-II superconducting composites relies on the measurement of the resistive critical current transition. We have developed a model for the resistive transition which improves on older models by allowing for the very different nature of monofilamentary and multifilamentary composite structures. The monofilamentary model allows for axial current flow around critical current weak links in the superconducting filament. The multifilamentary model incorporates an additional radial current transfer between neighboring filaments. The development of both models is presented. It is shown that the models are useful for extracting more information from the experimental data than was formerly possible. Specific information obtainable from the experimental voltage-current characteristic includes the distribution of critical currents in the composite, the average critical current of the distribution, the range of critical currents in the composite, the field and temperature dependence of the distribution, and the fraction of the composite dissipating energy in flux flow at any current. This additional information about the distribution of critical currents may be helpful in leading toward a better understanding of flux pinning in technological superconductors. Comparison of the models with several experiments is given and shown to be in reasonable agreement. Implications of the models for the measurement of critical currents in technological composites is presented and discussed with reference to basic flux pinning studies in such composites

  15. Plant control using embedded predictive models

    International Nuclear Information System (INIS)

    Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.

    1990-01-01

    B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available

  16. Predicting Power Outages Using Multi-Model Ensemble Forecasts

    Science.gov (United States)

    Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

    2017-12-01

    Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

  17. A computational model for lower hybrid current drive

    International Nuclear Information System (INIS)

    Englade, R.C.; Bonoli, P.T.; Porkolab, M.

    1983-01-01

    A detailed simulation model for lower hybrid (LH) current drive in toroidal devices is discussed. This model accounts reasonably well for the magnitude of radio frequency (RF) current observed in the PLT and Alcator C devices. It also reproduces the experimental dependencies of RF current generation on toroidal magnetic field and has provided insights about mechanisms which may underlie the observed density limit of current drive. (author)

  18. Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  19. Modeling and Prediction of Krueger Device Noise

    Science.gov (United States)

    Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

    2016-01-01

    This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

  20. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  1. Evaluating Predictive Models of Software Quality

    Science.gov (United States)

    Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

    2014-06-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  2. Predicting FLDs Using a Multiscale Modeling Scheme

    Science.gov (United States)

    Wu, Z.; Loy, C.; Wang, E.; Hegadekatte, V.

    2017-09-01

    The measurement of a single forming limit diagram (FLD) requires significant resources and is time consuming. We have developed a multiscale modeling scheme to predict FLDs using a combination of limited laboratory testing, crystal plasticity (VPSC) modeling, and dual sequential-stage finite element (ABAQUS/Explicit) modeling with the Marciniak-Kuczynski (M-K) criterion to determine the limit strain. We have established a means to work around existing limitations in ABAQUS/Explicit by using an anisotropic yield locus (e.g., BBC2008) in combination with the M-K criterion. We further apply a VPSC model to reduce the number of laboratory tests required to characterize the anisotropic yield locus. In the present work, we show that the predicted FLD is in excellent agreement with the measured FLD for AA5182 in the O temper. Instead of 13 different tests as for a traditional FLD determination within Novelis, our technique uses just four measurements: tensile properties in three orientations; plane strain tension; biaxial bulge; and the sheet crystallographic texture. The turnaround time is consequently far less than for the traditional laboratory measurement of the FLD.

  3. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  4. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  5. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  6. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  7. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

    Science.gov (United States)

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

  8. NASA AVOSS Fast-Time Wake Prediction Models: User's Guide

    Science.gov (United States)

    Ahmad, Nash'at N.; VanValkenburg, Randal L.; Pruis, Matthew

    2014-01-01

    The National Aeronautics and Space Administration (NASA) is developing and testing fast-time wake transport and decay models to safely enhance the capacity of the National Airspace System (NAS). The fast-time wake models are empirical algorithms used for real-time predictions of wake transport and decay based on aircraft parameters and ambient weather conditions. The aircraft dependent parameters include the initial vortex descent velocity and the vortex pair separation distance. The atmospheric initial conditions include vertical profiles of temperature or potential temperature, eddy dissipation rate, and crosswind. The current distribution includes the latest versions of the APA (3.4) and the TDP (2.1) models. This User's Guide provides detailed information on the model inputs, file formats, and the model output. An example of a model run and a brief description of the Memphis 1995 Wake Vortex Dataset is also provided.

  9. Multinucleon Ejection Model for Two Body Current Neutrino Interactions

    Energy Technology Data Exchange (ETDEWEB)

    Sobczyk, Jan T.; /Fermilab

    2012-06-01

    A model is proposed to describe nucleons ejected from a nucleus as a result of two-body-current neutrino interactions. The model can be easily implemented in Monte Carlo neutrino event generators. Various possibilities to measure the two-body-current contribution are discussed. The model can help identify genuine charge current quasielastic events and allow for a better determination of the systematic error on neutrino energy reconstruction in neutrino oscillation experiments.

  10. A molecular prognostic model predicts esophageal squamous cell carcinoma prognosis.

    Directory of Open Access Journals (Sweden)

    Hui-Hui Cao

    Full Text Available Esophageal squamous cell carcinoma (ESCC has the highest mortality rates in China. The 5-year survival rate of ESCC remains dismal despite improvements in treatments such as surgical resection and adjuvant chemoradiation, and current clinical staging approaches are limited in their ability to effectively stratify patients for treatment options. The aim of the present study, therefore, was to develop an immunohistochemistry-based prognostic model to improve clinical risk assessment for patients with ESCC.We developed a molecular prognostic model based on the combined expression of axis of epidermal growth factor receptor (EGFR, phosphorylated Specificity protein 1 (p-Sp1, and Fascin proteins. The presence of this prognostic model and associated clinical outcomes were analyzed for 130 formalin-fixed, paraffin-embedded esophageal curative resection specimens (generation dataset and validated using an independent cohort of 185 specimens (validation dataset.The expression of these three genes at the protein level was used to build a molecular prognostic model that was highly predictive of ESCC survival in both generation and validation datasets (P = 0.001. Regression analysis showed that this molecular prognostic model was strongly and independently predictive of overall survival (hazard ratio = 2.358 [95% CI, 1.391-3.996], P = 0.001 in generation dataset; hazard ratio = 1.990 [95% CI, 1.256-3.154], P = 0.003 in validation dataset. Furthermore, the predictive ability of these 3 biomarkers in combination was more robust than that of each individual biomarker.This technically simple immunohistochemistry-based molecular model accurately predicts ESCC patient survival and thus could serve as a complement to current clinical risk stratification approaches.

  11. Predictive Modelling Risk Calculators and the Non Dialysis Pathway.

    Science.gov (United States)

    Robins, Jennifer; Katz, Ivor

    2013-04-16

    This guideline will review the current prediction models and survival/mortality scores available for decision making in patients with advanced kidney disease who are being considered for a non-dialysis treatment pathway. Risk prediction is gaining increasing attention with emerging literature suggesting improved patient outcomes through individualised risk prediction (1). Predictive models help inform the nephrologist and the renal palliative care specialists in their discussions with patients and families about suitability or otherwise of dialysis. Clinical decision making in the care of end stage kidney disease (ESKD) patients on a non-dialysis treatment pathway is currently governed by several observational trials (3). Despite the paucity of evidence based medicine in this field, it is becoming evident that the survival advantages associated with renal replacement therapy in these often elderly patients with multiple co-morbidities and limited functional status may be negated by loss of quality of life (7) (6), further functional decline (5, 8), increased complications and hospitalisations. This article is protected by copyright. All rights reserved.

  12. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  13. An Anisotropic Hardening Model for Springback Prediction

    Science.gov (United States)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  14. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

  15. Hidden Semi-Markov Models for Predictive Maintenance

    Directory of Open Access Journals (Sweden)

    Francesco Cartella

    2015-01-01

    Full Text Available Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs with (i no constraints on the state duration density function and (ii being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL of the machine is calculated.

  16. Current models of positive mental health

    Directory of Open Access Journals (Sweden)

    Stanojević Dragana Z.

    2012-01-01

    Full Text Available The concept of positive mental health represents not merely the absence of mental disease but presence of high level of happiness and well-being. In this paper we mentioned shortly the earliest concept of mental health, presented by Marie Jahoda in the mid-twentieth century. After that, we described two traditions in understanding and researching of subjective well-being: hedonic and eudaimonic approach. First approach focuses on investigation of positive affects and happiness as emotional and life satisfaction as cognitive component of subjective well-being. Second tradition emphasizes potentials and competences that person develops to the highest level, in personal and social area. Both psychological and social well-being are core concept of positive mental health psychology, designated together as positive functioning. The psychological well-being comprises six dimensions: self-acceptance, positive relations with others, environmental mastery, autonomy, purpose of life and personal growth. Social well-being consists of five dimensions: social integration, social acceptance, social contribution, social actualization and social coherence. By integrating hedonic and eudaimonic well-being as well as absence of mental disease, Corey Keyes introduced concept of complete mental health. People with complete mental health have reported absence of disease during past year and presence of high level of emotional, psychological and social well-being (flourishing. People with incomplete mental health have also reported absence of mental disease but low level of positive functioning (languishing. Keyes thought there are people with complete and incomplete mental illness; both groups report presence of mental disease, but second group has high level of positive functioning. Models of positive mental health are widely used in research studies as well as in programs for prevention and promotion of mental health. .

  17. Wall conditioning for ITER: Current experimental and modeling activities

    Energy Technology Data Exchange (ETDEWEB)

    Douai, D., E-mail: david.douai@cea.fr [CEA, IRFM, Association Euratom-CEA, 13108 St. Paul lez Durance (France); Kogut, D. [CEA, IRFM, Association Euratom-CEA, 13108 St. Paul lez Durance (France); Wauters, T. [LPP-ERM/KMS, Association Belgian State, 1000 Brussels (Belgium); Brezinsek, S. [FZJ, Institut für Energie- und Klimaforschung Plasmaphysik, 52441 Jülich (Germany); Hagelaar, G.J.M. [Laboratoire Plasma et Conversion d’Energie, UMR5213, Toulouse (France); Hong, S.H. [National Fusion Research Institute, Daejeon 305-806 (Korea, Republic of); Lomas, P.J. [CCFE, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Lyssoivan, A. [LPP-ERM/KMS, Association Belgian State, 1000 Brussels (Belgium); Nunes, I. [Associação EURATOM-IST, Instituto de Plasmas e Fusão Nuclear, 1049-001 Lisboa (Portugal); Pitts, R.A. [ITER International Organization, F-13067 St. Paul lez Durance (France); Rohde, V. [Max-Planck-Institut für Plasmaphysik, 85748 Garching (Germany); Vries, P.C. de [ITER International Organization, F-13067 St. Paul lez Durance (France)

    2015-08-15

    Wall conditioning will be required in ITER to control fuel and impurity recycling, as well as tritium (T) inventory. Analysis of conditioning cycle on the JET, with its ITER-Like Wall is presented, evidencing reduced need for wall cleaning in ITER compared to JET–CFC. Using a novel 2D multi-fluid model, current density during Glow Discharge Conditioning (GDC) on the in-vessel plasma-facing components (PFC) of ITER is predicted to approach the simple expectation of total anode current divided by wall surface area. Baking of the divertor to 350 °C should desorb the majority of the co-deposited T. ITER foresees the use of low temperature plasma based techniques compatible with the permanent toroidal magnetic field, such as Ion (ICWC) or Electron Cyclotron Wall Conditioning (ECWC), for tritium removal between ITER plasma pulses. Extrapolation of JET ICWC results to ITER indicates removal comparable to estimated T-retention in nominal ITER D:T shots, whereas GDC may be unattractive for that purpose.

  18. A model for electron currents near a field null

    International Nuclear Information System (INIS)

    Stark, R.A.; Miley, G.H.

    1987-01-01

    The fluid approximation is invalid near a field null, since the local electron orbit size and the magnetic scale length are comparable. To model the electron currents in this region we propose a single equation of motion describing the bulk electron dynamics. The equation applies to the plasma within one thermal orbit size of the null. The region is treated as unmagnetized; electrons are accelerated by the inductive electric field and drag on ions; damping is provided by viscosity due to electrons and collisions with ions. Through variational calculations and a particle tracking code for electrons, the size of the terms in the equation of motion have been estimated. The resulting equation of motion combines with Faraday's Law to produce a governing equation which implicitly contains the self inductive field of the electrons. This governing equation predicts that viscosity prevents complete cancellation of the ion current density by the electrons in the null region. Thus electron dynamics near the field null should not prevent the formation and deepening of field reversal using neutral-beam injection

  19. Mathematical modeling and computational prediction of cancer drug resistance.

    Science.gov (United States)

    Sun, Xiaoqiang; Hu, Bin

    2017-06-23

    Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of

  20. Bayesian calibration of power plant models for accurate performance prediction

    International Nuclear Information System (INIS)

    Boksteen, Sowande Z.; Buijtenen, Jos P. van; Pecnik, Rene; Vecht, Dick van der

    2014-01-01

    Highlights: • Bayesian calibration is applied to power plant performance prediction. • Measurements from a plant in operation are used for model calibration. • A gas turbine performance model and steam cycle model are calibrated. • An integrated plant model is derived. • Part load efficiency is accurately predicted as a function of ambient conditions. - Abstract: Gas turbine combined cycles are expected to play an increasingly important role in the balancing of supply and demand in future energy markets. Thermodynamic modeling of these energy systems is frequently applied to assist in decision making processes related to the management of plant operation and maintenance. In most cases, model inputs, parameters and outputs are treated as deterministic quantities and plant operators make decisions with limited or no regard of uncertainties. As the steady integration of wind and solar energy into the energy market induces extra uncertainties, part load operation and reliability are becoming increasingly important. In the current study, methods are proposed to not only quantify various types of uncertainties in measurements and plant model parameters using measured data, but to also assess their effect on various aspects of performance prediction. The authors aim to account for model parameter and measurement uncertainty, and for systematic discrepancy of models with respect to reality. For this purpose, the Bayesian calibration framework of Kennedy and O’Hagan is used, which is especially suitable for high-dimensional industrial problems. The article derives a calibrated model of the plant efficiency as a function of ambient conditions and operational parameters, which is also accurate in part load. The article shows that complete statistical modeling of power plants not only enhances process models, but can also increases confidence in operational decisions

  1. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...

  2. Supervisory Model Predictive Control of the Heat Integrated Distillation Column

    DEFF Research Database (Denmark)

    Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted

    2017-01-01

    This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized control...... system outperforms the decentralized system, because it handles the interactions in the HIDiC process better. The integral absolute error (IAE) is reduced by a factor of 2 and a factor of 4 for control of the top and bottoms compositions, respectively....

  3. Advanced Computational Modeling Approaches for Shock Response Prediction

    Science.gov (United States)

    Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

    2015-01-01

    Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.

  4. Estimating Predictive Variance for Statistical Gas Distribution Modelling

    International Nuclear Information System (INIS)

    Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo

    2009-01-01

    Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.

  5. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  6. Predictive modelling of contagious deforestation in the Brazilian Amazon.

    Science.gov (United States)

    Rosa, Isabel M D; Purves, Drew; Souza, Carlos; Ewers, Robert M

    2013-01-01

    Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently

  7. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...

  8. Coupled assimilation for an intermediated coupled ENSO prediction model

    Science.gov (United States)

    Zheng, Fei; Zhu, Jiang

    2010-10-01

    The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind-ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997-2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event.

  9. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  10. Effective modelling for predictive analytics in data science ...

    African Journals Online (AJOL)

    Effective modelling for predictive analytics in data science. ... the nearabsence of empirical or factual predictive analytics in the mainstream research going on ... Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning.

  11. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  12. Current-voltage model of LED light sources

    DEFF Research Database (Denmark)

    Beczkowski, Szymon; Munk-Nielsen, Stig

    2012-01-01

    Amplitude modulation is rarely used for dimming light-emitting diodes in polychromatic luminaires due to big color shifts caused by varying magnitude of LED driving current and nonlinear relationship between intensity of a diode and driving current. Current-voltage empirical model of light...

