WorldWideScience

Sample records for modeling short-term processes

  1. Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-05-01

    Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.

  2. Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-09-01

    Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term

  3. Modelling of stochastic fat-tailed auto-correlated processes: an application to short-term rates

    OpenAIRE

    Yashkir, Olga; Yashkir, Yuriy

    2003-01-01

    Many financial products sensitive to daily rate changes dictate the importance of adequate modelling of short-term rates. Their intrinsic properties are investigated based on historical market data. A new short-term rate model with the non-Gaussian random driver and auto-correlation factors is introduced. Special calibration procedures for the model are presented.Short-term rate stochastic dynamics are investigated in several numerical experiments.

  4. Short-Term Forecasting of Taiwanese Earthquakes Using a Universal Model of Fusion-Fission Processes

    NARCIS (Netherlands)

    Cheong, S.A.; Tan, T.L.; Chen, C.-C.; Chang, W.-L.; Liu, Z.; Chew, L.Y.; Sloot, P.M.A.; Johnson, N.F.

    2014-01-01

    Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting fr

  5. Short-Term Forecasting of Taiwanese Earthquakes Using a Universal Model of Fusion-Fission Processes

    NARCIS (Netherlands)

    Cheong, S.A.; Tan, T.L.; Chen, C.-C.; Chang, W.-L.; Liu, Z.; Chew, L.Y.; Sloot, P.M.A.; Johnson, N.F.

    2014-01-01

    Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting

  6. Short-Term Forecasting of Taiwanese Earthquakes Using a Universal Model of Fusion-Fission Processes

    Science.gov (United States)

    Cheong, Siew Ann; Tan, Teck Liang; Chen, Chien-Chih; Chang, Wu-Lung; Liu, Zheng; Chew, Lock Yue; Sloot, Peter M. A.; Johnson, Neil F.

    2014-01-01

    Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting from catalog data. We show how the equilibrium dynamics of this model very naturally explains the Gutenberg-Richter law. Using the high-resolution earthquake catalog of Taiwan between Jan 1994 and Feb 2009, we illustrate how out-of-equilibrium spatio-temporal signatures in the time interval between earthquakes and the integrated energy released by earthquakes can be used to reliably determine the times, magnitudes, and locations of large earthquakes, as well as the maximum numbers of large aftershocks that would follow. PMID:24406467

  7. Short-term forecasting of Taiwanese earthquakes using a universal model of fusion-fission processes.

    Science.gov (United States)

    Cheong, Siew Ann; Tan, Teck Liang; Chen, Chien-Chih; Chang, Wu-Lung; Liu, Zheng; Chew, Lock Yue; Sloot, Peter M A; Johnson, Neil F

    2014-01-10

    Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting from catalog data. We show how the equilibrium dynamics of this model very naturally explains the Gutenberg-Richter law. Using the high-resolution earthquake catalog of Taiwan between Jan 1994 and Feb 2009, we illustrate how out-of-equilibrium spatio-temporal signatures in the time interval between earthquakes and the integrated energy released by earthquakes can be used to reliably determine the times, magnitudes, and locations of large earthquakes, as well as the maximum numbers of large aftershocks that would follow.

  8. Long-and Short-Term Self-Learning Models of Rolling Force in Rolling Process Without Gaugemeter of Plate

    Institute of Scientific and Technical Information of China (English)

    ZHU Fu-wen; ZENG Qing-liang; HU Xian-lei; LI Xi-an; LIU Xiang-hua

    2009-01-01

    .Owing to a lack of gaugemeter and the variety of steel grades and standards in some plate mills,the longand short-term self-learning models of rolling force based on gauge soft-measuring with high precision were brought up.The soft-measuring method and target value locked method were used in these models to confirm the actual exit gauge of passes,and thick layer division and exponential smoothing method were used to dispose the deformation resistance parameter,which could be calculated from the actual data of the rolling process.The correlative mathematical methods can also be adapted to self-learning with gaugemeter.The models were applied to the process control system of AGC (automatic gauge control) reconstruction on 2 800 mm finishing mill of Anyang steel and favorable effect was obtained.

  9. Predicting short-term stock fluctuations by using processing fluency

    Science.gov (United States)

    Alter, Adam L.; Oppenheimer, Daniel M.

    2006-01-01

    Three studies investigated the impact of the psychological principle of fluency (that people tend to prefer easily processed information) on short-term share price movements. In both a laboratory study and two analyses of naturalistic real-world stock market data, fluently named stocks robustly outperformed stocks with disfluent names in the short term. For example, in one study, an initial investment of $1,000 yielded a profit of $112 more after 1 day of trading for a basket of fluently named shares than for a basket of disfluently named shares. These results imply that simple, cognitive approaches to modeling human behavior sometimes outperform more typical, complex alternatives. PMID:16754871

  10. Spatial working memory deficits in GluA1 AMPA receptor subunit knockout mice reflect impaired short-term habituation: Evidence for Wagner's dual-process memory model

    Science.gov (United States)

    Sanderson, David J.; McHugh, Stephen B.; Good, Mark A.; Sprengel, Rolf; Seeburg, Peter H.; Rawlins, J. Nicholas P.; Bannerman, David M.

    2010-01-01

    Genetically modified mice, lacking the GluA1 AMPA receptor subunit, are impaired on spatial working memory tasks, but display normal acquisition of spatial reference memory tasks. One explanation for this dissociation is that working memory, win-shift performance engages a GluA1-dependent, non-associative, short-term memory process through which animals choose relatively novel arms in preference to relatively familiar options. In contrast, spatial reference memory, as exemplified by the Morris water maze task, reflects a GluA1-independent, associative, long-term memory mechanism. These results can be accommodated by Wagner's dual-process model of memory in which short and long-term memory mechanisms exist in parallel and, under certain circumstances, compete with each other. According to our analysis, GluA1−/− mice lack short-term memory for recently experienced spatial stimuli. One consequence of this impairment is that these stimuli should remain surprising and thus be better able to form long-term associative representations. Consistent with this hypothesis, we have recently shown that long-term spatial memory for recently visited locations is enhanced in GluA1−/− mice, despite impairments in hippocampal synaptic plasticity. Taken together, these results support a role for GluA1-containing AMPA receptors in short-term habituation, and in modulating the intensity or perceived salience of stimuli. PMID:20350557

  11. 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...

  12. A nonparametric approach to the estimation of diffusion processes, with an application to a short-term interest rate model

    NARCIS (Netherlands)

    Jiang, GJ; Knight, JL

    1997-01-01

    In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any

  13. A nonparametric approach to the estimation of diffusion processes, with an application to a short-term interest rate model

    NARCIS (Netherlands)

    Jiang, GJ; Knight, JL

    1997-01-01

    In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any func

  14. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  15. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  16. 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....... The conferred results show that the prediction errors can be decreased, while the computation time is reduced....

  17. Models for short term malaria prediction in Sri Lanka

    Directory of Open Access Journals (Sweden)

    Galappaththy Gawrie NL

    2008-05-01

    Full Text Available Abstract Background Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal ARIMA models. Results The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.

  18. Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Marin Cerjan

    2014-05-01

    Full Text Available Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

  19. A Simple Hybrid Model for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Suseelatha Annamareddi

    2013-01-01

    Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.

  20. Short-term Wind Speed Forecasting Based on Gaussian Process Regression Model%基于高斯过程回归的短期风速预测

    Institute of Scientific and Technical Information of China (English)

    孙斌; 姚海涛; 刘婷

    2012-01-01

    The short-term wind speed forecasting is very important for the operation of grid-connected wind power generation systems. The accuracy forecasting of the wind speed can also effectively reduces or avoids the adverse effect of wind farm on power grid, meanwhile, strengthens competition ability of wind farm in electricity market. In order to improve the forecasting accuracy, a wind speed forecasting method based on the Gaussian process (GP) was proposed. Firstly, the embedding dimension and the delay time of the wind speed time series were respectively calculated by autocorrelation method and false neighbor method, the phase space reconstruction of the chaotic wind speed time series was received. Then, the reconstructed wind speed time series was predicted by the GP model, at the same time the "super parameter" in the covariance function was determined under the Bayesian framework. Finally, wind speed time series was used to predict by the trained GP, which was compared with support vector machine (SVM), least squares support vector machine (LSSVM) and BP neural network (BPNN). The simulation results show that GP predict model can be used to accurately predict and has stable performance. So it can be widely used in engineering practice.%准确预测风速能有效减轻风电场对整个电网的不利影响,提高风电场在电力市场中的竞争能力.为了提高风速预测的精度,提出一种基于高斯过程(Gaussian processes,GP)的风速预测模型.首先运用自相关法和假近邻法分别求取风速时间序列的延迟时间和嵌入维数,进而对混沌风速时间序列进行相空间重构.其次运用GP模型对重构后的风速时间序列进行训练,同时在贝叶斯框架下,确定协方差函数中的“超参数”.最后利用训练好的GP模型风速时间序列进行预测,并与支持向量机、最小二乘支持向量机和BP神经网络进行比较.仿真结果表明,基于GP的风速预测模型具有很好的稳定性,

  1. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

    Directory of Open Access Journals (Sweden)

    Chen Wang

    2016-01-01

    Full Text Available Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD, which reduces the effect of noise on the wind speed data; (II artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM model are optimized by the cuckoo search (CS algorithm; (III parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

  2. A Master Equation Approach to Modeling Short-term Behaviors of the Stock Market

    Science.gov (United States)

    Zhao, Conan; Yang, Xiaoxiang; Mazilu, Irina

    2015-03-01

    Short term fluctuations in stock prices are highly random, due to the multitude of external factors acting on the price determination process. While long-term economic factors such as inflation and revenue growth rate affect short-term price fluctuation, it is difficult to obtain the complete set of information and uncertainties associated with a given period of time. Instead, we propose a simpler short-term model based on only prior price averages and extrema. In this paper, we take a master equation under the random walk hypothesis and fit parameters based on AAPL stock price data over the past ten years. We report results for small system sizes and for the short term average price. These results may lead to a general closed-form solution to this particular master equation.