  13. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  14. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.

    1995-01-01

    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  15. Towards predictive models for transitionally rough surfaces

    Science.gov (United States)

    Abderrahaman-Elena, Nabil; Garcia-Mayoral, Ricardo

    2017-11-01

    We analyze and model the previously presented decomposition for flow variables in DNS of turbulence over transitionally rough surfaces. The flow is decomposed into two contributions: one produced by the overlying turbulence, which has no footprint of the surface texture, and one induced by the roughness, which is essentially the time-averaged flow around the surface obstacles, but modulated in amplitude by the first component. The roughness-induced component closely resembles the laminar steady flow around the roughness elements at the same non-dimensional roughness size. For small - yet transitionally rough - textures, the roughness-free component is essentially the same as over a smooth wall. Based on these findings, we propose predictive models for the onset of the transitionally rough regime. Project supported by the Engineering and Physical Sciences Research Council (EPSRC).

  16. Database and prediction model for CANDU pressure tube diameter

    Energy Technology Data Exchange (ETDEWEB)

    Jung, J.Y.; Park, J.H. [Korea Atomic Energy Research Inst., Daejeon (Korea, Republic of)

    2014-07-01

    The pressure tube (PT) diameter is basic data in evaluating the CCP (critical channel power) of a CANDU reactor. Since the CCP affects the operational margin directly, an accurate prediction of the PT diameter is important to assess the operational margin. However, the PT diameter increases by creep owing to the effects of irradiation by neutron flux, stress, and reactor operating temperatures during the plant service period. Thus, it has been necessary to collect the measured data of the PT diameter and establish a database (DB) and develop a prediction model of PT diameter. Accordingly, in this study, a DB for the measured PT diameter data was established and a neural network (NN) based diameter prediction model was developed. The established DB included not only the measured diameter data but also operating conditions such as the temperature, pressure, flux, and effective full power date. The currently developed NN based diameter prediction model considers only extrinsic variables such as the operating conditions, and will be enhanced to consider the effect of intrinsic variables such as the micro-structure of the PT material. (author)

  17. Multivariate Models for Prediction of Human Skin Sensitization ...

    Science.gov (United States)

    One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens TM assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches , logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine

  18. Resource-estimation models and predicted discovery

    International Nuclear Information System (INIS)

    Hill, G.W.

    1982-01-01

    Resources have been estimated by predictive extrapolation from past discovery experience, by analogy with better explored regions, or by inference from evidence of depletion of targets for exploration. Changes in technology and new insights into geological mechanisms have occurred sufficiently often in the long run to form part of the pattern of mature discovery experience. The criterion, that a meaningful resource estimate needs an objective measure of its precision or degree of uncertainty, excludes 'estimates' based solely on expert opinion. This is illustrated by development of error measures for several persuasive models of discovery and production of oil and gas in USA, both annually and in terms of increasing exploration effort. Appropriate generalizations of the models resolve many points of controversy. This is illustrated using two USA data sets describing discovery of oil and of U 3 O 8 ; the latter set highlights an inadequacy of available official data. Review of the oil-discovery data set provides a warrant for adjusting the time-series prediction to a higher resource figure for USA petroleum. (author)

  19. Modeling the current distribution in HTS tapes with transport current and applied magnetic field

    NARCIS (Netherlands)

    Yazawa, T.; Yazawa, Takashi; Rabbers, J.J.; Chevtchenko, O.A.; ten Haken, Bernard; ten Kate, Herman H.J.; Maeda, Hideaki

    1999-01-01

    A numerical model is developed for the current distribution in a high temperature superconducting (HTS) tape, (Bi,Pb)2Sr2 Ca2Cu3Ox-Ag, subjected to a combination of a transport current and an applied magnetic field. This analysis is based on a two-dimensional formulation of Maxwell's equations in

  20. Prediction of pipeline corrosion rate based on grey Markov models

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

    2009-01-01

    Based on the model that combined by grey model and Markov model, the prediction of corrosion rate of nuclear power pipeline was studied. Works were done to improve the grey model, and the optimization unbiased grey model was obtained. This new model was used to predict the tendency of corrosion rate, and the Markov model was used to predict the residual errors. In order to improve the prediction precision, rolling operation method was used in these prediction processes. The results indicate that the improvement to the grey model is effective and the prediction precision of the new model combined by the optimization unbiased grey model and Markov model is better, and the use of rolling operation method may improve the prediction precision further. (authors)

  1. Biophysical models of larval dispersal in the Benguela Current ...

    African Journals Online (AJOL)

    We synthesise and update results from the suite of biophysical, larval-dispersal models developed in the Benguela Current ecosystem. Biophysical models of larval dispersal use outputs of physical hydrodynamic models as inputs to individual-based models in which biological processes acting during the larval life are ...

  2. An Operational Model for the Prediction of Jet Blast

    Science.gov (United States)

    2012-01-09

    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  3. Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding.

    Science.gov (United States)

    Zhang, Xianguo; Huang, Tiejun; Tian, Yonghong; Gao, Wen

    2014-02-01

    The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.

  4. Utilizing Data Mining for Predictive Modeling of Colorectal Cancer using Electronic Medical Records

    NARCIS (Netherlands)

    Hoogendoorn, M.; Moons, L.G.; Numans, M.E.; Sips, R.J.

    2014-01-01

    Colorectal cancer (CRC) is a relatively common cause of death around the globe. Predictive models for the development of CRC could be highly valuable and could facilitate an early diagnosis and increased survival rates. Currently available predictive models are improving, but do not fully utilize

  5. Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

    Science.gov (United States)

    Reinkensmeyer, David J; Burdet, Etienne; Casadio, Maura; Krakauer, John W; Kwakkel, Gert; Lang, Catherine E; Swinnen, Stephan P; Ward, Nick S; Schweighofer, Nicolas

    2016-04-30

    Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

  6. Data driven propulsion system weight prediction model

    Science.gov (United States)

    Gerth, Richard J.

    1994-10-01

    The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

  7. Predictive modeling of emergency cesarean delivery.

    Directory of Open Access Journals (Sweden)

    Carlos Campillo-Artero

    Full Text Available To increase discriminatory accuracy (DA for emergency cesarean sections (ECSs.We prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous communities of Spain. To identify risk factors (RFs for ECS, we used likelihood ratios and logistic regression, fitted a classification tree (CTREE, and analyzed a random forest model (RFM. We used the areas under the receiver-operating-characteristic (ROC curves (AUCs to assess their DA.The magnitude of the LR+ for all putative individual RFs and ORs in the logistic regression models was low to moderate. Except for parity, all putative RFs were positively associated with ECS, including hospital fixed-effects and night-shift delivery. The DA of all logistic models ranged from 0.74 to 0.81. The most relevant RFs (pH, induction, and previous C-section in the CTREEs showed the highest ORs in the logistic models. The DA of the RFM and its most relevant interaction terms was even higher (AUC = 0.94; 95% CI: 0.93-0.95.Putative fetal, maternal, and contextual RFs alone fail to achieve reasonable DA for ECS. It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications.

  8. To predict the niche, model colonization and extinction

    Science.gov (United States)

    Yackulic, Charles B.; Nichols, James D.; Reid, Janice; Der, Ricky

    2015-01-01

    Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species' niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both because of ongoing invasions and because the distribution of suitable environmental conditions is always changing. This mismatch between the equilibrium assumptions inherent in many analyses and the disequilibrium conditions in the real world leads to inaccurate predictions of species' geographic distributions and suggests the need for theory and analytical tools that avoid equilibrium assumptions. Here, we develop a general theory of environmental associations during periods of transient dynamics. We show that time-invariant relationships between environmental conditions and rates of local colonization and extinction can produce substantial temporal variation in occupancy–environment relationships. We then estimate occupancy–environment relationships during three avian invasions. Changes in occupancy–environment relationships over time differ among species but are predicted by dynamic occupancy models. Since estimates of the occupancy–environment relationships themselves are frequently poor predictors of future occupancy patterns, research should increasingly focus on characterizing how rates of local colonization and extinction vary with environmental conditions.

  9. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

  10. 'Will I want these stickers tomorrow?' Preschoolers' ability to predict current and future needs.

    Science.gov (United States)

    Martin-Ordas, Gema

    2017-11-01

    Between 3 and 5 years of age, children develop the ability to plan for their own and others' future needs; however, they have great difficulty predicting future needs that conflict with current ones. Importantly, this ability has only been tested in the domain of physiological states (e.g., thirst). Therefore, it is still an open question whether in a different context preschoolers can disengage from their current needs to secure a different future one. In a Resource Allocation task, 4- and 5-year-olds had to distribute three types of rewards between themselves and another child for either 'right now' or 'tomorrow'. Children's current needs were manipulated by providing them (or not) with their preferred reward at beginning of the task. Only 5-year-olds could predict future needs that conflict with their current ones and act accordingly. Younger children's performance is discussed in the context of temporal and social distance. Statement of contribution What is already known on this subject? By the age of 4, children can plan for their own and others' future needs. Seven-year-old children still have difficulty predicting future physiological needs that conflict with their current ones. What does this study add? In a Resource Allocation task, preschoolers had to share rewards with another child for 'right now' or 'tomorrow'. Children's current needs were manipulated by providing them (or not) with their preferred reward. This study shows that 5-year-olds can predict future (non-physiological) needs that conflict with their current ones. © 2017 The British Psychological Society.

  11. Prediction of transverse asymmetries in MHD ducts with zero net Hall current

    International Nuclear Information System (INIS)

    Swean, T.F. Jr.; Oliver, D.A.; Maxwell, C.D.; Demetriades, S.T.

    1981-01-01

    A new class of fluid-electrical asymmetries in MHD generator channel flow are predicted. It is shown that the existence of interelectrode asymmetries is not confined to generators in which there exists a nonzero net axial current, but rather they are induced even in the case of the Faraday generators. Also demonstrated is the impact of these asymmetries upon the generator and diffuser flow. It is concluded that in MHD generators, the net axial current in the cross plane is identically zero, while at any given point in the plane, the local Hall current density is in general nonzero

  12. Decline curve based models for predicting natural gas well performance

    Directory of Open Access Journals (Sweden)

    Arash Kamari

    2017-06-01

    Full Text Available The productivity of a gas well declines over its production life as cannot cover economic policies. To overcome such problems, the production performance of gas wells should be predicted by applying reliable methods to analyse the decline trend. Therefore, reliable models are developed in this study on the basis of powerful artificial intelligence techniques viz. the artificial neural network (ANN modelling strategy, least square support vector machine (LSSVM approach, adaptive neuro-fuzzy inference system (ANFIS, and decision tree (DT method for the prediction of cumulative gas production as well as initial decline rate multiplied by time as a function of the Arps' decline curve exponent and ratio of initial gas flow rate over total gas flow rate. It was concluded that the results obtained based on the models developed in current study are in satisfactory agreement with the actual gas well production data. Furthermore, the results of comparative study performed demonstrates that the LSSVM strategy is superior to the other models investigated for the prediction of both cumulative gas production, and initial decline rate multiplied by time.

  13. Probabilistic predictive modelling of carbon nanocomposites for medical implants design.

    Science.gov (United States)

    Chua, Matthew; Chui, Chee-Kong

    2015-04-01

    Modelling of the mechanical properties of carbon nanocomposites based on input variables like percentage weight of Carbon Nanotubes (CNT) inclusions is important for the design of medical implants and other structural scaffolds. Current constitutive models for the mechanical properties of nanocomposites may not predict well due to differences in conditions, fabrication techniques and inconsistencies in reagents properties used across industries and laboratories. Furthermore, the mechanical properties of the designed products are not deterministic, but exist as a probabilistic range. A predictive model based on a modified probabilistic surface response algorithm is proposed in this paper to address this issue. Tensile testing of three groups of different CNT weight fractions of carbon nanocomposite samples displays scattered stress-strain curves, with the instantaneous stresses assumed to vary according to a normal distribution at a specific strain. From the probabilistic density function of the experimental data, a two factors Central Composite Design (CCD) experimental matrix based on strain and CNT weight fraction input with their corresponding stress distribution was established. Monte Carlo simulation was carried out on this design matrix to generate a predictive probabilistic polynomial equation. The equation and method was subsequently validated with more tensile experiments and Finite Element (FE) studies. The method was subsequently demonstrated in the design of an artificial tracheal implant. Our algorithm provides an effective way to accurately model the mechanical properties in implants of various compositions based on experimental data of samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Social network models predict movement and connectivity in ecological landscapes

    Science.gov (United States)

    Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.

    2011-01-01

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  15. Social network models predict movement and connectivity in ecological landscapes.

    Science.gov (United States)

    Fletcher, Robert J; Acevedo, Miguel A; Reichert, Brian E; Pias, Kyle E; Kitchens, Wiley M

    2011-11-29

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  16. Methodology for Designing Models Predicting Success of Infertility Treatment

    OpenAIRE

    Alireza Zarinara; Mohammad Mahdi Akhondi; Hojjat Zeraati; Koorsh Kamali; Kazem Mohammad

    2016-01-01

    Abstract Background: The prediction models for infertility treatment success have presented since 25 years ago. There are scientific principles for designing and applying the prediction models that is also used to predict the success rate of infertility treatment. The purpose of this study is to provide basic principles for designing the model to predic infertility treatment success. Materials and Methods: In this paper, the principles for developing predictive models are explained and...

  17. Mathematical models to predict rheological parameters of lateritic hydromixtures

    Directory of Open Access Journals (Sweden)

    Gabriel Hernández-Ramírez

    2017-10-01

    Full Text Available The present work had as objective to establish mathematical models that allow the prognosis of the rheological parameters of the lateritic pulp at concentrations of solids from 35% to 48%, temperature of the preheated hydromixture superior to 82 ° C and number of mineral between 3 and 16. Four samples of lateritic pulp were used in the study at different process locations. The results allowed defining that the plastic properties of the lateritic pulp in the conditions of this study conform to the Herschel-Bulkley model for real plastics. In addition, they show that for current operating conditions, even for new situations, UPD mathematical models have a greater ability to predict rheological parameters than least squares mathematical models.

  18. Dust Composition in Climate Models: Current Status and Prospects

    Science.gov (United States)

    Pérez García-Pando, C.; Miller, R. L.; Perlwitz, J. P.; Kok, J. F.; Scanza, R.; Mahowald, N. M.

    2015-12-01

    Mineral dust created by wind erosion of soil particles is the dominant aerosol by mass in the atmosphere. It exerts significant effects on radiative fluxes, clouds, ocean biogeochemistry, and human health. Models that predict the lifecycle of mineral dust aerosols generally assume a globally uniform mineral composition. However, this simplification limits our understanding of the role of dust in the Earth system, since the effects of dust strongly depend on the particles' physical and chemical properties, which vary with their mineral composition. Hence, not only a detailed understanding of the processes determining the dust emission flux is needed, but also information about its size dependent mineral composition. Determining the mineral composition of dust aerosols is complicated. The largest uncertainty derives from the current atlases of soil mineral composition. These atlases provide global estimates of soil mineral fractions, but they are based upon massive extrapolation of a limited number of soil samples assuming that mineral composition is related to soil type. This disregards the potentially large variability of soil properties within each defined soil type. In addition, the analysis of these soil samples is based on wet sieving, a technique that breaks the aggregates found in the undisturbed parent soil. During wind erosion, these aggregates are subject to partial fragmentation, which generates differences on the size distribution and composition between the undisturbed parent soil and the emitted dust aerosols. We review recent progress on the representation of the mineral and chemical composition of dust in climate models. We discuss extensions of brittle fragmentation theory to prescribe the emitted size-resolved dust composition, and we identify key processes and uncertainties based upon model simulations and an unprecedented compilation of observations.

  19. Finite Unification: Theory, Models and Predictions

    CERN Document Server

    Heinemeyer, S; Zoupanos, G

    2011-01-01

    All-loop Finite Unified Theories (FUTs) are very interesting N=1 supersymmetric Grand Unified Theories (GUTs) realising an old field theory dream, and moreover have a remarkable predictive power due to the required reduction of couplings. The reduction of the dimensionless couplings in N=1 GUTs is achieved by searching for renormalization group invariant (RGI) relations among them holding beyond the unification scale. Finiteness results from the fact that there exist RGI relations among dimensional couplings that guarantee the vanishing of all beta-functions in certain N=1 GUTs even to all orders. Furthermore developments in the soft supersymmetry breaking sector of N=1 GUTs and FUTs lead to exact RGI relations, i.e. reduction of couplings, in this dimensionful sector of the theory, too. Based on the above theoretical framework phenomenologically consistent FUTs have been constructed. Here we review FUT models based on the SU(5) and SU(3)^3 gauge groups and their predictions. Of particular interest is the Hig...

  20. An analytical model for the prediction of rip spacing in intermediate ...

    Indian Academy of Sciences (India)

    61

    study, an analytical model was presented to predict the spacing of channel rip currents (Srip) ... beach and normal wave incidence in two cases with the fixed breaking line and variable one .... On the other hand, in the above relations radiation.

  1. An Evaluation of Growth Models as Predictive Tools for Estimates at Completion (EAC)

    National Research Council Canada - National Science Library

    Trahan, Elizabeth N

    2009-01-01

    ...) as the Estimates at Completion (EAC). Our research evaluates the prospect of nonlinear growth modeling as an alternative to the current predictive tools used for calculating EAC, such as the Cost Performance Index (CPI...

  2. Prediction of acute respiratory disease in current and former smokers with and without COPD.

    Science.gov (United States)

    Bowler, Russell P; Kim, Victor; Regan, Elizabeth; Williams, André A A; Santorico, Stephanie A; Make, Barry J; Lynch, David A; Hokanson, John E; Washko, George R; Bercz, Peter; Soler, Xavier; Marchetti, Nathaniel; Criner, Gerard J; Ramsdell, Joe; Han, MeiLan K; Demeo, Dawn; Anzueto, Antonio; Comellas, Alejandro; Crapo, James D; Dransfield, Mark; Wells, J Michael; Hersh, Craig P; MacIntyre, Neil; Martinez, Fernando; Nath, Hrudaya P; Niewoehner, Dennis; Sciurba, Frank; Sharafkhaneh, Amir; Silverman, Edwin K; van Beek, Edwin J R; Wilson, Carla; Wendt, Christine; Wise, Robert A

    2014-10-01

    The risk factors for acute episodes of respiratory disease in current and former smokers who do not have COPD are unknown. Eight thousand two hundred forty-six non-Hispanic white and black current and former smokers in the Genetic Epidemiology of COPD (COPDGene) cohort had longitudinal follow-up (LFU) every 6 months to determine acute respiratory episodes requiring antibiotics or systemic corticosteroids, an ED visit, or hospitalization. Negative binomial regression was used to determine the factors associated with acute respiratory episodes. A Cox proportional hazards model was used to determine adjusted hazard ratios (HRs) for time to first episode and an acute episode of respiratory disease risk score. At enrollment, 4,442 subjects did not have COPD, 658 had mild COPD, and 3,146 had moderate or worse COPD. Nine thousand three hundred three acute episodes of respiratory disease and 2,707 hospitalizations were reported in LFU (3,044 acute episodes of respiratory disease and 827 hospitalizations in those without COPD). Major predictors included acute episodes of respiratory disease in year prior to enrollment (HR, 1.20; 95% CI, 1.15-1.24 per exacerbation), airflow obstruction (HR, 0.94; 95% CI, 0.91-0.96 per 10% change in % predicted FEV1), and poor health-related quality of life (HR, 1.07; 95% CI, 1.06-1.08 for each 4-unit increase in St. George's Respiratory Questionnaire score). Risks were similar for those with and without COPD. Although acute episode of respiratory disease rates are higher in subjects with COPD, risk factors are similar, and at a population level, there are more episodes in smokers without COPD.

  3. Prediction of Acute Respiratory Disease in Current and Former Smokers With and Without COPD

    Science.gov (United States)

    Kim, Victor; Regan, Elizabeth; Williams, André A. A.; Santorico, Stephanie A.; Make, Barry J.; Lynch, David A.; Hokanson, John E.; Washko, George R.; Bercz, Peter; Soler, Xavier; Marchetti, Nathaniel; Criner, Gerard J.; Ramsdell, Joe; Han, MeiLan K.; Demeo, Dawn; Anzueto, Antonio; Comellas, Alejandro; Crapo, James D.; Dransfield, Mark; Wells, J. Michael; Hersh, Craig P.; MacIntyre, Neil; Martinez, Fernando; Nath, Hrudaya P.; Niewoehner, Dennis; Sciurba, Frank; Sharafkhaneh, Amir; Silverman, Edwin K.; van Beek, Edwin J. R.; Wilson, Carla; Wendt, Christine; Wise, Robert A.; Curtis, Jeffrey; Kazerooni, Ella; Hanania, Nicola; Alapat, Philip; Bandi, Venkata; Guntupalli, Kalpalatha; Guy, Elizabeth; Lunn, William; Mallampalli, Antara; Trinh, Charles; Atik, Mustafa; DeMeo, Dawn; Hersh, Craig; Jacobson, Francine; Graham Barr, R.; Thomashow, Byron; Austin, John; MacIntyre, Neil; Washington, Lacey; Page McAdams, H.; Rosiello, Richard; Bresnahan, Timothy; McEvoy, Charlene; Tashjian, Joseph; Wise, Robert; Hansel, Nadia; Brown, Robert; Casaburi, Richard; Porszasz, Janos; Fischer, Hans; Budoff, Matt; Sharafkhaneh, Amir; Niewoehner, Dennis; Allen, Tadashi; Rice, Kathryn; Foreman, Marilyn; Westney, Gloria; Berkowitz, Eugene; Bowler, Russell; Friedlander, Adam; Meoni, Eleonora; Criner, Gerard; Kim, Victor; Marchetti, Nathaniel; Satti, Aditi; James Mamary, A.; Steiner, Robert; Dass, Chandra; Bailey, William; Dransfield, Mark; Gerald, Lynn; Nath, Hrudaya; Ramsdell, Joe; Ferguson, Paul; Friedman, Paul; McLennan, Geoffrey; van Beek, Edwin JR; Martinez, Fernando; Han, MeiLan; Thompson, Deborah; Kazerooni, Ella; Wendt, Christine; Allen, Tadashi; Sciurba, Frank; Weissfeld, Joel; Fuhrman, Carl; Bon, Jessica; Anzueto, Antonio; Adams, Sandra; Orozco, Carlos; Santiago Restrepo, C.; Mumbower, Amy; Crapo, James; Silverman, Edwin; Make, Barry; Regan, Elizabeth; Samet, Jonathan; Willis, Amy; Stinson, Douglas; Beaty, Terri; Klanderman, Barbara; Laird, Nan; Lange, Christoph; Ionita, Iuliana; Santorico, Stephanie; Silverman, Edwin; Lynch, David; Schroeder, Joyce; Newell, John; Reilly, John; Coxson, Harvey; Judy, Philip; Hoffman, Eric; San Jose Estepar, Raul; Washko, George; Leek, Rebecca; Zach, Jordan; Kluiber, Alex; Rodionova, Anastasia; Mann, Tanya; Crapo, Robert; Jensen, Robert; Farzadegan, Homayoon; Murphy, James; Everett, Douglas; Wilson, Carla; Hokanson, John

    2014-01-01

    BACKGROUND: The risk factors for acute episodes of respiratory disease in current and former smokers who do not have COPD are unknown. METHODS: Eight thousand two hundred forty-six non-Hispanic white and black current and former smokers in the Genetic Epidemiology of COPD (COPDGene) cohort had longitudinal follow-up (LFU) every 6 months to determine acute respiratory episodes requiring antibiotics or systemic corticosteroids, an ED visit, or hospitalization. Negative binomial regression was used to determine the factors associated with acute respiratory episodes. A Cox proportional hazards model was used to determine adjusted hazard ratios (HRs) for time to first episode and an acute episode of respiratory disease risk score. RESULTS: At enrollment, 4,442 subjects did not have COPD, 658 had mild COPD, and 3,146 had moderate or worse COPD. Nine thousand three hundred three acute episodes of respiratory disease and 2,707 hospitalizations were reported in LFU (3,044 acute episodes of respiratory disease and 827 hospitalizations in those without COPD). Major predictors included acute episodes of respiratory disease in year prior to enrollment (HR, 1.20; 95% CI, 1.15-1.24 per exacerbation), airflow obstruction (HR, 0.94; 95% CI, 0.91-0.96 per 10% change in % predicted FEV1), and poor health-related quality of life (HR, 1.07; 95% CI, 1.06-1.08 for each 4-unit increase in St. George’s Respiratory Questionnaire score). Risks were similar for those with and without COPD. CONCLUSIONS: Although acute episode of respiratory disease rates are higher in subjects with COPD, risk factors are similar, and at a population level, there are more episodes in smokers without COPD. PMID:24945159

  4. Revised predictive equations for salt intrusion modelling in estuaries

    NARCIS (Netherlands)

    Gisen, J.I.A.; Savenije, H.H.G.; Nijzink, R.C.

    2015-01-01

    For one-dimensional salt intrusion models to be predictive, we need predictive equations to link model parameters to observable hydraulic and geometric variables. The one-dimensional model of Savenije (1993b) made use of predictive equations for the Van der Burgh coefficient $K$ and the dispersion

  5. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...

  6. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  7. Model predictive control of a wind turbine modelled in Simpack

    International Nuclear Information System (INIS)

    Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G

    2014-01-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine

  8. Model predictive control of a wind turbine modelled in Simpack

    Science.gov (United States)

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

    2014-06-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  9. Poisson Mixture Regression Models for Heart Disease Prediction.

    Science.gov (United States)

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

  10. Energy infrastructure modeling for the oil sands industry: Current situation

    International Nuclear Information System (INIS)

    Lazzaroni, Edoardo Filippo; Elsholkami, Mohamed; Arbiv, Itai; Martelli, Emanuele; Elkamel, Ali; Fowler, Michael

    2016-01-01

    Highlights: • A simulation-based modelling of energy demands of oil sands operations is proposed. • Aspen simulations used to simulate delayed coking-based upgrading of bitumen. • The energy infrastructure is simulated using Aspen Plus achieving self-sufficiency. • Various scenarios affecting energy demand intensities are investigated. • Energy and CO_2 emission intensities of integrated SAGD/upgrading are estimated. - Abstract: In this study, the total energy requirements associated with the production of bitumen from oil sands and its upgrading to synthetic crude oil (SCO) are modeled and quantified. The production scheme considered is based on the commercially applied steam assisted gravity drainage (SAGD) for bitumen extraction and delayed coking for bitumen upgrading. In addition, the model quantifies the greenhouse gas (GHG) emissions associated with the production of energy required for these operations from technologies utilized in the currently existing oil sands energy infrastructure. The model is based on fundamental engineering principles, and Aspen HYSYS and Aspen Plus simulations. The energy demand results are expressed in terms of heat, power, hydrogen, and process fuel consumption rates for SAGD extraction and bitumen upgrading. Based on the model’s output, a range of overall energy and emission intensity factors are estimated for a bitumen production rate of 112,500 BPD (or 93,272 BPD of SCO), which were determined to be 262.5–368.5 MJ/GJ_S_C_O and 14.17–19.84 gCO_2/MJ_S_C_O, respectively. The results of the model indicate that the majority of GHG emissions are generated during SAGD extraction (up to 60% of total emissions) due to the combustion of natural gas for steam production, and the steam-to-oil ratio is a major parameter affecting total GHG emissions. The developed model can be utilized as a tool to predict the energy demand requirements for integrated SAGD/upgrading projects under different operating conditions, and

  11. Modelling of the ring current in Saturn's magnetosphere

    Directory of Open Access Journals (Sweden)

    G. Giampieri

    2004-01-01

    Full Text Available The existence of a ring current inside Saturn's magnetosphere was first suggested by Smith et al. (1980 and Ness et al. (1981, 1982, in order to explain various features in the magnetic field observations from the Pioneer 11 and Voyager 1 and 2 spacecraft. Connerney et al. (1983 formalized the equatorial current model, based on previous modelling work of Jupiter's current sheet and estimated its parameters from the two Voyager data sets. Here, we investigate the model further, by reconsidering the data from the two Voyager spacecraft, as well as including the Pioneer 11 flyby data set. First, we obtain, in closed form, an analytic expression for the magnetic field produced by the ring current. We then fit the model to the external field, that is the difference between the observed field and the internal magnetic field, considering all the available data. In general, through our global fit we obtain more accurate parameters, compared to previous models. We point out differences between the model's parameters for the three flybys, and also investigate possible deviations from the axial and planar symmetries assumed in the model. We conclude that an accurate modelling of the Saturnian disk current will require taking into account both of the temporal variations related to the condition of the magnetosphere, as well as non-axisymmetric contributions due to local time effects. Key words. Magnetospheric physics (current systems; planetary magnetospheres; plasma sheet

  12. Current and Predicted Fertility using Poisson Regression Model ...

    African Journals Online (AJOL)

    AJRH Managing Editor

    We used descriptive statistics and analysis of variance (ANOVA) to ... women and estimated the probabilities of a woman ... observed events, the probability of finding more than one ...... Fahrmeir L & Lang S. Bayesian inference for generalized.

  13. Merging Imagery and Models for River Current Prediction

    Science.gov (United States)

    2011-01-01

    synthetically generated bathymetry. Measured Batbymel ry Synthetic Batbymel rj Mooring Mean Difference (nn/s) Correlation Mian Difference (cm s...8217orrelal ion Al Mi 0.90 27 077 A 2 17 0.86 21 n 36 Bl 17 0.87 21 B2 17 0.89 2 1 DJO 133 li , 0.87 23 0.87 is that mean differences between tlie

  14. Dinucleotide controlled null models for comparative RNA gene prediction

    Directory of Open Access Journals (Sweden)

    Gesell Tanja

    2008-05-01

    Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

  15. Dinucleotide controlled null models for comparative RNA gene prediction.

    Science.gov (United States)

    Gesell, Tanja; Washietl, Stefan

    2008-05-27

    Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

  16. Development of a Predictive Model for Induction Success of Labour

    Directory of Open Access Journals (Sweden)

    Cristina Pruenza

    2018-03-01

    Full Text Available Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements or less frequently, social (elective inductions for convenience. The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn, costs (medication, hospitalization, qualified staff and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the

  17. Usability Prediction & Ranking of SDLC Models Using Fuzzy Hierarchical Usability Model

    Science.gov (United States)

    Gupta, Deepak; Ahlawat, Anil K.; Sagar, Kalpna

    2017-06-01

    Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices. In order to overcome these challenges, this paper aims to define the term `usability' using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.