  3. The IEA Model of Short-term Energy Security

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2011-07-01

    Ensuring energy security has been at the centre of the IEA mission since its inception, following the oil crises of the early 1970s. While the security of oil supplies remains important, contemporary energy security policies must address all energy sources and cover a comprehensive range of natural, economic and political risks that affect energy sources, infrastructures and services. In response to this challenge, the IEA is currently developing a Model Of Short-term Energy Security (MOSES) to evaluate the energy security risks and resilience capacities of its member countries. The current version of MOSES covers short-term security of supply for primary energy sources and secondary fuels among IEA countries. It also lays the foundation for analysis of vulnerabilities of electricity and end-use energy sectors. MOSES contains a novel approach to analysing energy security, which can be used to identify energy security priorities, as a starting point for national energy security assessments and to track the evolution of a country's energy security profile. By grouping together countries with similar 'energy security profiles', MOSES depicts the energy security landscape of IEA countries. By extending the MOSES methodology to electricity security and energy services in the future, the IEA aims to develop a comprehensive policy-relevant perspective on global energy security. This Working Paper is intended for readers who wish to explore the MOSES methodology in depth; there is also a brochure which provides an overview of the analysis and results.

  4. A Latent Variable Analysis of Working Memory Capacity, Short-Term Memory Capacity, Processing Speed, and General Fluid Intelligence.

    Science.gov (United States)

    Conway, Andrew R. A.; Cowan, Nelsin; Bunting, Michael F.; Therriault, David J.; Minkoff, Scott R. B.

    2002-01-01

    Studied the interrelationships among general fluid intelligence, short-term memory capacity, working memory capacity, and processing speed in 120 young adults and used structural equation modeling to determine the best predictor of general fluid intelligence. Results suggest that working memory capacity, but not short-term memory capacity or…

  5. The Discretization Bias for Processes of the Short-Term Interest Rate : An Empirical Analysis

    OpenAIRE

    1995-01-01

    This paper compares difference continuous-time specifications for the short-term interest rate dynamics on five European markets. We propose a general specification which encompasses nine well-known processes of the financial literature. A classical estimation of the parameters leads us to the choice of simple models like the Ornstein-Uhlenbeck process of Vasicek (1977) or the “Square Root” process of Cox, Ingersoll and Ross (1985). Then we focus on the discretization bias and a methodology t...

  6. A dynamic nonstationary spatio-temporal model for short term prediction of precipitation

    OpenAIRE

    Sigrist, Fabio; Künsch, Hans R.; Stahel, Werner A.

    2011-01-01

    Precipitation is a complex physical process that varies in space and time. Predictions and interpolations at unobserved times and/or locations help to solve important problems in many areas. In this paper, we present a hierarchical Bayesian model for spatio-temporal data and apply it to obtain short term predictions of rainfall. The model incorporates physical knowledge about the underlying processes that determine rainfall, such as advection, diffusion and convection. It...

  7. Resolving the impact of short-term variations in physical processes impacting on the spawning environment of eastern Baltic cod : application of a 3-D hydrodynamic model

    DEFF Research Database (Denmark)

    Hinrichsen, H.H.; St. John, Michael; Lehmann, A.

    2002-01-01

    cod. Recent research has identified the importance of inflows of saline and oxygenated North Sea water into the Baltic Sea for the recruitment of Baltic cod. However, other processes have been suggested to modify this reproduction volume including variations in timing and volume of terrestrial runoff...... water into the Baltic, modifying wind stress, freshwater runoff and thermal inputs. The model is started from three-dimensional fields of temperature, salinity and oxygen obtained from a previous model run and forced by realistic atmospheric conditions. Results of this realistic reference run were...... compared to runs with modified meteorological forcing conditions and river runoff. From these simulations, it is apparent that processes other than major Baltic inflows have the potential to alter the reproduction volume of Baltic cod. Low near-surface air temperatures in the North Sea, the Skagerrak...

  8. Assessing the consistency between short-term global temperature trends in observations and climate model projections

    CERN Document Server

    Michaels, Patrick J; Christy, John R; Herman, Chad S; Liljegren, Lucia M; Annan, James D

    2013-01-01

    Assessing the consistency between short-term global temperature trends in observations and climate model projections is a challenging problem. While climate models capture many processes governing short-term climate fluctuations, they are not expected to simulate the specific timing of these somewhat random phenomena - the occurrence of which may impact the realized trend. Therefore, to assess model performance, we develop distributions of projected temperature trends from a collection of climate models running the IPCC A1B emissions scenario. We evaluate where observed trends of length 5 to 15 years fall within the distribution of model trends of the same length. We find that current trends lie near the lower limits of the model distributions, with cumulative probability-of-occurrence values typically between 5 percent and 20 percent, and probabilities below 5 percent not uncommon. Our results indicate cause for concern regarding the consistency between climate model projections and observed climate behavior...

  9. Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory.

    Science.gov (United States)

    Liu, Qin; Ulloa, Antonio; Horwitz, Barry

    2017-07-07

    Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320, 1998]. The original model consisted of arrays of Wilson-Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and pFC. We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT cortex, and pFC that match experimental findings. These working memory tasks can include distractor stimuli or can require that multiple items be retained in mind during a delay period (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).

  10. Tracking the Short Term Planning (STP) Development Process

    Science.gov (United States)

    Price, Melanie; Moore, Alexander

    2010-01-01

    Part of the National Aeronautics and Space Administration?s mission is to pioneer the future in space exploration, scientific discovery and aeronautics research is enhanced by discovering new scientific tools to improve life on earth. Sequentially, to successfully explore the unknown, there has to be a planning process that organizes certain events in the right priority. Therefore, the planning support team has to continually improve their processes so the ISS Mission Operations can operate smoothly and effectively. The planning support team consists of people in the Long Range Planning area that develop timelines that includes International Partner?s Preliminary STP inputs all the way through to publishing of the Final STP. Planning is a crucial part of the NASA community when it comes to planning the astronaut?s daily schedule in great detail. The STP Process is in need of improvement, because of the various tasks that are required to be broken down in order to get the overall objective of developing a Final STP done correctly. Then a new project came along in order to store various data in a more efficient database. "The SharePoint site is a Web site that provides a central storage and collaboration space for documents, information, and ideas."

  11. Enhanced stability of car-following model upon incorporation of short-term driving memory

    Science.gov (United States)

    Liu, Da-Wei; Shi, Zhong-Ke; Ai, Wen-Huan

    2017-06-01

    Based on the full velocity difference model, a new car-following model is developed to investigate the effect of short-term driving memory on traffic flow in this paper. Short-term driving memory is introduced as the influence factor of driver's anticipation behavior. The stability condition of the newly developed model is derived and the modified Korteweg-de Vries (mKdV) equation is constructed to describe the traffic behavior near the critical point. Via numerical method, evolution of a small perturbation is investigated firstly. The results show that the improvement of this new car-following model over the previous ones lies in the fact that the new model can improve the traffic stability. Starting and breaking processes of vehicles in the signalized intersection are also investigated. The numerical simulations illustrate that the new model can successfully describe the driver's anticipation behavior, and that the efficiency and safety of the vehicles passing through the signalized intersection are improved by considering short-term driving memory.