  18. Preventing patient absenteeism: validation of a predictive overbooking model.

    Science.gov (United States)

    Reid, Mark W; Cohen, Samuel; Wang, Hank; Kaung, Aung; Patel, Anish; Tashjian, Vartan; Williams, Demetrius L; Martinez, Bibiana; Spiegel, Brennan M R

    2015-12-01

    To develop a model that identifies patients at high risk for missing scheduled appointments ("no-shows" and cancellations) and to project the impact of predictive overbooking in a gastrointestinal endoscopy clinic-an exemplar resource-intensive environment with a high no-show rate. We retrospectively developed an algorithm that uses electronic health record (EHR) data to identify patients who do not show up to their appointments. Next, we prospectively validated the algorithm at a Veterans Administration healthcare network clinic. We constructed a multivariable logistic regression model that assigned a no-show risk score optimized by receiver operating characteristic curve analysis. Based on these scores, we created a calendar of projected open slots to offer to patients and compared the daily performance of predictive overbooking with fixed overbooking and typical "1 patient, 1 slot" scheduling. Data from 1392 patients identified several predictors of no-show, including previous absenteeism, comorbid disease burden, and current diagnoses of mood and substance use disorders. The model correctly classified most patients during the development (area under the curve [AUC] = 0.80) and validation phases (AUC = 0.75). Prospective testing in 1197 patients found that predictive overbooking averaged 0.51 unused appointments per day versus 6.18 for typical booking (difference = -5.67; 95% CI, -6.48 to -4.87; P < .0001). Predictive overbooking could have increased service utilization from 62% to 97% of capacity, with only rare clinic overflows. Information from EHRs can accurately predict whether patients will no-show. This method can be used to overbook appointments, thereby maximizing service utilization while staying within clinic capacity.

  19. The influence of coarse-scale environmental features on current and predicted future distributions of narrow-range endemic crayfish populations

    Science.gov (United States)

    Dyer, Joseph J.; Brewer, Shannon K.; Worthington, Thomas A.; Bergey, Elizabeth A.

    2013-01-01

    1.A major limitation to effective management of narrow-range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2.Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate-change scenarios. 3.The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 6587% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4.Current models created using two spatial resolutions (1 and 4.5km2) showed that fine-resolution data more accurately represented current distributions. For three of the four species, the 1-km2 resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1-km2 resolution models were more accurate than 4.5-km2 resolution models. 5.Future projected (4.5-km2 resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low-emission scenario

  20. Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance.

    Science.gov (United States)

    Smith, Lauren N; Makam, Anil N; Darden, Douglas; Mayo, Helen; Das, Sandeep R; Halm, Ethan A; Nguyen, Oanh Kieu

    2018-01-01

    Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown. We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models. Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions

  1. Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

    International Nuclear Information System (INIS)

    Kavaklioglu, Kadir

    2011-01-01

    Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)

  2. Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model

    Directory of Open Access Journals (Sweden)

    Yu-Ri Kim

    2016-03-01

    Full Text Available Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels (i.e., asset rating methodology, a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current study developed a simplified performance prediction model through experimental design, energy simulations and ANOVA (analysis of variance. Furthermore, using the new prediction model, a related mobile application was also developed.

  3. Electromagnetic modeling method for eddy current signal analysis

    International Nuclear Information System (INIS)

    Lee, D. H.; Jung, H. K.; Cheong, Y. M.; Lee, Y. S.; Huh, H.; Yang, D. J.

    2004-10-01

    An electromagnetic modeling method for eddy current signal analysis is necessary before an experiment is performed. Electromagnetic modeling methods consists of the analytical method and the numerical method. Also, the numerical methods can be divided by Finite Element Method(FEM), Boundary Element Method(BEM) and Volume Integral Method(VIM). Each modeling method has some merits and demerits. Therefore, the suitable modeling method can be chosen by considering the characteristics of each modeling. This report explains the principle and application of each modeling method and shows the comparison modeling programs

  4. Current algebra of classical non-linear sigma models

    International Nuclear Information System (INIS)

    Forger, M.; Laartz, J.; Schaeper, U.

    1992-01-01

    The current algebra of classical non-linear sigma models on arbitrary Riemannian manifolds is analyzed. It is found that introducing, in addition to the Noether current j μ associated with the global symmetry of the theory, a composite scalar field j, the algebra closes under Poisson brackets. (orig.)

  5. Recent advances using rodent models for predicting human allergenicity

    International Nuclear Information System (INIS)

    Knippels, Leon M.J.; Penninks, Andre H.

    2005-01-01

    The potential allergenicity of newly introduced proteins in genetically engineered foods has become an important safety evaluation issue. However, to evaluate the potential allergenicity and the potency of new proteins in our food, there are still no widely accepted and reliable test systems. The best-known allergy assessment proposal for foods derived from genetically engineered plants was the careful stepwise process presented in the so-called ILSI/IFBC decision tree. A revision of this decision tree strategy was proposed by a FAO/WHO expert consultation. As prediction of the sensitizing potential of the novel introduced protein based on animal testing was considered to be very important, animal models were introduced as one of the new test items, despite the fact that non of the currently studied models has been widely accepted and validated yet. In this paper, recent results are summarized of promising models developed in rat and mouse

  6. Cardiac magnetic source imaging based on current multipole model

    International Nuclear Information System (INIS)

    Tang Fa-Kuan; Wang Qian; Hua Ning; Lu Hong; Tang Xue-Zheng; Ma Ping

    2011-01-01

    It is widely accepted that the heart current source can be reduced into a current multipole. By adopting three linear inverse methods, the cardiac magnetic imaging is achieved in this article based on the current multipole model expanded to the first order terms. This magnetic imaging is realized in a reconstruction plane in the centre of human heart, where the current dipole array is employed to represent realistic cardiac current distribution. The current multipole as testing source generates magnetic fields in the measuring plane, serving as inputs of cardiac magnetic inverse problem. In the heart-torso model constructed by boundary element method, the current multipole magnetic field distribution is compared with that in the homogeneous infinite space, and also with the single current dipole magnetic field distribution. Then the minimum-norm least-squares (MNLS) method, the optimal weighted pseudoinverse method (OWPIM), and the optimal constrained linear inverse method (OCLIM) are selected as the algorithms for inverse computation based on current multipole model innovatively, and the imaging effects of these three inverse methods are compared. Besides, two reconstructing parameters, residual and mean residual, are also discussed, and their trends under MNLS, OWPIM and OCLIM each as a function of SNR are obtained and compared. (general)

  7. Current algebra of WZNW models at and away from criticality

    International Nuclear Information System (INIS)

    Abdalla, E.; Forger, M.

    1992-01-01

    In this paper, the authors derive the current algebra of principal chiral models with a Wess-Zumino term. At the critical coupling where the model becomes conformally invariant (Wess-Zumino-Novikov-Witten theory), this algebra reduces to two commuting Kac-Moody algebras, while in the limit where the coupling constant is taken to zero (ordinary chiral model), we recover the current algebra of that model. In this way, the latter is explicitly realized as a deformation of the former, with the coupling constant as the deformation parameter

  8. An investigation of r.f. travelling wave current drive using the model

    International Nuclear Information System (INIS)

    Bertram, W.K.

    1988-01-01

    Previous experimental investigations in the use of travelling r.f. waves to drive steady toroidal currents in a toroidal plasma have shown that I t , the amount of current driven, is strongly dependent on the ratio of the static toroidal magnetic field B z , to the strength of the r.f. magnetic field B ω . This dependence is characterised by an initial increase and subsequent decrease of I t when B t /B ω increases. It is shown that this observed behaviour is entirely consistent with the behaviour predicted by the current drive model. Results from numerical computations using the model show good quantitative agreement with the published experimental results

  9. Numerical modeling capabilities to predict repository performance

    International Nuclear Information System (INIS)

    1979-09-01

    This report presents a summary of current numerical modeling capabilities that are applicable to the design and performance evaluation of underground repositories for the storage of nuclear waste. The report includes codes that are available in-house, within Golder Associates and Lawrence Livermore Laboratories; as well as those that are generally available within the industry and universities. The first listing of programs are in-house codes in the subject areas of hydrology, solute transport, thermal and mechanical stress analysis, and structural geology. The second listing of programs are divided by subject into the following categories: site selection, structural geology, mine structural design, mine ventilation, hydrology, and mine design/construction/operation. These programs are not specifically designed for use in the design and evaluation of an underground repository for nuclear waste; but several or most of them may be so used

  10. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

  11. Predicting future glacial lakes in Austria using different modelling approaches

    Science.gov (United States)

    Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus

    2017-04-01

    Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers

  12. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

    Directory of Open Access Journals (Sweden)

    Cecilia Noecker

    2015-03-01

    Full Text Available Upon infection of a new host, human immunodeficiency virus (HIV replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV. First, we found that the mode of virus production by infected cells (budding vs. bursting has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral

  13. Prediction of porosity of food materials during drying: Current challenges and directions.

    Science.gov (United States)

    Joardder, Mohammad U H; Kumar, C; Karim, M A

    2017-07-18

    Pore formation in food samples is a common physical phenomenon observed during dehydration processes. The pore evolution during drying significantly affects the physical properties and quality of dried foods. Therefore, it should be taken into consideration when predicting transport processes in the drying sample. Characteristics of pore formation depend on the drying process parameters, product properties and processing time. Understanding the physics of pore formation and evolution during drying will assist in accurately predicting the drying kinetics and quality of food materials. Researchers have been trying to develop mathematical models to describe the pore formation and evolution during drying. In this study, existing porosity models are critically analysed and limitations are identified. Better insight into the factors affecting porosity is provided, and suggestions are proposed to overcome the limitations. These include considerations of process parameters such as glass transition temperature, sample temperature, and variable material properties in the porosity models. Several researchers have proposed models for porosity prediction of food materials during drying. However, these models are either very simplistic or empirical in nature and failed to consider relevant significant factors that influence porosity. In-depth understanding of characteristics of the pore is required for developing a generic model of porosity. A micro-level analysis of pore formation is presented for better understanding, which will help in developing an accurate and generic porosity model.

  14. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  15. Cost prediction following traumatic brain injury: model development and validation.

    Science.gov (United States)

    Spitz, Gershon; McKenzie, Dean; Attwood, David; Ponsford, Jennie L

    2016-02-01

    The ability to predict costs following a traumatic brain injury (TBI) would assist in planning treatment and support services by healthcare providers, insurers and other agencies. The objective of the current study was to develop predictive models of hospital, medical, paramedical, and long-term care (LTC) costs for the first 10 years following a TBI. The sample comprised 798 participants with TBI, the majority of whom were male and aged between 15 and 34 at time of injury. Costing information was obtained for hospital, medical, paramedical, and LTC costs up to 10 years postinjury. Demographic and injury-severity variables were collected at the time of admission to the rehabilitation hospital. Duration of PTA was the most important single predictor for each cost type. The final models predicted 44% of hospital costs, 26% of medical costs, 23% of paramedical costs, and 34% of LTC costs. Greater costs were incurred, depending on cost type, for individuals with longer PTA duration, obtaining a limb or chest injury, a lower GCS score, older age at injury, not being married or defacto prior to injury, living in metropolitan areas, and those reporting premorbid excessive or problem alcohol use. This study has provided a comprehensive analysis of factors predicting various types of costs following TBI, with the combination of injury-related and demographic variables predicting 23-44% of costs. PTA duration was the strongest predictor across all cost categories. These factors may be used for the planning and case management of individuals following TBI. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  16. Model-predictive control based on Takagi-Sugeno fuzzy model for electrical vehicles delayed model

    DEFF Research Database (Denmark)

    Khooban, Mohammad-Hassan; Vafamand, Navid; Niknam, Taher

    2017-01-01

    Electric vehicles (EVs) play a significant role in different applications, such as commuter vehicles and short distance transport applications. This study presents a new structure of model-predictive control based on the Takagi-Sugeno fuzzy model, linear matrix inequalities, and a non......-quadratic Lyapunov function for the speed control of EVs including time-delay states and parameter uncertainty. Experimental data, using the Federal Test Procedure (FTP-75), is applied to test the performance and robustness of the suggested controller in the presence of time-varying parameters. Besides, a comparison...... is made between the results of the suggested robust strategy and those obtained from some of the most recent studies on the same topic, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are performed on a direct current motor. Simulation...

  17. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  18. Modelling of helical current filaments induced by LHW on EAST

    Energy Technology Data Exchange (ETDEWEB)

    Rack, Michael; Denner, Peter; Liang, Yunfeng [Institute of Energy and Climate Research - Plasma Physics, Forschungszentrum Juelich GmbH, Association EURATOM-FZJ, Partner in the Trilateral Euregio Cluster, D-52425 Juelich (Germany); Zeng, Long [Institute of Energy and Climate Research - Plasma Physics, Forschungszentrum Juelich GmbH, Association EURATOM-FZJ, Partner in the Trilateral Euregio Cluster, D-52425 Juelich (Germany); Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Gong, Xianzu; Gan, Kaifu; Wang, Liang; Liu, Fukun; Qian, Jinping; Shen, Biao; Li, Jiangang [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Gauthier, Eric [Association EURATOM-CEA, IRFM, F-13108 Saint-Paul-lez-Durance (France); Collaboration: the EAST Team

    2013-07-01

    Helical radiation belts have been observed in the scrape-off layer (SOL) of the plasma during the application of lower hybrid wave (LHW) heating at the superconducting tokamak EAST. Modelled SOL field lines, starting in-front of the LHW antennas, show agreement in position and pitch angle to the experimental observed radiation belts. A splitting of the strike-line can be observed on the outer divertor plates during the application of LHW heating. Agreement in the comparison of the Mirnov coil signals and a modelled electric current flow along these SOL field lines was found. A lower hybrid current drive can induce such an electric current flow near the plasma edge. This electric current flow causes a change of the plasma topology which could result in the splitting of the strike-line as known from the application of resonant magnetic perturbation fields. Comparisons of modelled footprint structures and experimental observed heat load patterns in the divertor region are discussed.

  19. Nonconvex model predictive control for commercial refrigeration

    Science.gov (United States)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  20. Predictive Modelling of Heavy Metals in Urban Lakes

    OpenAIRE

    Lindström, Martin

    2000-01-01

    Heavy metals are well-known environmental pollutants. In this thesis predictive models for heavy metals in urban lakes are discussed and new models presented. The base of predictive modelling is empirical data from field investigations of many ecosystems covering a wide range of ecosystem characteristics. Predictive models focus on the variabilities among lakes and processes controlling the major metal fluxes. Sediment and water data for this study were collected from ten small lakes in the ...

  1. Analytical Model of Subthreshold Drain Current Characteristics of Ballistic Silicon Nanowire Transistors

    Directory of Open Access Journals (Sweden)

    Wanjie Xu

    2015-01-01

    Full Text Available A physically based subthreshold current model for silicon nanowire transistors working in the ballistic regime is developed. Based on the electric potential distribution obtained from a 2D Poisson equation and by performing some perturbation approximations for subband energy levels, an analytical model for the subthreshold drain current is obtained. The model is further used for predicting the subthreshold slopes and threshold voltages of the transistors. Our results agree well with TCAD simulation with different geometries and under different biasing conditions.

  2. Higher-spin currents in the Gross-Neveu model at 1/n{sup 2}

    Energy Technology Data Exchange (ETDEWEB)

    Manashov, A.N. [Institut für Theoretische Physik, Universität Hamburg,Hamburg, D-22761 (Germany); Institut für Theoretische Physik, Universität Regensburg,Regensburg, D-93040 (Germany); Skvortsov, E.D. [Arnold Sommerfeld Center for Theoretical Physics, Ludwig-Maximilians University Munich, Theresienstr. 37, Munich, D-80333 (Germany); Lebedev Institute of Physics,Leninsky ave. 53, Moscow, 119991 (Russian Federation)

    2017-01-30

    We calculate the anomalous dimensions of higher-spin currents, both singlet and non-singlet, in the Gross-Neveu model at the 1/n{sup 2} order. It was conjectured that in the critical regime this model is dual to a higher-spin gauge theory on AdS{sub 4}. The AdS/CFT correspondence predicts that the masses of higher-spin fields correspond to the scaling dimensions of the singlet currents in the Gross-Neveu model.

  3. Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

    Science.gov (United States)

    Kennedy, Curtis E; Turley, James P

    2011-10-24

    Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9

  4. Analytical model for local scour prediction around hydrokinetic turbine foundations

    Science.gov (United States)

    Musa, M.; Heisel, M.; Hill, C.; Guala, M.

    2017-12-01

    Marine and Hydrokinetic renewable energy is an emerging sustainable and secure technology which produces clean energy harnessing water currents from mostly tidal and fluvial waterways. Hydrokinetic turbines are typically anchored at the bottom of the channel, which can be erodible or non-erodible. Recent experiments demonstrated the interactions between operating turbines and an erodible surface with sediment transport, resulting in a remarkable localized erosion-deposition pattern significantly larger than those observed by static in-river construction such as bridge piers, etc. Predicting local scour geometry at the base of hydrokinetic devices is extremely important during foundation design, installation, operation, and maintenance (IO&M), and long-term structural integrity. An analytical modeling framework is proposed applying the phenomenological theory of turbulence to the flow structures that promote the scouring process at the base of a turbine. The evolution of scour is directly linked to device operating conditions through the turbine drag force, which is inferred to locally dictate the energy dissipation rate in the scour region. The predictive model is validated using experimental data obtained at the University of Minnesota's St. Anthony Falls Laboratory (SAFL), covering two sediment mobility regimes (clear water and live bed), different turbine designs, hydraulic parameters, grain size distribution and bedform types. The model is applied to a potential prototype scale deployment in the lower Mississippi River, demonstrating its practical relevance and endorsing the feasibility of hydrokinetic energy power plants in large sandy rivers. Multi-turbine deployments are further studied experimentally by monitoring both local and non-local geomorphic effects introduced by a twelve turbine staggered array model installed in a wide channel at SAFL. Local scour behind each turbine is well captured by the theoretical predictive model. However, multi

  5. Multivariate Models for Prediction of Human Skin Sensitization Hazard

    Science.gov (United States)

    Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M.; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

    2016-01-01

    One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays—the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens™ assay—six physicochemical properties, and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression (LR) and support vector machine (SVM), to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three LR and three SVM) with the highest accuracy (92%) used: (1) DPRA, h-CLAT, and read-across; (2) DPRA, h-CLAT, read-across, and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens, and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy = 88%), any of the alternative methods alone (accuracy = 63–79%), or test batteries combining data from the individual methods (accuracy = 75%). These results suggest that computational methods are promising tools to effectively identify potential human skin sensitizers without animal testing. PMID:27480324

  6. Storm-time ring current: model-dependent results

    Directory of Open Access Journals (Sweden)

    N. Yu. Ganushkina

    2012-01-01

    Full Text Available The main point of the paper is to investigate how much the modeled ring current depends on the representations of magnetic and electric fields and boundary conditions used in simulations. Two storm events, one moderate (SymH minimum of −120 nT on 6–7 November 1997 and one intense (SymH minimum of −230 nT on 21–22 October 1999, are modeled. A rather simple ring current model is employed, namely, the Inner Magnetosphere Particle Transport and Acceleration model (IMPTAM, in order to make the results most evident. Four different magnetic field and two electric field representations and four boundary conditions are used. We find that different combinations of the magnetic and electric field configurations and boundary conditions result in very different modeled ring current, and, therefore, the physical conclusions based on simulation results can differ significantly. A time-dependent boundary outside of 6.6 RE gives a possibility to take into account the particles in the transition region (between dipole and stretched field lines forming partial ring current and near-Earth tail current in that region. Calculating the model SymH* by Biot-Savart's law instead of the widely used Dessler-Parker-Sckopke (DPS relation gives larger and more realistic values, since the currents are calculated in the regions with nondipolar magnetic field. Therefore, the boundary location and the method of SymH* calculation are of key importance for ring current data-model comparisons to be correctly interpreted.

  7. An analytical gate tunneling current model for MOSFETs

    Energy Technology Data Exchange (ETDEWEB)

    Kazerouni, Iman Abaspur, E-mail: imanabaspur@gmail.com; Hosseini, Seyed Ebrahim [Sabzevar Tarbiat Moallem University, Electrical and Computer Department (Iran, Islamic Republic of)

    2012-03-15

    Gate tunneling current of MOSFETs is an important factor in modeling ultra small devices. In this paper, gate tunneling in present-generation MOSFETs is studied. In the proposed model, we calculate the electron wave function at the semiconductor-oxide interface and inversion charge by treating the inversion layer as a potential well, including some simplifying assumptions. Then we compute the gate tunneling current using the calculated wave function. The proposed model results have an excellent agreement with experimental results in the literature.

  8. A Neuron Model Based Ultralow Current Sensor System for Bioapplications

    Directory of Open Access Journals (Sweden)

    A. K. M. Arifuzzman

    2016-01-01

    Full Text Available An ultralow current sensor system based on the Izhikevich neuron model is presented in this paper. The Izhikevich neuron model has been used for its superior computational efficiency and greater biological plausibility over other well-known neuron spiking models. Of the many biological neuron spiking features, regular spiking, chattering, and neostriatal spiny projection spiking have been reproduced by adjusting the parameters associated with the model at hand. This paper also presents a modified interpretation of the regular spiking feature in which the firing pattern is similar to that of the regular spiking but with improved dynamic range offering. The sensor current ranges between 2 pA and 8 nA and exhibits linearity in the range of 0.9665 to 0.9989 for different spiking features. The efficacy of the sensor system in detecting low amount of current along with its high linearity attribute makes it very suitable for biomedical applications.

  9. Numerical Modeling of Edge-Localized-Mode Filaments on Divertor Plates Based on Thermoelectric Currents

    International Nuclear Information System (INIS)

    Wingen, A.; Spatschek, K. H.; Evans, T. E.; Lasnier, C. J.

    2010-01-01

    Edge localized modes (ELMs) are qualitatively and quantitatively modeled in tokamaks using current bursts which have been observed in the scrape-off-layer (SOL) during an ELM crash. During the initial phase of an ELM, a heat pulse causes thermoelectric currents. They first flow in short connection length flux tubes which are initially established by error fields or other nonaxisymmetric magnetic perturbations. The currents change the magnetic field topology in such a way that larger areas of short connection length flux tubes emerge. Then currents predominantly flow in short SOL-like flux tubes and scale with the area of the flux tube assuming a constant current density. Quantitative predictions of flux tube patterns for a given current are in excellent agreement with measurements of the heat load and current flow at the DIII-D target plates during an ELM cycle.

  10. Seasonal predictability of Kiremt rainfall in coupled general circulation models

    Science.gov (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

    2017-11-01

    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  11. eTOXlab, an open source modeling framework for implementing predictive models in production environments.

    Science.gov (United States)

    Carrió, Pau; López, Oriol; Sanz, Ferran; Pastor, Manuel

    2015-01-01

    Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by e

  12. MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  13. Predictive modelling of complex agronomic and biological systems.

    Science.gov (United States)

    Keurentjes, Joost J B; Molenaar, Jaap; Zwaan, Bas J

    2013-09-01

    Biological systems are tremendously complex in their functioning and regulation. Studying the multifaceted behaviour and describing the performance of such complexity has challenged the scientific community for years. The reduction of real-world intricacy into simple descriptive models has therefore convinced many researchers of the usefulness of introducing mathematics into biological sciences. Predictive modelling takes such an approach another step further in that it takes advantage of existing knowledge to project the performance of a system in alternating scenarios. The ever growing amounts of available data generated by assessing biological systems at increasingly higher detail provide unique opportunities for future modelling and experiment design. Here we aim to provide an overview of the progress made in modelling over time and the currently prevalent approaches for iterative modelling cycles in modern biology. We will further argue for the importance of versatility in modelling approaches, including parameter estimation, model reduction and network reconstruction. Finally, we will discuss the difficulties in overcoming the mathematical interpretation of in vivo complexity and address some of the future challenges lying ahead. © 2013 John Wiley & Sons Ltd.

  14. Predictive Pulse Pattern Current Modulation Scheme for Harmonic Reduction in Three-Phase Multidrive Systems

    DEFF Research Database (Denmark)

    Davari, Pooya; Yang, Yongheng; Zare, Firuz

    2016-01-01

    at the rectification stage to synthesize sinusoidal input currents. The input voltage sensing is avoided in order to minimize the number of required sensors, and the grid synchronization also has been implemented based on a common Phase-Locked-Loop (PLL) using the DC-link capacitor voltage ripple. Experimental results......The majority of the industrial motor drive systems are equipped with the conventional line-commutated front-end rectifiers, and being one of the main sources of harmonics in the power line. While a parallel combination of these drive units elevates current quality issues, a proper arrangement...... of them can lead to the cancellation of specific harmonics. This paper proposes a new cost-effective harmonic mitigation solution for multi-drive systems using a predictive pulse pattern current modulation control strategy. The proposed technique applies suitable interaction among parallel drive units...

  15. Climate modelling, uncertainty and responses to predictions of change

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.

    1996-01-01

    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

  16. MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

    Science.gov (United States)

    Son, S. W.; Lim, Y.; Kim, D.

    2017-12-01

    The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

  17. Currents, charges, and canonical structure of pseudodual chiral models

    International Nuclear Information System (INIS)

    Curtright, T.; Zachos, C.

    1994-01-01

    We discuss the pseudodual chiral model to illustrate a class of two-dimensional theories which have an infinite number of conservation laws but allow particle production, at variance with naive expectations. We describe the symmetries of the pseudodual model, both local and nonlocal, as transmutations of the symmetries of the usual chiral model. We refine the conventional algorithm to more efficiently produce the nonlocal symmetries of the model, and we discuss the complete local current algebra for the pseudodual theory. We also exhibit the canonical transformation which connects the usual chiral model to its fully equivalent dual, further distinguishing the pseudodual theory

  18. Current psychiatric disorders in patients with epilepsy are predicted by maltreatment experiences during childhood.

    Science.gov (United States)

    Labudda, Kirsten; Illies, Dominik; Herzig, Cornelia; Schröder, Katharina; Bien, Christian G; Neuner, Frank

    2017-09-01

    Childhood maltreatment has been shown to be a risk factor for the development of psychiatric disorders. Although the prevalence of psychiatric disorders is high in epilepsy patients, it is unknown if childhood maltreatment experiences are elevated compared to the normal population and if early maltreatment is a risk factor for current psychiatric comorbidities in epilepsy patients. This is the main purpose of this study. Structured interviews were used to assess current Axis I diagnoses in 120 epilepsy patients from a tertiary Epilepsy Center (34 TLE patients, 86 non-TLE patients). Childhood maltreatment in the family and peer victimization were assessed with validated questionnaires. Patients' maltreatment scores were compared with those of a representative matched control group. Logistic regression analysis was conducted to assess the potential impact of childhood maltreatment on current psychiatric comorbidity in epilepsy patients. Compared to a matched control group, epilepsy patients had higher emotional and sexual maltreatment scores. Patients with a current psychiatric diagnosis reported more family and peer maltreatment than patients without a psychiatric disorder. Family maltreatment scores predicted the likelihood of a current psychiatric disorder. TLE patients did not differ from non-TLE patients according to maltreatment experiences and rates of current psychiatric disorders. Our findings suggest that in epilepsy patients emotional and sexual childhood maltreatment is experienced more often than in the normal population and that early maltreatment is a general risk factor for psychiatric comorbidities in this group. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Design and implementation of predictive current control of three-phase PWM rectifier using space-vector modulation (SVM)

    International Nuclear Information System (INIS)

    Bouafia, Abdelouahab; Gaubert, Jean-Paul; Krim, Fateh

    2010-01-01

    This paper is concerned with the design and implementation of current control of three-phase PWM rectifier based on predictive control strategy. The proposed predictive current control technique operates with constant switching frequency, using space-vector modulation (SVM). The main goal of the designed current control scheme is to maintain the dc-bus voltage at the required level and to achieve the unity power factor (UPF) operation of the converter. For this purpose, two predictive current control algorithms, in the sense of deadbeat control, are developed for direct controlling input current vector of the converter in the stationary α-β and rotating d-q reference frame, respectively. For both predictive current control algorithms, at the beginning of each switching period, the required rectifier average voltage vector allowing the cancellation of both tracking errors of current vector components at the end of the switching period, is computed and applied during a predefined switching period by means of SVM. The main advantages of the proposed predictive current control are that no need to use hysteresis comparators or PI controllers in current control loops, and constant switching frequency. Finally, the developed predictive current control algorithms were tested both in simulations and experimentally, and illustrative results are presented here. Results have proven excellent performance in steady and transient states, and verify the validity of the proposed predictive current control which is compared to other control strategies.

  20. Analytical model development of an eddy-current-based non-contacting steel plate conveyance system

    International Nuclear Information System (INIS)

    Liu, C.-T.; Lin, S.-Y.; Yang, Y.-Y.; Hwang, C.-C.

    2008-01-01

    A concise model for analyzing and predicting the quasi-static electromagnetic characteristics of an eddy-current-based non-contacting steel plate conveyance system has been developed. Confirmed by three-dimensional (3-D) finite element analysis (FEA), adequacy of the analytical model can be demonstrated. Such an effective approach, which can be conveniently used by the potential industries for preliminary system operational performance evaluations, will be essential for designers and on-site engineers

  1. Comparison between the electron cyclotron current drive experiments on DIII-D and predictions for T-10

    International Nuclear Information System (INIS)

    Lohr, J.; Harvey, R.W.; Luce, T.C.; Matsuda, Kyoko; Moeller, C.P.; Petty, C.C.; Prater, R.; James, R.A.; Giruzzi, G.; Gorelov, Y.; DeHaas, J.

    1990-11-01

    Electron cyclotron current drive has been demonstrated on the DIII-D tokamak in an experiment in which ∼1 MW of microwave power generated ∼50 kA of non-inductive current. The rf-generated portion was about 15% of the total current. On the T-10 tokamak, more than 3 MW of microwave power will be available for current generation, providing the possibility that all the plasma current could be maintained by this method. Fokker-Planck calculations using the code CQL3D and ray tracing calculations using TORAY have been performed to model both experiments. For DIII-D the agreement between the calculations and measurements is good, producing confidence in the validity of the computational models. The same calculations using the T-10 geometry predict that for n e (0) ∼ 1.8 x 10 13 cm -3 , and T e (0) ∼ 7 keV, 1.2 MW, that is, the power available from only three gyrotrons, could generate as much as 150 kA of non-inductive current. Parameter space scans in which temperature, density and resonance location were varied have been performed to indicate the current drive expected under different experimental conditions. The residual dc electric field was considered in the DIII-D analysis because of its nonlinear effect on the electron distribution, which complicates the interpretation of the results. A 110 GHz ECH system is being installed on DIII-D. Initial operations, planned for late 1991, will use four gyrotrons with 500 kW each and 10 second output pulses. Injection will be from the low field side from launchers which can be steered to heat at the desired location. These launchers, two of which are presently installed, are set at 20 degrees to the radial and rf current drive studies are planned for the initial operation. 8 refs., 10 figs

  2. To predict the niche, model colonization and extinction

    Science.gov (United States)

    Charles B. Yackulic; James D. Nichols; Janice Reid; Ricky Der

    2015-01-01

    Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species’ niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both...

  3. Meson exchange current (MEC) models in neutrino interaction generators

    International Nuclear Information System (INIS)

    Katori, Teppei

    2015-01-01

    Understanding of the so-called 2 particle-2 hole (2p-2h) effect is an urgent program in neutrino interaction physics for current and future oscillation experiments. Such processes are believed to be responsible for the event excesses observed by recent neutrino experiments. The 2p-2h effect is dominated by the meson exchange current (MEC), and is accompanied by a 2-nucleon emission from the primary vertex, instead of a single nucleon emission from the charged-current quasi-elastic (CCQE) interaction. Current and future high resolution experiments can potentially nail down this effect. For this reason, there are world wide efforts to model and implement this process in neutrino interaction simulations. In these proceedings, I would like to describe how this channel is modeled in neutrino interaction generators

  4. Butterfly, Recurrence, and Predictability in Lorenz Models

    Science.gov (United States)

    Shen, B. W.