  12. Ordered Short-Term Memory Differs in Signers and Speakers: Implications for Models of Short-Term Memory

    Science.gov (United States)

    Bavelier, Daphne; Newport, Elissa L.; Hall, Matt; Supalla, Ted; Boutla, Mrim

    2008-01-01

    Capacity limits in linguistic short-term memory (STM) are typically measured with forward span tasks in which participants are asked to recall lists of words in the order presented. Using such tasks, native signers of American Sign Language (ASL) exhibit smaller spans than native speakers ([Boutla, M., Supalla, T., Newport, E. L., & Bavelier, D.…

  13. Ordered Short-Term Memory Differs in Signers and Speakers: Implications for Models of Short-Term Memory

    Science.gov (United States)

    Bavelier, Daphne; Newport, Elissa L.; Hall, Matt; Supalla, Ted; Boutla, Mrim

    2008-01-01

    Capacity limits in linguistic short-term memory (STM) are typically measured with forward span tasks in which participants are asked to recall lists of words in the order presented. Using such tasks, native signers of American Sign Language (ASL) exhibit smaller spans than native speakers ([Boutla, M., Supalla, T., Newport, E. L., & Bavelier, D.…

  14. Short-term time step convergence in a climate model.

    Science.gov (United States)

    Wan, Hui; Rasch, Philip J; Taylor, Mark A; Jablonowski, Christiane

    2015-03-01

    This paper evaluates the numerical convergence of very short (1 h) simulations carried out with a spectral-element (SE) configuration of the Community Atmosphere Model version 5 (CAM5). While the horizontal grid spacing is fixed at approximately 110 km, the process-coupling time step is varied between 1800 and 1 s to reveal the convergence rate with respect to the temporal resolution. Special attention is paid to the behavior of the parameterized subgrid-scale physics. First, a dynamical core test with reduced dynamics time steps is presented. The results demonstrate that the experimental setup is able to correctly assess the convergence rate of the discrete solutions to the adiabatic equations of atmospheric motion. Second, results from full-physics CAM5 simulations with reduced physics and dynamics time steps are discussed. It is shown that the convergence rate is 0.4-considerably slower than the expected rate of 1.0. Sensitivity experiments indicate that, among the various subgrid-scale physical parameterizations, the stratiform cloud schemes are associated with the largest time-stepping errors, and are the primary cause of slow time step convergence. While the details of our findings are model specific, the general test procedure is applicable to any atmospheric general circulation model. The need for more accurate numerical treatments of physical parameterizations, especially the representation of stratiform clouds, is likely common in many models. The suggested test technique can help quantify the time-stepping errors and identify the related model sensitivities.

  15. Short term forecasting of surface layer wind speed using a continuous cascade model

    CERN Document Server

    Baile, Rachel; Poggi, Philippe

    2010-01-01

    This paper describes a statistical method for short-term forecasting of surface layer wind velocity amplitude relying on the notion of continuous cascades. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal component and a fluctuating part represented by a "multifractal noise" associated with a random cascade. Performances of our model are tested on hourly wind speed series gathered at various locations in Corsica (France) and Netherlands. The obtained results show a systematic improvement of the prediction as compared to reference models like persistence or Artificial Neural Networks.

  16. SHORT-TERM SELLING OF A STOCK: A MODEL

    Directory of Open Access Journals (Sweden)

    Pritibhushan Sinha

    2011-03-01

    Full Text Available This article presents a discrete period model for decision-making related to selling of the units ofa stock. An exact analysis of the model is given and a method for determining optimal decisions isdescribed. Application of the model is illustrated with some relevant numerical examples.

  17. Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhisheng Zhang

    2016-01-01

    Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

  18. A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

    Institute of Scientific and Technical Information of China (English)

    JIN Long; JIN Jian; YAO Cai

    2005-01-01

    In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the selfadaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN)model, does not occur, indicating a better practical application potential of the MFNN model.

  19. Short-term Wind Speed Forecasting Based on Phase-space Reconstruction and Evolutionary Gaussian Process Model%基于相空间重构和进化高斯过程的短期风速预测

    Institute of Scientific and Technical Information of China (English)

    常纯; 李德胜

    2016-01-01

    A short-term wind speed forecasting method based on phase-space reconstruction and evolutionary Gaussian process model is proposed in this paper .Firstly, the autocorrelation method and false nearest neighbor method are applied to calculate the delay time and embedding dimension of the wind speed time series , which are used to accomplish the phase-space reconstruction of the chaotic wind speed time series .Secondly, the evolutionary Gaussian process model , which combines Gaussian process with evolutionary algorithm , is used to forcast the wind speed .This model uses Gaussian process model to determine the relationship between the input and output variables , and the improved PSO algorithm to optimize the hyper parameters .The prediction results show that the proposed method can improve the prediction accuracy .%提出一种基于相空间重构和进化高斯过程的短期风速预测方法。首先,运用自相关法和假近邻法分别得出原始风速时间序列的延迟时间和嵌入维数,实现混沌风速时间序列的相空间重构;然后,运用进化高斯过程回归模型进行建模,通过高斯过程模型确定输入量和输出量之间的关系,并用改进粒子群算法求取最优超参数。根据某实测风速数据进行了风速预测,结果表明本文所提出的方法能有效提高风速预测精度。

  20. The Cultural Adaptation Process during a Short-Term Study Abroad Experience in Swaziland

    Science.gov (United States)

    Conner, Nathan W.; Roberts, T. Grady

    2015-01-01

    Globalization continuously shapes our world and influences post-secondary education. This study explored the cultural adaptation process of participants during a short-term study abroad program. Participants experienced stages which included initial feelings, cultural uncertainty, cultural barriers, cultural negativity, academic and career growth,…

  1. A neural network model for short term river flow prediction

    OpenAIRE

    2006-01-01

    International audience; This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar meas...

  2. A neural network model for short term river flow prediction

    Science.gov (United States)

    Teschl, R.; Randeu, W. L.

    2006-07-01

    This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar measurements (pixels) representing areas requiring approximately the same time to dewater are grouped.

  3. A neural network model for short term river flow prediction

    Directory of Open Access Journals (Sweden)

    R. Teschl

    2006-01-01

    Full Text Available This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar measurements (pixels representing areas requiring approximately the same time to dewater are grouped.

  4. Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

    OpenAIRE

    Haixiang Zang; Lei Fan; Mian Guo; Zhinong Wei; Guoqiang Sun; Li Zhang

    2016-01-01

    Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EE...

  5. Relations between Short-term Memory Deficits, Semantic Processing, and Executive Function

    Science.gov (United States)

    Allen, Corinne M.; Martin, Randi C.; Martin, Nadine

    2012-01-01

    Background Previous research has suggested separable short-term memory (STM) buffers for the maintenance of phonological and lexical-semantic information, as some patients with aphasia show better ability to retain semantic than phonological information and others show the reverse. Recently, researchers have proposed that deficits to the maintenance of semantic information in STM are related to executive control abilities. Aims The present study investigated the relationship of executive function abilities with semantic and phonological short-term memory (STM) and semantic processing in such patients, as some previous research has suggested that semantic STM deficits and semantic processing abilities are critically related to specific or general executive function deficits. Method and Procedures 20 patients with aphasia and STM deficits were tested on measures of short-term retention, semantic processing, and both complex and simple executive function tasks. Outcome and Results In correlational analyses, we found no relation between semantic STM and performance on simple or complex executive function tasks. In contrast, phonological STM was related to executive function performance in tasks that had a verbal component, suggesting that performance in some executive function tasks depends on maintaining or rehearsing phonological codes. Although semantic STM was not related to executive function ability, performance on semantic processing tasks was related to executive function, perhaps due to similar executive task requirements in both semantic processing and executive function tasks. Conclusions Implications for treatment and interpretations of executive deficits are discussed. PMID:22736889

  6. Parametric and non-parametric modeling of short-term synaptic plasticity. Part II: Experimental study.

    Science.gov (United States)

    Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W

    2009-02-01

    This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.

  7. A Comparison of Different Short-Term Macroeconomic Forecasting Models: Evidence from Armenia

    Directory of Open Access Journals (Sweden)

    Poghosyan Karen

    2016-05-01

    Full Text Available We evaluate the forecasting performance of four competing models for short-term macroeconomic forecasting: the traditional VAR, small scale Bayesian VAR, Factor Augmented VAR and Bayesian Factor Augmented VAR models. Using Armenian quarterly actual macroeconomic time series from 1996Q1 – 2014Q4, we estimate parameters of four competing models. Based on the out-of-sample recursive forecast evaluations and using root mean squared error (RMSE criterion we conclude that small scale Bayesian VAR and Bayesian Factor Augmented VAR models are more suitable for short-term forecasting than traditional unrestricted VAR model.

  8. Long and Short-Term Memory Processes in Cortically Damaged Patients.

    Science.gov (United States)

    1981-01-01

    Atkinson , J. R., and Shiffrin , R. M1. (1968) Human Memory : A proposed system and its control processes. In K. W. Spence and J. T. Spence (Eds.), Advances...rehearse and thus penalized the initial region of the curve. Atkinson and Shiffrin (1968) further elaborated upon the dual storage system. In...Psychology 5 - Shallice, T., and Warrington, E. (1977) Auditory-verbal short- term memory and conduction aphasia. Brain and Language 4, 479-491. Shiffrin , R

  9. Standardizing the performance evaluation of short-term wind prediction models

    DEFF Research Database (Denmark)

    Madsen, Henrik; Pinson, Pierre; Kariniotakis, G.

    2005-01-01

    Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective...... evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term wind-poser preciction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated using results...

  10. Complex network structure influences processing in long-term and short-term memory

    Science.gov (United States)

    Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven

    2012-01-01

    Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological word-forms influenced retrieval from the mental lexicon (that portion of long-term memory dedicated to language) during the on-line recognition and production of spoken words. In the present study we examined how network structure influences other retrieval processes in long- and short-term memory. In a false-memory task—examining long-term memory—participants falsely recognized more words with low- than high-C. In a recognition memory task—examining veridical memories in long-term memory—participants correctly recognized more words with low- than high-C. However, participants in a serial recall task—examining redintegration in short-term memory—recalled lists comprised of high-C words more accurately than lists comprised of low-C words. These results demonstrate that network structure influences cognitive processes associated with several forms of memory including lexical, long-term, and short-term. PMID:22745522

  11. Short term uptake and transport process for metformin in roots of Phragmites australis and Typha latifolia.

    Science.gov (United States)

    Cui, H; Hense, B A; Müller, J; Schröder, P

    2015-09-01

    Metformin (MET) as an emerging contaminant has been detected in surface water and wastewater in numerous countries, due to insufficient retention in classical waste water treatment plants. In order to characterize the uptake of the compound during phytotreatment of waste water, a short term Pitman chamber experiment was carried out to assess the characteristics of MET uptake and transport by roots. Three different concentrations (0.5, 1.0 and 2.0 mmol L(-)(1)) were applied to cattail (Typha latifolia) and reed (Phragmites australis) roots which were used to investigate the uptake mechanism because they are frequently utilized in phytoremediation. In addition, quinidine was used as an inhibitor to assess the role of organic cation transporters (OCTs) in the uptake of MET by T. latifolia. The transport process of MET is different from carbamazepine (CBZ) and caffeine (CFN). In both T. latifolia and P. australis, the uptake processes were independent of initial concentrations. Quinidine, a known inhibitor of organic cation transporters, can significantly affect MET uptake by T. latifolia roots with inhibition ratios of 70-74%. Uptake into the root could be characterized by a linear model with R(2) values in the range of 0.881-0.999. Overall, the present study provides evidence that MET is taken up by plant roots and has the potential for subsequent translocation. OCTs could be one of the important pathways for MET uptake into the plant. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Short-term load forecast using trend information and process reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Santos, P.J.; Pires, A.J.; Martins, J.F. [Instituto Politecnico de Setubal (Portugal). Dept. of Electrical Engineering; Martins, A.G. [University of Coimbra (Portugal). Dept. of Electrical Engineering; Mendes, R.V. [Instituto Superior Tecnico, Lisboa (Portugal). Laboratorio de Mecatronica

    2005-07-01

    The algorithms for short-term load forecast (STLF), especially within the next-hour horizon, belong to a group of methodologies that aim to render more effective the actions of planning, operating and controlling electric energy systems (EES). In the context of the progressive liberalization of the electricity sector, unbundling of the previous monopolistic structure emphasizes the need for load forecast, particularly at the network level. Methodologies such as artificial neural networks (ANN) have been widely used in next-hour load forecast. Designing an ANN requires the proper choice of input variables, avoiding overfitting and an unnecessarily complex input vector (IV). This may be achieved by trying to reduce the arbitrariness in the choice of endogenous variables. At a first stage, we have applied the mathematical techniques of process-reconstruction to the underlying stochastic process, using coding and block entropies to characterize the measure and memory range. At a second stage, the concept of consumption trend in homologous days of previous weeks has been used. The possibility to include weather-related variables in the IV has also been analysed, the option finally being to establish a model of the non-weather sensitive type. The paper uses a real-life case study. (author)

  13. Wavelet time series MPARIMA modeling for power system short term load forecasting

    Institute of Scientific and Technical Information of China (English)

    冉启文; 单永正; 王建赜; 王骐

    2003-01-01

    The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near-periodicity, nonstationarity and nonlinearity existed in power system short term quarter-hour load time series, and can therefore accurately forecast the quarter-hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.

  14. A long-term/short-term model for daily electricity prices with dynamic volatility

    Energy Technology Data Exchange (ETDEWEB)

    Schlueter, Stephan

    2010-09-15

    In this paper we introduce a new stochastic long-term/short-term model for short-term electricity prices, and apply it to four major European indices, namely to the German, Dutch, UK and Nordic one. We give evidence that all time series contain certain periodic (mostly annual) patterns, and show how to use the wavelet transform, a tool of multiresolution analysis, for filtering purpose. The wavelet transform is also applied to separate the long-term trend from the short-term oscillation in the seasonal-adjusted log-prices. In all time series we find evidence for dynamic volatility, which we incorporate by using a bivariate GARCH model with constant correlation. Eventually we fit various models from the existing literature to the data, and come to the conclusion that our approach performs best. For the error distribution, the Normal Inverse Gaussian distribution shows the best fit. (author)

  15. A fully adaptive forecasting model for short-term drinking water demand

    NARCIS (Netherlands)

    Bakker, M.; Vreeburg, J.H.G.; Schagen, van K.M.; Rietveld, L.C.

    2013-01-01

    For the optimal control of a water supply system, a short-term water demand forecast is necessary. We developed a model that forecasts the water demand for the next 48 h with 15-min time steps. The model uses measured water demands and static calendar data as single input. Based on this input, the m

  16. Folk music style modelling by recurrent neural networks with long short term memory units

    OpenAIRE

    Sturm, Bob; Santos, João Felipe; Korshunova, Iryna

    2015-01-01

    We demonstrate two generative models created by training a recurrent neural network (RNN) with three hidden layers of long short-term memory (LSTM) units. This extends past work in numerous directions, including training deeper models with nearly 24,000 high-level transcriptions of folk tunes. We discuss our on-going work.

  17. Postscript: More Problems with Botvinick and Plaut's (2006) PDP Model of Short-Term Memory

    Science.gov (United States)

    Bowers, Jeffrey S.; Damian, Markus F.; Davis, Colin J.

    2009-01-01

    Presents a postscript to the current authors' comment on the original article, "Short-term memory for serial order: A recurrent neural network model," by M. M. Botvinick and D. C. Plaut. In their commentary, the current authors demonstrated that Botvinick and Plaut's (2006) model of immediate serial recall catastrophically fails when familiar…

  18. Short-Term Memory for Serial Order: A Recurrent Neural Network Model

    Science.gov (United States)

    Botvinick, Matthew M.; Plaut, David C.

    2006-01-01

    Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according…

  19. Jump Diffusion Modelling for the Brazilian Short-Term Interest Rate

    Directory of Open Access Journals (Sweden)

    José Carlos Nogueira Cavalcante Filho

    2015-01-01

    Full Text Available In order to capture the informational effect of the Brazilian short-term interest rate (SELIC rate by Poisson jumps, we build on the tests condu cted by Das (2002 and Johannes (2004, which show the significance of such structures for U.S. Federal Open Market Committee (FOMC announcements. As in the above researches, w e have found evidence that a relevant amount of the short-term volatility in the fixed in come market is captured by introducing jumps on the stochastic process of the short-term r ate. This structure also allows the verification of the information content of specific events, such as Brazilian monetary policy authority (COPOM meetings and public bond auctions.

  20. Short-term load forecasting based on a multi-model

    Energy Technology Data Exchange (ETDEWEB)

    Faller, C. [ETH, Zurich (Switzerland). Faculty of Electrical Engineering; Dvorakova, R.; Horacek, P. [Czech Technical University (Czech Republic). Faculty of Electrical Engineering

    2000-07-01

    Two algorithms for short-term electricity demand forecasting in the regional electricity distribution network are presented. Several approaches - feedforward neural network, adaptive modelling and fuzzy modelling - are applied to the forecast. Two different models are designed. A one hour forecasting is based on the General Regression Neural Network (GRNN) model and Principle Component Analysis. The multi-model with adaptive features and fuzzy reasoning is used for a longer-term forecast. (author)

  1. Neural evidence for a 3-state model of visual short-term memory.

    Science.gov (United States)

    Nee, Derek Evan; Jonides, John

    2013-07-01

    Recent research has suggested that short-term memory (STM) can be partitioned into three distinct states. By this model, a single item is held in the focus of attention making it available for immediate processing (focus of attention), a capacity-limited set of additional items is actively maintained for future processing (direct access region), and other recently presented information is passively active, but can nevertheless influence ongoing cognition (activated portion of long-term memory). While there is both behavioral and neural support for this 3-state model in verbal STM, it is unclear whether the model generalizes to non-verbal STM. Here, we tested a 3-state model of visual STM using fMRI. We found a triple dissociation of regions involved in the access of each hypothesized state. The inferior parietal cortex mediated access to the focus of attention, the medial temporal lobe (MTL) including the hippocampus mediated access to the direct access region, and the left ventrolateral prefrontal cortex (VLPFC) mediated access to the activated portion of long-term memory. Direct comparison with previously collected verbal STM data revealed overlapping neural activations involved in the access of each state across different forms of content suggesting that mechanisms of access are domain general. These data support a 3-state model of STM.