    2017-12-01

    Over the span of 50 years, the original three-dimensional Lorenz model (3DLM; Lorenz,1963) and its high-dimensional versions (e.g., Shen 2014a and references therein) have been used for improving our understanding of the predictability of weather and climate with a focus on chaotic responses. Although the Lorenz studies focus on nonlinear processes and chaotic dynamics, people often apply a "linear" conceptual model to understand the nonlinear processes in the 3DLM. In this talk, we present examples to illustrate the common misunderstandings regarding butterfly effect and discuss the importance of solutions' recurrence and boundedness in the 3DLM and high-dimensional LMs. The first example is discussed with the following folklore that has been widely used as an analogy of the butterfly effect: "For want of a nail, the shoe was lost.For want of a shoe, the horse was lost.For want of a horse, the rider was lost.For want of a rider, the battle was lost.For want of a battle, the kingdom was lost.And all for the want of a horseshoe nail."However, in 2008, Prof. Lorenz stated that he did not feel that this verse described true chaos but that it better illustrated the simpler phenomenon of instability; and that the verse implicitly suggests that subsequent small events will not reverse the outcome (Lorenz, 2008). Lorenz's comments suggest that the verse neither describes negative (nonlinear) feedback nor indicates recurrence, the latter of which is required for the appearance of a butterfly pattern. The second example is to illustrate that the divergence of two nearby trajectories should be bounded and recurrent, as shown in Figure 1. Furthermore, we will discuss how high-dimensional LMs were derived to illustrate (1) negative nonlinear feedback that stabilizes the system within the five- and seven-dimensional LMs (5D and 7D LMs; Shen 2014a; 2015a; 2016); (2) positive nonlinear feedback that destabilizes the system within the 6D and 8D LMs (Shen 2015b; 2017); and (3

  5. Auditing predictive models : a case study in crop growth

    NARCIS (Netherlands)

    Metselaar, K.

    1999-01-01

    Methods were developed to assess and quantify the predictive quality of simulation models, with the intent to contribute to evaluation of model studies by non-scientists. In a case study, two models of different complexity, LINTUL and SUCROS87, were used to predict yield of forage maize

  6. Models for predicting compressive strength and water absorption of ...

    African Journals Online (AJOL)

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

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

    Energy Technology Data Exchange (ETDEWEB)

    Venkattraman, Ayyaswamy [Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai 600036 (India)

    2013-11-15

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

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

    International Nuclear Information System (INIS)

    Venkattraman, Ayyaswamy

    2013-01-01

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

  9. Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control

    DEFF Research Database (Denmark)

    Errouissi, Rachid; Al-Durra, Ahmed; Muyeen, S.M.

    2017-01-01

    This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current...... is directly used to compute the required rotor voltage in order to minimize the difference between the actual stator currents and their references over the predictive time. However, as the proposed strategy is sensitive to parameter variations and external disturbances, a disturbance observer is embedded...... into the control loop to remove the steady-state error of the stator current. It turns out that the steady-state and the transient performances can be identified by simple design parameters. In this paper, the reference of the stator current is directly calculated from the desired stator active and reactive powers...

  10. Statistical and Machine Learning Models to Predict Programming Performance

    OpenAIRE

    Bergin, Susan

    2006-01-01

    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  11. Dynamic modelling of tearing mode stabilization by RF current drive

    International Nuclear Information System (INIS)

    Giruzzi, G.; Zabiego, M.; Gianakon, T.A.; Garbet, X.; Bernabei, S.

    1998-01-01

    The theory of tearing mode stabilization in toroidal plasmas by RF-driven currents that are modulated in phase with the island rotation is investigated. A time scale analysis of the phenomena involved indicates that transient effects, such as finite time response of the driven currents, island rotation during the power pulses, and the inductive response of the plasma, are intrinsically important. A dynamic model of such effects is developed, based on a 3-D Fokker-Planck code coupled to both the electric field diffusion and the island evolution equations. Extensive applications to both Electron Cyclotron and Lower Hybrid current drive in ITER are presented. (author)

  12. NON-EQUILIBRIUM IONIZATION MODELING OF THE CURRENT SHEET IN A SIMULATED SOLAR ERUPTION

    International Nuclear Information System (INIS)

    Shen Chengcai; Reeves, Katharine K.; Raymond, John C.; Murphy, Nicholas A.; Ko, Yuan-Kuen; Lin Jun; Mikić, Zoran; Linker, Jon A.

    2013-01-01

    The current sheet that extends from the top of flare loops and connects to an associated flux rope is a common structure in models of coronal mass ejections (CMEs). To understand the observational properties of CME current sheets, we generated predictions from a flare/CME model to be compared with observations. We use a simulation of a large-scale CME current sheet previously reported by Reeves et al. This simulation includes ohmic and coronal heating, thermal conduction, and radiative cooling in the energy equation. Using the results of this simulation, we perform time-dependent ionization calculations of the flow in a CME current sheet and construct two-dimensional spatial distributions of ionic charge states for multiple chemical elements. We use the filter responses from the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory and the predicted intensities of emission lines to compute the count rates for each of the AIA bands. The results show differences in the emission line intensities between equilibrium and non-equilibrium ionization. The current sheet plasma is underionized at low heights and overionized at large heights. At low heights in the current sheet, the intensities of the AIA 94 Å and 131 Å channels are lower for non-equilibrium ionization than for equilibrium ionization. At large heights, these intensities are higher for non-equilibrium ionization than for equilibrium ionization inside the current sheet. The assumption of ionization equilibrium would lead to a significant underestimate of the temperature low in the current sheet and overestimate at larger heights. We also calculate the intensities of ultraviolet lines and predict emission features to be compared with events from the Ultraviolet Coronagraph Spectrometer on the Solar and Heliospheric Observatory, including a low-intensity region around the current sheet corresponding to this model

  13. A predictive coding account of bistable perception - a model-based fMRI study.

    Directory of Open Access Journals (Sweden)

    Veith Weilnhammer

    2017-05-01

    Full Text Available In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together

  14. A predictive coding account of bistable perception - a model-based fMRI study.

    Science.gov (United States)

    Weilnhammer, Veith; Stuke, Heiner; Hesselmann, Guido; Sterzer, Philipp; Schmack, Katharina

    2017-05-01

    In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work

  15. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  16. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    Science.gov (United States)

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  17. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...... of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset...

  18. Testing the predictive power of nuclear mass models

    International Nuclear Information System (INIS)

    Mendoza-Temis, J.; Morales, I.; Barea, J.; Frank, A.; Hirsch, J.G.; Vieyra, J.C. Lopez; Van Isacker, P.; Velazquez, V.

    2008-01-01

    A number of tests are introduced which probe the ability of nuclear mass models to extrapolate. Three models are analyzed in detail: the liquid drop model, the liquid drop model plus empirical shell corrections and the Duflo-Zuker mass formula. If predicted nuclei are close to the fitted ones, average errors in predicted and fitted masses are similar. However, the challenge of predicting nuclear masses in a region stabilized by shell effects (e.g., the lead region) is far more difficult. The Duflo-Zuker mass formula emerges as a powerful predictive tool

  19. Quasi-equilibrium channel model of an constant current arc

    Directory of Open Access Journals (Sweden)

    Gerasimov Alexander V.

    2003-01-01

    Full Text Available The rather simple method of calculation of electronic and gas temperature in the channel of arc of plasma generator is offered. This method is based on self-consistent two-temperature channel model of an electric arc. The method proposed enables to obtain radial allocation of gas and electronic temperatures in a non-conducting zone of an constant current arc, for prescribed parameters of discharge (current intensity and power of the discharge, with enough good precision. The results obtained can be used in model and engineering calculations to estimate gas and electronic temperatures in the channel of an arc plasma generator.

  20. Development of three dimensional ocean current model for coastal region

    International Nuclear Information System (INIS)

    Kobayashi, Takuya

    1999-12-01

    In order to study the migration behavior of radionuclides released into a coastal region around Japan, Princeton Ocean Model (POM) was introduced. This three-dimensional ocean current model was modified to be applied for oceanic simulations around Japan. This report describes the governing equations, numerical methods and model improvements. In addition, database system which is utilized for calculations and visualization system for graphical outputs are also described. Model simulation was carried out at off the area of Shimokita. Aomori-ken, Japan to investigate the effects of the boundary conditions on simulated results. (author)

  1. From Predictive Models to Instructional Policies

    Science.gov (United States)

    Rollinson, Joseph; Brunskill, Emma

    2015-01-01

    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  2. Predictive modeling of reactive wetting and metal joining.

    Energy Technology Data Exchange (ETDEWEB)

    van Swol, Frank B.

    2013-09-01

    The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.

  3. Validation of neoclassical bootstrap current models in the edge of an H-mode plasma.

    Science.gov (United States)

    Wade, M R; Murakami, M; Politzer, P A

    2004-06-11

    Analysis of the parallel electric field E(parallel) evolution following an L-H transition in the DIII-D tokamak indicates the generation of a large negative pulse near the edge which propagates inward, indicative of the generation of a noninductive edge current. Modeling indicates that the observed E(parallel) evolution is consistent with a narrow current density peak generated in the plasma edge. Very good quantitative agreement is found between the measured E(parallel) evolution and that expected from neoclassical theory predictions of the bootstrap current.

  4. The Complexity of Developmental Predictions from Dual Process Models

    Science.gov (United States)

    Stanovich, Keith E.; West, Richard F.; Toplak, Maggie E.

    2011-01-01

    Drawing developmental predictions from dual-process theories is more complex than is commonly realized. Overly simplified predictions drawn from such models may lead to premature rejection of the dual process approach as one of many tools for understanding cognitive development. Misleading predictions can be avoided by paying attention to several…

  5. Numeral eddy current sensor modelling based on genetic neural network

    International Nuclear Information System (INIS)

    Yu Along

    2008-01-01

    This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

  6. Higher spin currents in the critical O(N) vector model at 1/N2

    International Nuclear Information System (INIS)

    Manashov, A.N.; Strohmaier, M.

    2017-06-01

    We calculate the anomalous dimensions of higher spin singlet currents in the critical O(N) vector model at order 1/N 2 . The results are shown to be in agreement with the four-loop perturbative computation in φ 4 theory in 4-2ε dimensions. It is known that the order 1/N anomalous dimensions of higher-spin currents happen to be the same in the Gross-Neveu and the critical vector model. On the contrary, the order 1/N 2 corrections are different. The results can also be interpreted as a prediction for the two-loop computation in the dual higher-spin gravity.

  7. Sweat loss prediction using a multi-model approach.

    Science.gov (United States)

    Xu, Xiaojiang; Santee, William R

    2011-07-01

    A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.

  8. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  9. Ring current models for acetylene and ethylene molecules

    International Nuclear Information System (INIS)

    Pelloni, Stefano; Lazzeretti, Paolo

    2009-01-01

    Spatial models of the current density vector field, induced in the electronic cloud of the acetylene and ethylene molecules by a uniform, time-independent magnetic field, are discussed in terms of topological stagnation graphs and three-dimensional streamline plots. The models are validated by documenting their ability to explain magnetic susceptibility and nuclear magnetic shieldings of carbon and hydrogen via related shielding density maps

  10. Eddy current modeling in linear and nonlinear multifilamentary composite materials

    Science.gov (United States)

    Menana, Hocine; Farhat, Mohamad; Hinaje, Melika; Berger, Kevin; Douine, Bruno; Lévêque, Jean

    2018-04-01

    In this work, a numerical model is developed for a rapid computation of eddy currents in composite materials, adaptable for both carbon fiber reinforced polymers (CFRPs) for NDT applications and multifilamentary high temperature superconductive (HTS) tapes for AC loss evaluation. The proposed model is based on an integro-differential formulation in terms of the electric vector potential in the frequency domain. The high anisotropy and the nonlinearity of the considered materials are easily handled in the frequency domain.

  11. Current amplification models of sensorineurall and conductive hearing loss

    OpenAIRE

    Ostojić, Sanja; Mikić, Branka; Mirić, Danica

    2012-01-01

    The main function of a hearing aid is to improve auditory and language abilities of hearing impaired users. The amplification model has to be adapted according to age, degree and type of hearing loss. The goal of this paper is to analyze the current amplification models of sensorineural and conductive hearing loss which can provide a high quality of speech perception and sounds at any degree of hearing loss. The BAHA is a surgically implantable system for treatment of conductive hearing loss ...

  12. Predicting Trophic Interactions and Habitat Utilization in the California Current Ecosystem

    Science.gov (United States)

    2015-09-30

    Foraging patterns: model-data comparison . Simulated (colored circles) and observed (black circles) foraging locations for male sea lion individuals off...focusing on trophic interactions affecting habitat utilization and foraging patterns of California sea lions (CSL) in the California Current Large Marine...by considering patterns of covariability between environmental variables (e.g., temperature, primary production) and foraging patterns and success of

  13. Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

    Science.gov (United States)

    Abellán-Nebot, J. V.; Liu, J.; Romero, F.

    2009-11-01

    The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

  14. Nutrient supply, surface currents, and plankton dynamics predict zooplankton hotspots in coastal upwelling systems

    Science.gov (United States)

    Messié, Monique; Chavez, Francisco P.

    2017-09-01

    A simple combination of wind-driven nutrient upwelling, surface currents, and plankton growth/grazing equations generates zooplankton patchiness and hotspots in coastal upwelling regions. Starting with an initial input of nitrate from coastal upwelling, growth and grazing equations evolve phytoplankton and zooplankton over time and space following surface currents. The model simulates the transition from coastal (large phytoplankton, e.g., diatoms) to offshore (picophytoplankton and microzooplankton) communities, and in between generates a large zooplankton maximum. The method was applied to four major upwelling systems (California, Peru, Northwest Africa, and Benguela) using latitudinal estimates of wind-driven nitrate supply and satellite-based surface currents. The resulting zooplankton simulations are patchy in nature; areas of high concentrations coincide with previously documented copepod and krill hotspots. The exercise highlights the importance of the upwelling process and surface currents in shaping plankton communities.

  15. Predictive Trailing-Edge Modulation Average Current Control in DC-DC Converters

    Directory of Open Access Journals (Sweden)

    LASCU, D.

    2013-11-01

    Full Text Available The paper investigates predictive digital average current control (PDACC in dc/dc converters using trailing-edge modulation (TEM. The study is focused on the recurrence duty cycle equation and then stability analysis is performed. It is demonstrated that average current control using trailing-edge modulation is stable on the whole range of the duty cycle and thus design problems are highly reduced. The analysis is carried out in a general manner, independent of converter topology and therefore the results can then be easily applied for a certain converter (buck, boost, buck-boost, etc.. The theoretical considerations are confirmed for a boost converter first using the MATLAB program based on state-space equations and finally with the CASPOC circuit simulation package.

  16. Current State of the Art Historic Building Information Modelling

    Science.gov (United States)

    Dore, C.; Murphy, M.

    2017-08-01

    In an extensive review of existing literature a number of observations were made in relation to the current approaches for recording and modelling existing buildings and environments: Data collection and pre-processing techniques are becoming increasingly automated to allow for near real-time data capture and fast processing of this data for later modelling applications. Current BIM software is almost completely focused on new buildings and has very limited tools and pre-defined libraries for modelling existing and historic buildings. The development of reusable parametric library objects for existing and historic buildings supports modelling with high levels of detail while decreasing the modelling time. Mapping these parametric objects to survey data, however, is still a time-consuming task that requires further research. Promising developments have been made towards automatic object recognition and feature extraction from point clouds for as-built BIM. However, results are currently limited to simple and planar features. Further work is required for automatic accurate and reliable reconstruction of complex geometries from point cloud data. Procedural modelling can provide an automated solution for generating 3D geometries but lacks the detail and accuracy required for most as-built applications in AEC and heritage fields.