  2. Competitive short-term and long-term memory processes in spatial habituation.

    Science.gov (United States)

    Sanderson, David J; Bannerman, David M

    2011-04-01

    Exposure to a spatial location leads to habituation of exploration such that, in a novelty preference test, rodents subsequently prefer exploring a novel location to the familiar location. According to Wagner's (1981) theory of memory, short-term and long-term habituation are caused by separate and sometimes opponent processes. In the present study, this dual-process account of memory was tested. Mice received a series of exposure training trials to a location before receiving a novelty preference test. The novelty preference was greater when tested after a short, rather than a long, interval. In contrast, the novelty preference was weaker when exposure training trials were separated by a short, rather than a long interval. Furthermore, it was found that long-term habituation was determined by the independent effects of the amount of exposure training and the number of exposure training trials when factors such as the intertrial interval and the cumulative intertrial interval were controlled. A final experiment demonstrated that a long-term reduction of exploration could be caused by a negative priming effect due to associations formed during exploration. These results provide evidence against a single-process account of habituation and suggest that spatial habituation is determined by both short-term, recency-based memory and long-term, incrementally strengthened memory.

  3. Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Tansu Filik

    2016-03-01

    Full Text Available In this paper, the spatio-temporal (multi-channel linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and revised. It is shown in detail how to apply these MARMA models to the spatially distributed wind speed measurements. The proposed MARMA models are tested using real wind speed measurements which are collected from the five stations around Canakkale region of Turkey. According to the test results, considerable improvements are observed over the well known persistence, autoregressive (AR and multi-channel/vector autoregressive (VAR models. It is also shown that the model can predict wind speed very fast (in milliseconds which is suitable for the immediate short-term forecasting.

  4. A feature-segmentation model of short-term visual memory.

    Science.gov (United States)

    Sakai, Koji; Inui, Toshio

    2002-01-01

    A feature-segmentation model of short-term visual memory (STVM) for contours is proposed. Memory of the First stimulus is maintained until the second stimulus is observed. Three processes interact to determine the relationship between stimulus and response: feature encoding, memory, and decision. Basic assumptions of the model are twofold: (i) the STVM system divides a contour into convex parts at regions of concavity; and (ii) the value of each convex part represented in STVM is an independent Gaussian random variable. Simulation showed that the five-parameter fits give a good account of the effects of the four experimental variables. The model provides evidence that: (i) contours are successfully encoded within 0.5 s exposure, regardless of pattern complexity; (ii) memory noise increases as a linear function of retention interval; (iii) the capacity of STVM, defined by pattern complexity (the degree that a pattern can be handled for several seconds with little loss), is about 4 convex parts; and (iv) the confusability contributing to the decision process is a primary factor in deteriorating recognition of complex figures. It is concluded that visually presented patterns can be retained in STVM with considerable precision for prolonged periods of time, though some loss of precision is inevitable.

  5. Short-term and long-term earthquake occurrence models for Italy: ETES, ERS and LTST

    Directory of Open Access Journals (Sweden)

    Maura Murru

    2010-11-01

    Full Text Available This study describes three earthquake occurrence models as applied to the whole Italian territory, to assess the occurrence probabilities of future (M ≥5.0 earthquakes: two as short-term (24 hour models, and one as long-term (5 and 10 years. The first model for short-term forecasts is a purely stochastic epidemic type earthquake sequence (ETES model. The second short-term model is an epidemic rate-state (ERS forecast based on a model that is physically constrained by the application to the earthquake clustering of the Dieterich rate-state constitutive law. The third forecast is based on a long-term stress transfer (LTST model that considers the perturbations of earthquake probability for interacting faults by static Coulomb stress changes. These models have been submitted to the Collaboratory for the Study of Earthquake Predictability (CSEP for forecast testing for Italy (ETH-Zurich, and they were locked down to test their validity on real data in a future setting starting from August 1, 2009.

  6. Short-term estimation of GNSS TEC using a neural network model in Brazil

    Science.gov (United States)

    Ferreira, Arthur Amaral; Borges, Renato Alves; Paparini, Claudia; Ciraolo, Luigi; Radicella, Sandro M.

    2017-10-01

    This work presents a novel Neural Network (NN) model to estimate Total Electron Content (TEC) from Global Navigation Satellite Systems (GNSS) measurements in three distinct sectors in Brazil. The purpose of this work is to start the investigations on the development of a regional model that can be used to determine the vertical TEC over Brazil, aiming future applications on a near real-time frame estimations and short-term forecasting. The NN is used to estimate the GNSS TEC values at void locations, where no dual-frequency GNSS receiver that may be used as a source of data to GNSS TEC estimation is available. This approach is particularly useful for GNSS single-frequency users that rely on corrections of ionospheric range errors by TEC models. GNSS data from the first GLONASS network for research and development (GLONASS R&D network) installed in Latin America, and from the Brazilian Network for Continuous Monitoring of the GNSS (RMBC) were used on TEC calibration. The input parameters of the NN model are based on features known to influence TEC values, such as geographic location of the GNSS receiver, magnetic activity, seasonal and diurnal variations, and solar activity. Data from two ten-days periods (from DoY 154 to 163 and from 282 to 291) are used to train the network. Three distinct analyses have been carried out in order to assess time-varying and spatial performance of the model. At the spatial performance analysis, for each region, a set of stations is chosen to provide training data to the NN, and after the training procedure, the NN is used to estimate vTEC behavior for the test station which data were not presented to the NN in training process. An analysis is done by comparing, for each testing station, the estimated NN vTEC delivered by the NN and reference calibrated vTEC. Also, as a second analysis, the network ability to forecast one day after the time interval (DoY 292) based on information of the second period of investigation is also assessed

  7. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    OpenAIRE

    Wang, Fei; Mi, Zengqiang; Su, Shi; Zhao, Hongshan

    2012-01-01

    Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need t...

  8. Space station short-term mission planning using ontology modelling and time iteration

    Institute of Scientific and Technical Information of China (English)

    Huijiao Bu; Jin Zhang; Yazhong Luo

    2016-01-01

    This paper studies the problem of the space station short-term mission planning, which aims to alocate the exe-cuting time of missions effectively, schedule the correspon- ding resources reasonably and arrange the time of the as-tronauts properly. A domain model is developed by using the ontology theory to describe the concepts, constraints and relations of the planning domain formaly, abstractly and normatively. A method based on time iteration is adopted to solve the short-term planning problem. Meanwhile, the re-solving strategies are proposed to resolve different kinds of conflicts induced by the constraints of power, heat, resource, astronaut and relationship. The proposed approach is evalu-ated in a test case with fifteen missions, thirteen resources and three astronauts. The results show that the developed domain ontology model is reasonable, and the time iteration method using the proposed resolving strategies can suc-cessfuly obtain the plan satisfying al considered constraints.

  9. Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

    Directory of Open Access Journals (Sweden)

    Haixiang Zang

    2016-01-01

    Full Text Available Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD, runs test (RT, and relevance vector machine (RVM. First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF components and residual (RES component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy.

  10. Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction

    Energy Technology Data Exchange (ETDEWEB)

    Salcedo-Sanz, Sancho; Perez-Bellido, Angel M.; Ortiz-Garcia, Emilio G.; Portilla-Figueras, Antonio [Department of Signal Theory and Communications, Universidad de Alcala, Madrid (Spain); Prieto, Luis [Wind Resource Department, Iberdrola Renovables, Madrid (Spain); Paredes, Daniel [Department of Physics of the Earth, Astronomy and Astrophysics II, Universidad Complutense de Madrid (Spain)

    2009-06-15

    This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points. (author)

  11. A Neural Network Model of the Visual Short-Term Memory

    DEFF Research Database (Denmark)

    Petersen, Anders; Kyllingsbæk, Søren; Hansen, Lars Kai

    2009-01-01

    In this paper a neural network model of Visual Short-Term Memory (VSTM) is presented. The model links closely with Bundesen’s (1990) well-established mathematical theory of visual attention. We evaluate the model’s ability to fit experimental data from a classical whole and partial report study....... Previous statistic models have successfully assessed the spatial distribution of visual attention; our neural network meets this standard and offers a neural interpretation of how objects are consolidated in VSTM at the same time. We hope that in the future, the model will be able to fit temporally...

  12. Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data.

    Science.gov (United States)

    Chen, Feng; Ma, Xiaoxiang; Chen, Suren; Yang, Lin

    2016-10-26

    Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world.

  13. A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation.

    Science.gov (United States)

    Fiebig, Florian; Lansner, Anders

    2017-01-04

    A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism. Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying

  14. A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

    Science.gov (United States)

    Fiebig, Florian

    2017-01-01

    A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism. SIGNIFICANCE STATEMENT Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and

  15. Standardizing the performance evaluation of short-term wind prediction models

    DEFF Research Database (Denmark)

    Madsen, Henrik; Pinson, Pierre; Kariniotakis, G.;

    2005-01-01

    evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term wind-poser preciction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated using results...... from both on-shore and off-shore wind forms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems....

  16. Effect of short-term escitalopram treatment on neural activation during emotional processing.

    Science.gov (United States)

    Maron, Eduard; Wall, Matt; Norbury, Ray; Godlewska, Beata; Terbeck, Sylvia; Cowen, Philip; Matthews, Paul; Nutt, David J

    2016-01-01

    Recent functional magnetic resonance (fMRI) imaging studies have revealed that subchronic medication with escitalopram leads to significant reduction in both amygdala and medial frontal gyrus reactivity during processing of emotional faces, suggesting that escitalopram may have a distinguishable modulatory effect on neural activation as compared with other serotonin-selective antidepressants. In this fMRI study we aimed to explore whether short-term medication with escitalopram in healthy volunteers is associated with reduced neural response to emotional processing, and whether this effect is predicted by drug plasma concentration. The neural response to fearful and happy faces was measured before and on day 7 of treatment with escitalopram (10mg) in 15 healthy volunteers and compared with those in a control unmedicated group (n=14). Significantly reduced activation to fearful, but not to happy facial expressions was observed in the bilateral amygdala, cingulate and right medial frontal gyrus following escitalopram medication. This effect was not correlated with plasma drug concentration. In accordance with previous data, we showed that escitalopram exerts its rapid direct effect on emotional processing via attenuation of neural activation in pathways involving medial frontal gyrus and amygdala, an effect that seems to be distinguishable from that of other SSRIs.

  17. Ontogeny of sensorimotor gating and short-term memory processing throughout the adolescent period in rats

    Directory of Open Access Journals (Sweden)

    Anja A. Goepfrich

    2017-06-01

    Full Text Available Adolescence and puberty are highly susceptible developmental periods during which the neuronal organization and maturation of the brain is completed. The endocannabinoid (eCB system, which is well known to modulate cognitive processing, undergoes profound and transient developmental changes during adolescence. With the present study we were aiming to examine the ontogeny of cognitive skills throughout adolescence in male rats and clarify the potential modulatory role of CB1 receptor signalling. Cognitive skills were assessed repeatedly every 10th day in rats throughout adolescence. All animals were tested for object recognition memory and prepulse inhibition of the acoustic startle reflex. Although cognitive performance in short-term memory as well as sensorimotor gating abilities were decreased during puberty compared to adulthood, both tasks were found to show different developmental trajectories throughout adolescence. A low dose of the CB1 receptor antagonist/inverse agonist SR141716 was found to improve recognition memory specifically in pubertal animals while not affecting behavioral performance at other ages tested. The present findings demonstrate that the developmental trajectory of cognitive abilities does not occur linearly for all cognitive processes and is strongly influenced by pubertal maturation. Developmental alterations within the eCB system at puberty onset may be involved in these changes in cognitive processing.

  18. A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

    Directory of Open Access Journals (Sweden)

    Francesca Gagliardi

    2017-07-01

    Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

  19. Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

  20. Short-term droughts forecast using Markov chain model in Victoria, Australia

    Science.gov (United States)

    Rahmat, Siti Nazahiyah; Jayasuriya, Niranjali; Bhuiyan, Muhammed A.

    2017-07-01

    A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.

  1. Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

    Directory of Open Access Journals (Sweden)

    Zhang Chi

    2016-01-01

    Full Text Available Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.

  2. Statistical Models for Tornado Climatology: Long and Short-Term Views

    CERN Document Server

    Elsner, James B; Fricker, Tyler

    2016-01-01

    This paper estimates local tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies the shift of tornado activity away from the Ohio Valley under El Ni\\~no conditions and away from the S...

  3. A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

    Directory of Open Access Journals (Sweden)

    Yuan-Kang Wu

    2014-01-01

    Full Text Available The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

  4. Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts

    Science.gov (United States)

    AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.

    2014-12-01

    Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using

  5. A phenomenological memristor model for short-term/long-term memory

    Science.gov (United States)

    Chen, Ling; Li, Chuandong; Huang, Tingwen; Ahmad, Hafiz Gulfam; Chen, Yiran

    2014-08-01

    Memristor is considered to be a natural electrical synapse because of its distinct memory property and nanoscale. In recent years, more and more similar behaviors are observed between memristors and biological synapse, e.g., short-term memory (STM) and long-term memory (LTM). The traditional mathematical models are unable to capture the new emerging behaviors. In this article, an updated phenomenological model based on the model of the Hewlett-Packard (HP) Labs has been proposed to capture such new behaviors. The new dynamical memristor model with an improved ion diffusion term can emulate the synapse behavior with forgetting effect, and exhibit the transformation between the STM and the LTM. Further, this model can be used in building new type of neural networks with forgetting ability like biological systems, and it is verified by our experiment with Hopfield neural network.

  6. A Hybrid Short-Term Power Load Forecasting Model Based on the Singular Spectrum Analysis and Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Hongze Li

    2014-01-01

    Full Text Available Short-term power load forecasting is one of the most important issues in the economic and reliable operation of electricity power system. Taking the characteristics of randomness, tendency, and periodicity of short-term power load into account, a new method (SSA-AR model which combines the univariate singular spectrum analysis and autoregressive model is proposed. Firstly, the singular spectrum analysis (SSA is employed to decompose and reconstruct the original power load series. Secondly, the autoregressive (AR model is used to forecast based on the reconstructed power load series. The employed data is the hourly power load series of the Mid-Atlantic region in PJM electricity market. Empirical analysis result shows that, compared with the single autoregressive model (AR, SSA-based linear recurrent method (SSA-LRF, and BPNN (backpropagation neural network model, the proposed SSA-AR method has a better performance in terms of short-term power load forecasting.

  7. The attention-weighted sample-size model of visual short-term memory

    DEFF Research Database (Denmark)

    Smith, Philip L.; Lilburn, Simon D.; Corbett, Elaine A.

    2016-01-01

    exceeded that predicted by the sample-size model for both simultaneously and sequentially presented stimuli. Instead, the set-size effect and the serial position curves with sequential presentation were predicted by an attention-weighted version of the sample-size model, which assumes that one of the items......We investigated the capacity of visual short-term memory (VSTM) in a phase discrimination task that required judgments about the configural relations between pairs of black and white features. Sewell et al. (2014) previously showed that VSTM capacity in an orientation discrimination task was well...... described by a sample-size model, which views VSTM as a resource comprised of a finite number of noisy stimulus samples. The model predicts the invariance of ∑i(di ′)2, the sum of squared sensitivities across items, for displays of different sizes. For phase discrimination, the set-size effect significantly...

  8. Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

    Directory of Open Access Journals (Sweden)

    E. Faghihnia

    2014-01-01

    Full Text Available Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF approach, trained by the polynomial model tree (POLYMOT learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.

  9. Multivariate time series modeling of short-term system scale irrigation demand

    Science.gov (United States)

    Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

    2015-12-01

    Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as

  10. A Short Term Therapy Approach to Processing Trauma: Art Therapy and Bilateral Stimulation

    Science.gov (United States)

    Tripp, Tally

    2007-01-01

    This article describes a dynamic, short-term art therapy approach that has been developed for the treatment of trauma related disorders. Using a modified Eye Movement Desensitization and Reprocessing (EMDR) protocol with alternating tactile and auditory bilateral stimulation, associations are rapidly brought to conscious awareness and expressed in…

  11. A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

    Science.gov (United States)

    Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Jedlovec, Gary J.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and

  12. Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2013-01-01

    Full Text Available We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV plants: the analytical PV power forecasting model (APVF and the multiplayer perceptron PV forecasting model (MPVF. Both models use forecasts from numerical weather prediction (NWP tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs. The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.

  13. Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity

    Directory of Open Access Journals (Sweden)

    M. Sonia Terreros-Olarte

    2013-05-01

    Full Text Available This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV plant. The model is called HIstorical SImilar MIning (HISIMI model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.

  14. Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

    Directory of Open Access Journals (Sweden)

    Zhifeng Zhong

    2017-01-01

    Full Text Available Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.