  17. SESAM – a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

    DEFF Research Database (Denmark)

    Guisan, Antoine; Rahbek, Carsten

    2011-01-01

    Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts...

  18. Maintenance personnel performance simulation (MAPPS): a model for predicting maintenance performance reliability in nuclear power plants

    International Nuclear Information System (INIS)

    Knee, H.E.; Krois, P.A.; Haas, P.M.; Siegel, A.I.; Ryan, T.G.

    1983-01-01

    The NRC has developed a structured, quantitative, predictive methodology in the form of a computerized simulation model for assessing maintainer task performance. Objective of the overall program is to develop, validate, and disseminate a practical, useful, and acceptable methodology for the quantitative assessment of NPP maintenance personnel reliability. The program was organized into four phases: (1) scoping study, (2) model development, (3) model evaluation, and (4) model dissemination. The program is currently nearing completion of Phase 2 - Model Development

  19. Decentralized energy management strategy based on predictive controllers for a medium voltage direct current photovoltaic electric vehicle charging station

    International Nuclear Information System (INIS)

    Torreglosa, Juan P.; García-Triviño, Pablo; Fernández-Ramirez, Luis M.; Jurado, Francisco

    2016-01-01

    Highlights: • Electric vehicle charging station supplied by photovoltaic, batteries and grid connection is analyzed. • The bus voltage is the key parameter for controlling the system by decentralized approach. • Decentralized control approach facilities the enlargement of the system. • Photovoltaic and battery systems are controlled by model predictive controllers. • Response by model predictive controllers improves that by PI controllers. - Abstract: The use of distributed charging stations based on renewable energy sources for electric vehicles has increased in recent years. Combining photovoltaic solar energy and batteries as energy storage system, directly tied into a medium voltage direct current bus, and with the grid support, results to be an interesting option for improving the operation and efficiency of electric vehicle charging stations. In this paper, an electric vehicle charging station supplied by photovoltaic solar panels, batteries and with grid connection is analysed and evaluated. A decentralized energy management system is developed for regulating the energy flow among the photovoltaic system, the battery and the grid in order to achieve the efficient charging of electric vehicles. The medium voltage direct current bus voltage is the key parameter for controlling the system. The battery is controlled by a model predictive controller in order to keep the bus voltage at its reference value. Depending on the state-of-charge of the battery and the bus voltage, the photovoltaic system can work at maximum power point tracking mode or at bus voltage sustaining mode, or even the grid support can be needed. The results demonstrate the proper operation and energy management of the electric vehicle charging station under study.

  20. How well do basic models describe the turbidity currents coming down Monterey and Congo Canyon?

    Science.gov (United States)

    Cartigny, M.; Simmons, S.; Heerema, C.; Xu, J. P.; Azpiroz, M.; Clare, M. A.; Cooper, C.; Gales, J. A.; Maier, K. L.; Parsons, D. R.; Paull, C. K.; Sumner, E. J.; Talling, P.

    2017-12-01

    Turbidity currents rival rivers in their global capacity to transport sediment and organic carbon. Furthermore, turbidity currents break submarine cables that now transport >95% of our global data traffic. Accurate turbidity current models are thus needed to quantify their transport capacity and to predict the forces exerted on seafloor structures. Despite this need, existing numerical models are typically only calibrated with scaled-down laboratory measurements due to the paucity of direct measurements of field-scale turbidity currents. This lack of calibration thus leaves much uncertainty in the validity of existing models. Here we use the most detailed observations of turbidity currents yet acquired to validate one of the most fundamental models proposed for turbidity currents, the modified Chézy model. Direct measurements on which the validation is based come from two sites that feature distinctly different flow modes and grain sizes. The first are from the multi-institution Coordinated Canyon Experiment (CCE) in Monterey Canyon, California. An array of six moorings along the canyon axis captured at least 15 flow events that lasted up to hours. The second is the deep-sea Congo Canyon, where 10 finer grained flows were measured by a single mooring, each lasting several days. Moorings captured depth-resolved velocity and suspended sediment concentration at high resolution (turbidity currents; the modified Chézy model. This basic model has been very useful for river studies over the past 200 years, as it provides a rapid estimate of how flow velocity varies with changes in river level and energy slope. Chézy-type models assume that the gravitational force of the flow equals the friction of the river-bed. Modified Chézy models have been proposed for turbidity currents. However, the absence of detailed measurements of friction and sediment concentration within full-scale turbidity currents has forced modellers to make rough assumptions for these parameters. Here

  1. Modelling and simulation of eddy current non-destructive testing

    International Nuclear Information System (INIS)

    Mansir, H.; Burais, N.; Nicolas, A.

    1986-01-01

    This paper presents the practical configuration for detecting cracks in conducting materials by eddy current non destructive testing. An electromagnetic field formulation is proposed using Maxwell's relations. Geometrical and physical properties of the crack are taken into account by several models, particularly with a new finite element called ''crack element''. Modelisation is applied to sensor impedance calculation with classical numerical methods [fr

  2. Higher quantum conserved current in a new completely integrable model

    International Nuclear Information System (INIS)

    Nissimov, E.R.

    1980-06-01

    The first higher local quantum conserved current is the recently proposed new completely integrable (2esup(βphi)+esup(-2βphi)) 2 model is explicitly constructed thus proving absence of particle production and factorization of multiparticle scattering. (author)

  3. MHD Modeling of Conductors at Ultra-High Current Density

    International Nuclear Information System (INIS)

    ROSENTHAL, STEPHEN E.; DESJARLAIS, MICHAEL P.; SPIELMAN, RICK B.; STYGAR, WILLIAM A.; ASAY, JAMES R.; DOUGLAS, M.R.; HALL, C.A.; FRESE, M.H.; MORSE, R.L.; REISMAN, D.B.

    2000-01-01

    In conjunction with ongoing high-current experiments on Sandia National Laboratories' Z accelerator, the authors have revisited a problem first described in detail by Heinz Knoepfel. Unlike the 1-Tesla MITLs of pulsed power accelerators used to produce intense particle beams, Z's disc transmission line (downstream of the current addition) is in a 100--1,200 Tesla regime, so its conductors cannot be modeled simply as static infinite conductivity boundaries. Using the MHD code MACH2 they have been investigating the conductor hydrodynamics, characterizing the joule heating, magnetic field diffusion, and material deformation, pressure, and velocity over a range of current densities, current rise-times, and conductor materials. Three purposes of this work are (1) to quantify power flow losses owing to ultra-high magnetic fields, (2) to model the response of VISAR diagnostic samples in various configurations on Z, and (3) to incorporate the most appropriate equation of state and conductivity models into the MHD computations. Certain features are strongly dependent on the details of the conductivity model

  4. The nonparametric bootstrap for the current status model

    NARCIS (Netherlands)

    Groeneboom, P.; Hendrickx, K.

    2017-01-01

    It has been proved that direct bootstrapping of the nonparametric maximum likelihood estimator (MLE) of the distribution function in the current status model leads to inconsistent confidence intervals. We show that bootstrapping of functionals of the MLE can however be used to produce valid

  5. Modeling current climate conditions for forest pest risk assessment

    Science.gov (United States)

    Frank H. Koch; John W. Coulston

    2010-01-01

    Current information on broad-scale climatic conditions is essential for assessing potential distribution of forest pests. At present, sophisticated spatial interpolation approaches such as the Parameter-elevation Regressions on Independent Slopes Model (PRISM) are used to create high-resolution climatic data sets. Unfortunately, these data sets are based on 30-year...

  6. MHD Modeling of Conductors at Ultra-High Current Density

    International Nuclear Information System (INIS)

    Rosenthal, S.E.; Asay, J.R.; Desjarlais, M.P.; Douglas, M.R.; Frese, M.H.; Hall, C.A.; Morse, R.L.; Reisman, D.; Spielman, R.B.; Stygar, W.A.

    1999-01-01

    In conjunction with ongoing high-current experiments on Sandia National Laboratories' Z accelerator we have revisited a problem first described in detail by Heinz Knoepfel. MITLs of previous pulsed power accelerators have been in the 1-Tesla regime. Z's disc transmission line (downstream of the current addition) is in a 100-1200 Tesla regime, so its conductors cannot be modeled simply as static infinite conductivity boundaries. Using the MHD code MACH2 we have been investigating conductor hydrodynamics, characterizing the joule heating, magnetic field diffusion, and material deformation, pressure, and velocity over a range of current densities, current rise-times, and conductor materials. Three purposes of this work are ( 1) to quantify power flow losses owing to ultra-high magnetic fields, (2) to model the response of VISAR diagnostic samples in various configurations on Z, and (3) to incorporate the most appropriate equation of state and conductivity models into our MHD computations. Certain features are strongly dependent on the details of the conductivity model. Comparison with measurements on Z will be discussed

  7. [Neither Descartes nor Freud? current pain models in psychosomatic medicine].

    Science.gov (United States)

    Egloff, N; Egle, U T; von Känel, R

    2008-05-14

    Models explaining chronic pain based on the mere presence or absence of peripheral somatic findings or which view pain of psychological origin when there is no somatic explanation, have their shortcomings. Current scientific knowledge calls for distinct pain concepts, which integrate neurobiological and neuropsychological aspects of pain processing.

  8. Dilution and Ferrite Number Prediction in Pulsed Current Cladding of Super-Duplex Stainless Steel Using RSM

    Science.gov (United States)

    Eghlimi, Abbas; Shamanian, Morteza; Raeissi, Keyvan

    2013-12-01

    Super-duplex stainless steels have an excellent combination of mechanical properties and corrosion resistance at relatively low temperatures and can be used as a coating to improve the corrosion and wear resistance of low carbon and low alloy steels. Such coatings can be produced using weld cladding. In this study, pulsed current gas tungsten arc cladding process was utilized to deposit super-duplex stainless steel on high strength low alloy steel substrates. In such claddings, it is essential to understand how the dilution affects the composition and ferrite number of super-duplex stainless steel layer in order to be able to estimate its corrosion resistance and mechanical properties. In the current study, the effect of pulsed current gas tungsten arc cladding process parameters on the dilution and ferrite number of super-duplex stainless steel clad layer was investigated by applying response surface methodology. The validity of the proposed models was investigated by using quadratic regression models and analysis of variance. The results showed an inverse relationship between dilution and ferrite number. They also showed that increasing the heat input decreases the ferrite number. The proposed mathematical models are useful for predicting and controlling the ferrite number within an acceptable range for super-duplex stainless steel cladding.

  9. An employer brand predictive model for talent attraction and retention

    Directory of Open Access Journals (Sweden)

    Annelize Botha

    2011-11-01

    Full Text Available Orientation: In an ever shrinking global talent pool organisations use employer brand to attract and retain talent, however, in the absence of theoretical pointers, many organisations are losing out on a powerful business tool by not developing or maintaining their employer brand correctly. Research purpose: This study explores the current state of knowledge about employer brand and identifies the various employer brand building blocks which are conceptually integrated in a predictive model. Motivation for the study: The need for scientific progress though the accurate representation of a set of employer brand phenomena and propositions, which can be empirically tested, motivated this study. Research design, approach and method: This study was nonempirical in approach and searched for linkages between theoretical concepts by making use of relevant contextual data. Theoretical propositions which explain the identified linkages were developed for purpose of further empirical research. Main findings: Key findings suggested that employer brand is influenced by target group needs, a differentiated Employer Value Proposition (EVP, the people strategy, brand consistency, communication of the employer brand and measurement of Human Resources (HR employer branding efforts. Practical/managerial implications: The predictive model provides corporate leaders and their human resource functionaries a theoretical pointer relative to employer brand which could guide more effective talent attraction and retention decisions. Contribution/value add: This study adds to the small base of research available on employer brand and contributes to both scientific progress as well as an improved practical understanding of factors which influence employer brand.

  10. Modeling of Complex Life Cycle Prediction Based on Cell Division

    Directory of Open Access Journals (Sweden)

    Fucheng Zhang

    2017-01-01

    Full Text Available Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.

  11. Predictive modeling and reducing cyclic variability in autoignition engines

    Science.gov (United States)

    Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

    2016-08-30

    Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

  12. Parametric overdispersed frailty models for current status data.

    Science.gov (United States)

    Abrams, Steven; Aerts, Marc; Molenberghs, Geert; Hens, Niel

    2017-12-01

    Frailty models have a prominent place in survival analysis to model univariate and multivariate time-to-event data, often complicated by the presence of different types of censoring. In recent years, frailty modeling gained popularity in infectious disease epidemiology to quantify unobserved heterogeneity using Type I interval-censored serological data or current status data. In a multivariate setting, frailty models prove useful to assess the association between infection times related to multiple distinct infections acquired by the same individual. In addition to dependence among individual infection times, overdispersion can arise when the observed variability in the data exceeds the one implied by the model. In this article, we discuss parametric overdispersed frailty models for time-to-event data under Type I interval-censoring, building upon the work by Molenberghs et al. (2010) and Hens et al. (2009). The proposed methodology is illustrated using bivariate serological data on hepatitis A and B from Flanders, Belgium anno 1993-1994. Furthermore, the relationship between individual heterogeneity and overdispersion at a stratum-specific level is studied through simulations. Although it is important to account for overdispersion, one should be cautious when modeling both individual heterogeneity and overdispersion based on current status data as model selection is hampered by the loss of information due to censoring. © 2017, The International Biometric Society.

  13. Dynamic Simulation of Human Gait Model With Predictive Capability.

    Science.gov (United States)

    Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

    2018-03-01

    In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

  14. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...

  15. A model to predict the power output from wind farms

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Riso National Lab., Roskilde (Denmark)

    1997-12-31

    This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.

  16. Predictive model for functional consequences of oral cavity tumour resections

    NARCIS (Netherlands)

    van Alphen, M.J.A.; Hageman, T.A.G.; Hageman, Tijmen Antoon Geert; Smeele, L.E.; Balm, Alfonsus Jacobus Maria; Balm, A.J.M.; van der Heijden, Ferdinand; Lemke, H.U.

    2013-01-01

    The prediction of functional consequences after treatment of large oral cavity tumours is mainly based on the size and location of the tumour. However, patient specific factors play an important role in the functional outcome, making the current predictions unreliable and subjective. An objective

  17. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  18. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  19. Fokker-Planck modeling of current penetration during electron cyclotron current drive

    International Nuclear Information System (INIS)

    Merkulov, A.; Westerhof, E.; Schueller, F. C.

    2007-01-01

    The current penetration during electron cyclotron current drive (ECCD) on the resistive time scale is studied with a Fokker-Planck simulation, which includes a model for the magnetic diffusion that determines the parallel electric field evolution. The existence of the synergy between the inductive electric field and EC driven current complicates the process of the current penetration and invalidates the standard method of calculation in which Ohm's law is simply approximated by j-j cd =σE. Here it is proposed to obtain at every time step a self-consistent approximation to the plasma resistivity from the Fokker-Planck code, which is then used in a concurrent calculation of the magnetic diffusion equation in order to obtain the inductive electric field at the next time step. A series of Fokker-Planck calculations including a self-consistent evolution of the inductive electric field has been performed. Both the ECCD power and the electron density have been varied, thus varying the well known nonlinearity parameter for ECCD P rf [MW/m -3 ]/n e 2 [10 19 m -3 ] [R. W. Harvey et al., Phys. Rev. Lett 62, 426 (1989)]. This parameter turns out also to be a good predictor of the synergetic effects. The results are then compared with the standard method of calculations of the current penetration using a transport code. At low values of the Harvey parameter, the standard method is in quantitative agreement with Fokker-Planck calculations. However, at high values of the Harvey parameter, synergy between ECCD and E parallel is found. In the case of cocurrent drive, this synergy leads to the generation of large amounts of nonthermal electrons and a concomitant increase of the electrical conductivity and current penetration time. In the case of countercurrent drive, the ECCD efficiency is suppressed by the synergy with E parallel while only a small amount of nonthermal electrons is produced

  20. A mathematical model for predicting earthquake occurrence ...

    African Journals Online (AJOL)

    We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...