  15. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    Directory of Open Access Journals (Sweden)

    Hongshan Zhao

    2012-05-01

    Full Text Available Short-term solar irradiance forecasting (STSIF is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV, and the Levenberg-Marquardt algorithm (LMA is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS, and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

  16. Relative performance of different numerical weather prediction models for short term predition of wind wnergy

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Landberg, L. [Risoe National Lab., Wind Energy and Atmospheric Physics Dept., Roskilde (Denmark); Moennich, K.; Waldl, H.P. [Carl con Ossietzky Univ., Faculty of Physics, Dept. of Energy and Semiconductor, Oldenburg (Germany)

    1999-03-01

    In several approaches presented in other papers in this conference, short term forecasting of wind power for a time horizon covering the next two days is done on the basis of Numerical Weather Prediction (NWP) models. This paper explores the relative merits of HIRLAM, which is the model used by the Danish Meteorological Institute, the Deutschlandmodell from the German Weather Service and the Nested Grid Model used in the US. The performance comparison will be mainly done for a site in Germany which is in the forecasting area of both the Deutschlandmodell and HIRLAM. In addition, a comparison of measured data with the forecasts made for one site in Iowa will be included, which allows conclusions on the merits of all three models. Differences in the relative performances could be due to a better tailoring of one model to its country, or to a tighter grid, or could be a function of the distance between the grid points and the measuring site. Also the amount, in which the performance can be enhanced by the use of model output statistics (topic of other papers in this conference) could give insights into the performance of the models. (au)

  17. Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model

    Directory of Open Access Journals (Sweden)

    Han Jiang

    2016-01-01

    Full Text Available Recently, a number of short-term speed prediction approaches have been developed, in which most algorithms are based on machine learning and statistical theory. This paper examined the multistep ahead prediction performance of eight different models using the 2-minute travel speed data collected from three Remote Traffic Microwave Sensors located on a southbound segment of 4th ring road in Beijing City. Specifically, we consider five machine learning methods: Back Propagation Neural Network (BPNN, nonlinear autoregressive model with exogenous inputs neural network (NARXNN, support vector machine with radial basis function as kernel function (SVM-RBF, Support Vector Machine with Linear Function (SVM-LIN, and Multilinear Regression (MLR as candidate. Three statistical models are also selected: Autoregressive Integrated Moving Average (ARIMA, Vector Autoregression (VAR, and Space-Time (ST model. From the prediction results, we find the following meaningful results: (1 the prediction accuracy of speed deteriorates as the prediction time steps increase for all models; (2 the BPNN, NARXNN, and SVM-RBF can clearly outperform two traditional statistical models: ARIMA and VAR; (3 the prediction performance of ANN is superior to that of SVM and MLR; (4 as time step increases, the ST model can consistently provide the lowest MAE comparing with ARIMA and VAR.

  18. Short-Term Electrical Load Forecasting using Neuro-Fuzzy Models

    Energy Technology Data Exchange (ETDEWEB)

    Park, Young Jin; Shim, Hyun Jeong; Wang, Bo Hyeun [Kang Nung National University (Korea)

    2000-03-01

    This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models, The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptions, radial basis function networks, and neuro-fuzzy models without the structure learning. (author). 23 refs., 11 figs., 8 tabs.

  19. Building self-consistent, short-term earthquake probability (STEP models: improved strategies and calibration procedures

    Directory of Open Access Journals (Sweden)

    Damiano Monelli

    2010-11-01

    Full Text Available We present here two self-consistent implementations of a short-term earthquake probability (STEP model that produces daily seismicity forecasts for the area of the Italian national seismic network. Both implementations combine a time-varying and a time-invariant contribution, for which we assume that the instrumental Italian earthquake catalog provides the best information. For the time-invariant contribution, the catalog is declustered using the clustering technique of the STEP model; the smoothed seismicity model is generated from the declustered catalog. The time-varying contribution is what distinguishes the two implementations: 1 for one implementation (STEP-LG, the original model parameterization and estimation is used; 2 for the other (STEP-NG, the mean abundance method is used to estimate aftershock productivity. In the STEP-NG implementation, earthquakes with magnitude up to ML= 6.2 are expected to be less productive compared to the STEP-LG implementation, whereas larger earthquakes are expected to be more productive. We have retrospectively tested the performance of these two implementations and applied likelihood tests to evaluate their consistencies with observed earthquakes. Both of these implementations were consistent with the observed earthquake data in space: STEP-NG performed better than STEP-LG in terms of forecast rates. More generally, we found that testing earthquake forecasts issued at regular intervals does not test the full power of clustering models, and future experiments should allow for more frequent forecasts starting at the times of triggering events.

  20. Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

    Science.gov (United States)

    Mohammed, E.; Wang, S.; Yu, J.

    2017-05-01

    This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

  1. Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

    Institute of Scientific and Technical Information of China (English)

    Sheng JIN; Dian-hai WANG; Cheng XU; Dong-fang MA

    2013-01-01

    In this paper; a prediction model is developed that combines a Gaussian mixture model (GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision (TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood (ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization (EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts (PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.

  2. A prognostic model for short term adverse events in normotensive patients with pulmonary embolism.

    Science.gov (United States)

    Agterof, Mariette J; Schutgens, Roger E G; Moumli, Noureddine; Eijkemans, M J C; van der Griend, René; Tromp, Ellen A M; Biesma, Douwe H

    2011-08-01

    Risk stratification of patients with PE has gained interest in terms of the identification of patients in whom treatment on an outpatient base can be considered. Previous studies are of limited value due to their focus on adverse clinical events within several months after diagnosis of PE. We developed a prognostic model, based on easily accessible, clinical, and laboratory parameters, to predict adverse events during the first 10 days after the diagnosis of acute PE. We have analyzed the data of 210 outpatients with confirmed PE. Collected data included medical history, pulse rate, blood pressure, NT-proBNP, and D-dimer concentrations. The primary outcome was the occurrence of adverse clinical events in a 10 day follow-up period. Our final prognostic model to predict short-term adverse events consists of NT-proBNP levels, D-dimer concentrations, pulse rate, and the occurrence of active malignancy; the total score ranges from 0 to 37 points. Patients with a low score (no active malignancy, pulse rate rate, D-dimer concentrations, and NT-proBNP levels. Our prognostic model, once prospectively validated in an independent sample of patients, can be used in the early risk stratification of PE to estimate the risk of adverse events and to differentiate between candidates for in- or out- hospital treatment. Copyright © 2011 Wiley-Liss, Inc.

  3. Bayesian adaptive combination of short-term wind speed forecasts from neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Li, Gong; Shi, Jing; Zhou, Junyi [Department of Industrial and Manufacturing Engineering, North Dakota State University, Dept. 2485, PO Box 6050, Fargo, ND 58108 (United States)

    2011-01-15

    Short-term wind speed forecasting is of great importance for wind farm operations and the integration of wind energy into the power grid system. Adaptive and reliable methods and techniques of wind speed forecasts are urgently needed in view of the stochastic nature of wind resource varying from time to time and from site to site. This paper presents a robust two-step methodology for accurate wind speed forecasting based on Bayesian combination algorithm, and three neural network models, namely, adaptive linear element network (ADALINE), backpropagation (BP) network, and radial basis function (RBF) network. The hourly average wind speed data from two North Dakota sites are used to demonstrate the effectiveness of the proposed approach. The results indicate that, while the performances of the neural networks are not consistent in forecasting 1-h-ahead wind speed for the two sites or under different evaluation metrics, the Bayesian combination method can always provide adaptive, reliable and comparatively accurate forecast results. The proposed methodology provides a unified approach to tackle the challenging model selection issue in wind speed forecasting. (author)

  4. Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

    Directory of Open Access Journals (Sweden)

    Jinxing Shen

    2013-01-01

    Full Text Available In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN. The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.

  5. Capacity and precision in an animal model of visual short-term memory.

    Science.gov (United States)

    Lara, Antonio H; Wallis, Jonathan D

    2012-01-01

    Temporary storage of information in visual short-term memory (VSTM) is a key component of many complex cognitive abilities. However, it is highly limited in capacity. Understanding the neurophysiological nature of this capacity limit will require a valid animal model of VSTM. We used a multiple-item color change detection task to measure macaque monkeys' VSTM capacity. Subjects' performance deteriorated and reaction times increased as a function of the number of items in memory. Additionally, we measured the precision of the memory representations by varying the distance between sample and test colors. In trials with similar sample and test colors, subjects made more errors compared to trials with highly discriminable colors. We modeled the error distribution as a Gaussian function and used this to estimate the precision of VSTM representations. We found that as the number of items in memory increases the precision of the representations decreases dramatically. Additionally, we found that focusing attention on one of the objects increases the precision with which that object is stored and degrades the precision of the remaining. These results are in line with recent findings in human psychophysics and provide a solid foundation for understanding the neurophysiological nature of the capacity limit of VSTM.