  1. Model for predicting the injury severity score.

    Science.gov (United States)

    Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

    2015-07-01

    To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

  2. Predicting Career Advancement with Structural Equation Modelling

    Science.gov (United States)

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  3. Fatigue Life Prediction in Rapid Die Casting - Preliminary Work in View of Current Research

    International Nuclear Information System (INIS)

    Chuan Huat Ng; Grote, Karl-Heinrich; Baehr, Ruediger

    2007-01-01

    Numerical simulation technique as a prediction tool is slowly adopted in metal casting industry for predicting design modelling solidification analysis. The reasons for this activity is found in the need to further enhance the geometrical design and mechanical properties of the tool design and the correct prediction methodology to fulfil industrial needs. The present state of numerical simulation capabilities in rapid die casting technologies is reviewed and the failure mode mechanisms of thermal fatigue, aimed at developing a numerical simulation with a systematic design guidance for predicting the thermal cyclic loading analysis and improvement is presented along with several other methods. The economic benefits of a numerical simulation technique in die casting are limited to tool life time, mechanical properties and design guidance. The extensive computer capabilities of a numerical simulation with a systematic design guidance methodology are exploited to provide a solution for flexible design, mechanical properties and mould life time. Related research carried out worldwide by different organisations and academic institutions are discussed

  4. Integrated Modeling for Road Condition Prediction (IMRCP)

    Science.gov (United States)

    2018-01-17

    Intelligent transportation system deployments have enabled great advances in operational awareness and response based on the data they gather on the current state of the roadways. The next step in decision support is to forecast road conditions and b...

  5. Prediction of leakage current of non-ceramic insulators in early aging period

    Energy Technology Data Exchange (ETDEWEB)

    El-Hag, Ayman H. [Electrical Engineering Department, American University of Sharjah, Sharjah (United Arab Emirates); Jahromi, Ali Naderian [Kinectrics Inc., Transmission and Distribution Technologies, Toronto (Canada); Sanaye-Pasand, Majid [Electrical and Computer Engineering Department, University of Tehran (Iran)

    2008-10-15

    The paper presents a neural network based prediction technique for the leakage current (LC) of non-ceramic insulators during salt-fog test. Nearly 50 distribution class silicone rubber (SIR) insulators with three different voltage classes have been tested in a salt-fog chamber, where the LC has been continuously recorded for at least 100 h. A boundary for early aging period is defined by the rate of change of the LC instead of a fixed threshold value. Consequently, the Gaussian radial basis network has been adopted to predict the level of LC at the early stage of aging of the SIR insulators and is compared with a classical network. The initial values of LC and its rate of change at 10 min intervals for the first 5 h are selected as the input to the network, and the final value of LC of the early aging period is considered as the output of the network. It is found that Gaussian radial basis function network with a random optimizing training method is an appropriate network to predict the LC with a 3.5-5.3% accuracy, if the training data and the testing data are selected from the same type of SIR insulators. (author)

  6. A two dimensional model of undertow current over mud bed

    International Nuclear Information System (INIS)

    Mir Hammadul Azam; Abdul Aziz Ibrahim; Noraieni Hj, Mokhtar

    1996-01-01

    Coastal wave-current dynamics often causes severe erosion and this activity is more prominent within the surf zone. Turbulence generated by breaking wave is a complex phenomena and the degree of complexity increases to a higher degree when it happens over mud bed. A better understanding on wave and current is necessary to enrich the engineering hand to facilitate any coastal development work. Since physical model has certain deficiencies, such as high cost and scaling problem, the need for developing numerical models in such cases is significant. A time averaged two dimensional model has been developed to simulate the undertow over mud bed. A turbulent energy model also included which considers only the vertical variation of mixing length. Production of turbulent kinetic energy in the surf zone has been calculated from an hydraulic jump analogy. The result obtained shows an insignificant vertical variation of current. Further research is needed involving laboratory and field works to get sufficient data for comparing the model results

  7. A modified wake oscillator model for predicting vortex induced vibration of heat exchanger tube

    International Nuclear Information System (INIS)

    Feng Zhipeng; Zang Fenggang; Zhang Yixiong; Ye Xianhui

    2014-01-01

    Base on the classical wake oscillator model, a new modified wake oscillator model is proposed, for predicting vortex induced vibration of heat exchanger tube in uniform current. The comparison between the new wake oscillator model and experimental show that the present model can simulate the characteristics of vortex induced vibration of tube. Firstly, the research shows that the coupled fluid-structure dynamical system should be modeled by combined displacement and acceleration mode. Secondly, the empirical parameter in wake oscillator model depends on the material properties of the structure, instead of being a universal constant. Lastly, the results are compared between modified wake oscillator model and fluid-structure interaction numerical model. It shows the present, predicted results are compared to the fluid-structure interaction numerical data. The new modified wake oscillator model can predict the vortex induced heat exchanger tube vibration feasibly. (authors)

  8. Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

    DEFF Research Database (Denmark)

    Morstyn, Thomas; Hredzak, Branislav; Aguilera, Ricardo P.

    2018-01-01

    , and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical......This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear $d$ – $q$ reference frame voltage...... feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real...

  9. Piloted Simulation of a Model-Predictive Automated Recovery System

    Science.gov (United States)

    Liu, James (Yuan); Litt, Jonathan; Sowers, T. Shane; Owens, A. Karl; Guo, Ten-Huei

    2014-01-01

    This presentation describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.

  10. A predictive pilot model for STOL aircraft landing

    Science.gov (United States)

    Kleinman, D. L.; Killingsworth, W. R.

    1974-01-01

    An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.

  11. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...

  12. Wind turbine control and model predictive control for uncertain systems

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

  13. Testing and analysis of internal hardwood log defect prediction models

    Science.gov (United States)

    R. Edward Thomas

    2011-01-01

    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  14. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  15. Models for predicting fuel consumption in sagebrush-dominated ecosystems

    Science.gov (United States)

    Clinton S. Wright

    2013-01-01

    Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

  16. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  17. A new, accurate predictive model for incident hypertension

    DEFF Research Database (Denmark)

    Völzke, Henry; Fung, Glenn; Ittermann, Till

    2013-01-01

    Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures.......Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....

  18. Prediction models for successful external cephalic version: a systematic review

    NARCIS (Netherlands)

    Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M.; Molkenboer, Jan F. M.; van der Post, Joris A. M.; Mol, Ben W.; Kok, Marjolein

    2015-01-01

    To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015.

  19. Hidden Markov Model for quantitative prediction of snowfall

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  20. Mathematical model for dissolved oxygen prediction in Cirata ...

    African Journals Online (AJOL)

    This paper presents the implementation and performance of mathematical model to predict theconcentration of dissolved oxygen in Cirata Reservoir, West Java by using Artificial Neural Network (ANN). The simulation program was created using Visual Studio 2012 C# software with ANN model implemented in it. Prediction ...

  1. Econometric models for predicting confusion crop ratios

    Science.gov (United States)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

    1979-01-01

    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  2. Impact of modellers' decisions on hydrological a priori predictions

    Science.gov (United States)

    Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

    2014-06-01

    In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of

  3. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  4. Reversible thermal fusing model of carbon black current-limiting thermistors

    International Nuclear Information System (INIS)

    Martin, James E.; Heaney, Michael B.

    2000-01-01

    Composites of carbon black particles in polyethylene exhibit an unusually rapid increase in resistivity as the applied electric field is increased, making this material commercially useful as current-limiting thermistors, also known as automatically resettable fuses. In this application the composite is in series with the circuit it is protecting: at low applied voltages the circuit is the load, but at high applied voltages the composite becomes the load, limiting the current to the circuit. We present a simple model of this behavior in terms of a network of nonlinear resistors. Each resistor has a resistance that depends explicitly and reversibly on its instantaneous power dissipation. This model predicts that in the soft fusing, or current-limiting, regime, where the current through the composite decreases with increasing voltage, a platelike dissipation instability develops normal to the applied field, in agreement with experimental observations, which is solely due to fluctuations in the microstructure

  5. A Bingham-plastic model for fluid mud transport under waves and currents

    Science.gov (United States)

    Liu, Chun-rong; Wu, Bo; Huhe, Ao-de

    2014-04-01

    Simplified equations of fluid mud motion, which is described as Bingham-Plastic model under waves and currents, are presented by order analysis. The simplified equations are non-linear ordinary differential equations which are solved by hybrid numerical-analytical technique. As the computational cost is very low, the effects of wave current parameters and fluid mud properties on the transportation velocity of the fluid mud are studied systematically. It is found that the fluid mud can move toward one direction even if the shear stress acting on the fluid mud bed is much smaller than the fluid mud yield stress under the condition of wave and current coexistence. Experiments of the fluid mud motion under current with fluctuation water surface are carried out. The fluid mud transportation velocity predicted by the presented mathematical model can roughly match that measured in experiments.

  6. NOx PREDICTION FOR FBC BOILERS USING EMPIRICAL MODELS

    Directory of Open Access Journals (Sweden)

    Jiří Štefanica

    2014-02-01

    Full Text Available Reliable prediction of NOx emissions can provide useful information for boiler design and fuel selection. Recently used kinetic prediction models for FBC boilers are overly complex and require large computing capacity. Even so, there are many uncertainties in the case of FBC boilers. An empirical modeling approach for NOx prediction has been used exclusively for PCC boilers. No reference is available for modifying this method for FBC conditions. This paper presents possible advantages of empirical modeling based prediction of NOx emissions for FBC boilers, together with a discussion of its limitations. Empirical models are reviewed, and are applied to operation data from FBC boilers used for combusting Czech lignite coal or coal-biomass mixtures. Modifications to the model are proposed in accordance with theoretical knowledge and prediction accuracy.

  7. Complex versus simple models: ion-channel cardiac toxicity prediction.

    Science.gov (United States)

    Mistry, Hitesh B

    2018-01-01

    There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

  8. Complex versus simple models: ion-channel cardiac toxicity prediction

    Directory of Open Access Journals (Sweden)

    Hitesh B. Mistry

    2018-02-01

    Full Text Available There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the Bnet model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

  9. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    Science.gov (United States)

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  10. [Application of ARIMA model on prediction of malaria incidence].

    Science.gov (United States)

    Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

    2016-01-29

    To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

  11. Application of Eddy Currents in Medicine and their Modelling

    International Nuclear Information System (INIS)

    Krawczyk, A.; Wiak, S.; Zyss, T.; Sikora, R.

    1998-01-01

    The paper deals with the problems of interactions between the electromagnetic field and biological material, in particular the problem of eddy currents in human tissues and cells induced there for medical purposes, and the mathematical modeling of the phenomenon. The diagnostic and therapeutic effects of eddy currents are discussed and the advantages and drawbacks of these effects are given. A deeper analysis is devoted to the problem of transcranial magnetic stimulation (TMS) which is used in psychiatry as the treatment in depressive psychosis. (author)

  12. Dynamics of edge currents in a linearly quenched Haldane model

    Science.gov (United States)

    Mardanya, Sougata; Bhattacharya, Utso; Agarwal, Amit; Dutta, Amit

    2018-03-01

    In a finite-time quantum quench of the Haldane model, the Chern number determining the topology of the bulk remains invariant, as long as the dynamics is unitary. Nonetheless, the corresponding boundary attribute, the edge current, displays interesting dynamics. For the case of sudden and adiabatic quenches the postquench edge current is solely determined by the initial and the final Hamiltonians, respectively. However for a finite-time (τ ) linear quench in a Haldane nanoribbon, we show that the evolution of the edge current from the sudden to the adiabatic limit is not monotonic in τ and has a turning point at a characteristic time scale τ =τ0 . For small τ , the excited states lead to a huge unidirectional surge in the edge current of both edges. On the other hand, in the limit of large τ , the edge current saturates to its expected equilibrium ground-state value. This competition between the two limits lead to the observed nonmonotonic behavior. Interestingly, τ0 seems to depend only on the Semenoff mass and the Haldane flux. A similar dynamics for the edge current is also expected in other systems with topological phases.

  13. Mobility Modelling through Trajectory Decomposition and Prediction

    OpenAIRE

    Faghihi, Farbod

    2017-01-01

    The ubiquity of mobile devices with positioning sensors make it possible to derive user's location at any time. However, constantly sensing the position in order to track the user's movement is not feasible, either due to the unavailability of sensors, or computational and storage burdens. In this thesis, we present and evaluate a novel approach for efficiently tracking user's movement trajectories using decomposition and prediction of trajectories. We facilitate tracking by taking advantage ...

  14. Model predictive control of a high speed switched reluctance generator system

    NARCIS (Netherlands)

    Marinkov, Sava; De Jager, Bram; Steinbuch, Maarten

    2013-01-01

    This paper presents a novel voltage control strategy for the high-speed operation of a Switched Reluctance Generator. It uses a linear Model Predictive Control law based on the average system model. The controller computes the DC-link current needed to achieve the tracking of a desired voltage

  15. Dynamic-landscape metapopulation models predict complex response of wildlife populations to climate and landscape change

    Science.gov (United States)

    Thomas W. Bonnot; Frank R. Thompson; Joshua J. Millspaugh

    2017-01-01

    The increasing need to predict how climate change will impact wildlife species has exposed limitations in how well current approaches model important biological processes at scales at which those processes interact with climate. We used a comprehensive approach that combined recent advances in landscape and population modeling into dynamic-landscape metapopulation...

  16. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  17. Predicting birth weight with conditionally linear transformation models.

    Science.gov (United States)

    Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

    2016-12-01

    Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

  18. Survey of current situation in radiation belt modeling

    Science.gov (United States)

    Fung, Shing F.

    2004-01-01

    The study of Earth's radiation belts is one of the oldest subjects in space physics. Despite the tremendous progress made in the last four decades, we still lack a complete understanding of the radiation belts in terms of their configurations, dynamics, and detailed physical accounts of their sources and sinks. The static nature of early empirical trapped radiation models, for examples, the NASA AP-8 and AE-8 models, renders those models inappropriate for predicting short-term radiation belt behaviors associated with geomagnetic storms and substorms. Due to incomplete data coverage, these models are also inaccurate at low altitudes (e.g., <1000 km) where many robotic and human space flights occur. The availability of radiation data from modern space missions and advancement in physical modeling and data management techniques have now allowed the development of new empirical and physical radiation belt models. In this paper, we will review the status of modern radiation belt modeling. Published by Elsevier Ltd on behalf of COSPAR.

  19. Models of Eucalypt phenology predict bat population flux.

    Science.gov (United States)

    Giles, John R; Plowright, Raina K; Eby, Peggy; Peel, Alison J; McCallum, Hamish

    2016-10-01

    Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt-focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86-93% accuracy. Negative binomial models explained 43-53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt-based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar-based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology

  20. 3D mathematical modelling of scour around a circular pile in current

    DEFF Research Database (Denmark)

    Roulund, Andreas; Sumer, B. Mutlu; Fredsøe, Jørgen

    1999-01-01

    This paper deals with scour around a circular pile exposed to a steady current. A 3D numerical model incorporated with the k-w,SST closure coupled with the sediment-continuity equation and a bedload sediment transport formula has been used to predict the scour. 3D calculations have also been...... carried out for a plane rigid bottom for reference purpose. The predicted flow features are apparently in fairly good agreement with the experimental data. Early calculations indicate that the model is able to predict the scour properties satisfactorily in the initial stages of the scour process, up...... to scour depth of 0.6-0.7 times the pile diameter. Calculations that describe the entire scour process (including the equilibrium stage) are underway....