  6. A short-term model of COPD identifies a role for mast cell tryptase

    Science.gov (United States)

    Beckett, Emma L.; Stevens, Richard L.; Jarnicki, Andrew G.; Kim, Richard Y.; Hanish, Irwan; Hansbro, Nicole G.; Deane, Andrew; Keely, Simon; Horvat, Jay C.; Yang, Ming; Oliver, Brian G.; van Rooijen, Nico; Inman, Mark D.; Adachi, Roberto; Soberman, Roy J.; Hamadi, Sahar; Wark, Peter A.; Foster, Paul S.; Hansbro, Philip M.

    2013-01-01

    Background Cigarette smoke-induced chronic obstructive pulmonary disease (COPD) is a life-threatening inflammatory disorder of the lung. The development of effective therapies for COPD has been hampered by the lack of an animal model that mimics the human disease in a short time-frame. Objectives To create an early onset mouse model of cigarette smoke-induced COPD that develops the hallmark features of the human condition in a short time-frame. To use this model to better understand pathogenesis and the roles of macrophages and mast cells (MCs) in COPD. Methods Tightly controlled amounts of cigarette smoke were delivered to the airways of mice, and the development of the pathological features of COPD was assessed. The roles of macrophages and MC tryptase in pathogenesis were evaluated using depletion and in vitro studies and MC protease-6 deficient mice. Results After just 8 weeks of smoke exposure, wild-type mice developed chronic inflammation, mucus hypersecretion, airway remodeling, emphysema, and reduced lung function. These characteristic features of COPD were glucocorticoid-resistant and did not spontaneously resolve. Systemic effects on skeletal muscle and the heart, and increased susceptibility to respiratory infections also were observed. Macrophages and tryptase-expressing MCs were required for the development of COPD. Recombinant MC tryptase induced pro-inflammatory responses from cultured macrophages. Conclusion A short-term mouse model of cigarette smoke-induced COPD was developed in which the characteristic features of the disease were induced more rapidly than existing models. The model can be used to better understand COPD pathogenesis, and we show a requirement for macrophages and tryptase-expressing MCs. PMID:23380220

  7. A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition.

    Directory of Open Access Journals (Sweden)

    Margarita Zachariou

    Full Text Available Memories are believed to be represented in the synaptic pathways of vastly interconnected networks of neurons. The plasticity of synapses, that is, their strengthening and weakening depending on neuronal activity, is believed to be the basis of learning and establishing memories. An increasing number of studies indicate that endocannabinoids have a widespread action on brain function through modulation of synaptic transmission and plasticity. Recent experimental studies have characterised the role of endocannabinoids in mediating both short- and long-term synaptic plasticity in various brain regions including the hippocampus, a brain region strongly associated with cognitive functions, such as learning and memory. Here, we present a biophysically plausible model of cannabinoid retrograde signalling at the synaptic level and investigate how this signalling mediates depolarisation induced suppression of inhibition (DSI, a prominent form of short-term synaptic depression in inhibitory transmission in hippocampus. The model successfully captures many of the key characteristics of DSI in the hippocampus, as observed experimentally, with a minimal yet sufficient mathematical description of the major signalling molecules and cascades involved. More specifically, this model serves as a framework to test hypotheses on the factors determining the variability of DSI and investigate under which conditions it can be evoked. The model reveals the frequency and duration bands in which the post-synaptic cell can be sufficiently stimulated to elicit DSI. Moreover, the model provides key insights on how the state of the inhibitory cell modulates DSI according to its firing rate and relative timing to the post-synaptic activation. Thus, it provides concrete suggestions to further investigate experimentally how DSI modulates and is modulated by neuronal activity in the brain. Importantly, this model serves as a stepping stone for future deciphering of the role of

  8. Modeling short-term variability of semivolatile organic chemicals in air at a local scale: an integrated modeling approach.

    Science.gov (United States)

    Morselli, Melissa; Ghirardello, Davide; Semplice, Matteo; Di Guardo, Antonio

    2011-05-01

    Monitoring campaigns from different locations have recently shown how air concentrations of persistent semivolatile contaminants such as polychlorinated biphenyls (PCBs) often exhibit short-term (less than 24 h) variations. The observed patterns have been ascribed to different factors, such as temperature-mediated air-surface exchange and variability of planetary boundary layer (PBL) height and dynamics. Here, we present a new modeling approach developed in order to investigate the short-term variability in air concentrations of organic pollutants at a local scale. A new dynamic multimedia box model is supplied by a meteorological preprocessor (AERMET) with hourly values of air compartment height and wind speed. The resulting model is tested against an existing dataset of PCB air concentrations measured in Zurich, Switzerland. Results show the importance of such modeling approach in elucidating the short- and long-term behavior of semivolatile contaminants in the air/soil system.

  9. Trade-off Assessment of Simplified Routing Models for Short-Term Hydropower Reservoir Optimization

    Science.gov (United States)

    Issao Kuwajima, Julio; Schwanenberg, Dirk; Alvardo Montero, Rodolfo; Mainardi Fan, Fernando; Assis dos Reis, Alberto

    2014-05-01

    Short-term reservoir optimization, also referred to as model predictive control, integrates model-based forecasts and optimization algorithms to meet multiple management objectives such as water supply, navigation, hydroelectricity generation, environmental obligations and flood protection. It is a valuable decision support tool to handle water-stress conditions or flooding events, and supports decision makers to minimize their impact. If the reservoir management includes downstream control, for example for mitigation flood damages in inundation areas downstream of the operated dam, the flow routing between the dam and the downstream inundation area is of major importance. The unsteady open channel flow in river reaches can be described by the one-dimensional Saint-Venant equations. However, owing to the mathematical complexity of those equations, some simplifications may be required to speed up the computation within the optimization procedure. Another strategy to limit the model runtime is a schematization on a course computational grid. In particular the last measure can introduce significant numerical diffusion into the solution. This is a major drawback, in particular if the reservoir release has steep gradients which we often find in hydropower reservoirs. In this work, four different routing models are assessed concerning their implementation in the predictive control of the Três Marias Reservoir located at the Upper River São Francisco in Brazil: i) a fully dynamic model using the software package SOBEK; ii) a semi-distributed rainfall-runoff model with Muskingum-Cunge routing for the flow reaches of interest, the MGB-IPH (Modelo Hidrológico de Grandes Bacias - Instituto de Pesquisas Hidráulicas); iii) a reservoir routing approach; and iv) a diffusive wave model. The last two models are implemented in the RTC-Tool toolbox. The overall model accuracy between the simplified models in RTC-Tools (iii, iv) and the more sophisticated SOBEK model (i) are

  10. Neural processing of short-term recurrence in songbird vocal communication.

    Directory of Open Access Journals (Sweden)

    Gabriël J L Beckers

    Full Text Available BACKGROUND: Many situations involving animal communication are dominated by recurring, stereotyped signals. How do receivers optimally distinguish between frequently recurring signals and novel ones? Cortical auditory systems are known to be pre-attentively sensitive to short-term delivery statistics of artificial stimuli, but it is unknown if this phenomenon extends to the level of behaviorally relevant delivery patterns, such as those used during communication. METHODOLOGY/PRINCIPAL FINDINGS: We recorded and analyzed complete auditory scenes of spontaneously communicating zebra finch (Taeniopygia guttata pairs over a week-long period, and show that they can produce tens of thousands of short-range contact calls per day. Individual calls recur at time scales (median interval 1.5 s matching those at which mammalian sensory systems are sensitive to recent stimulus history. Next, we presented to anesthetized birds sequences of frequently recurring calls interspersed with rare ones, and recorded, in parallel, action and local field potential responses in the medio-caudal auditory forebrain at 32 unique sites. Variation in call recurrence rate over natural ranges leads to widespread and significant modulation in strength of neural responses. Such modulation is highly call-specific in secondary auditory areas, but not in the main thalamo-recipient, primary auditory area. CONCLUSIONS/SIGNIFICANCE: Our results support the hypothesis that pre-attentive neural sensitivity to short-term stimulus recurrence is involved in the analysis of auditory scenes at the level of delivery patterns of meaningful sounds. This may enable birds to efficiently and automatically distinguish frequently recurring vocalizations from other events in their auditory scene.

  11. Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

    Directory of Open Access Journals (Sweden)

    D. L. Shrestha

    2013-05-01

    Full Text Available The quality of precipitation forecasts from four Numerical Weather Prediction (NWP models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT and regional (ACCESS-R NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

  12. Short term memory decays and high presentation rates hurry this decay: The Murdock free recall experiments interpreted in the Tagging/Retagging model

    OpenAIRE

    Tarnow, Dr. Eugen

    2009-01-01

    I show that the curious free recall data of Murdock (1962) can be explained by the Tagging/Retagging model of short term memory (Tarnow, 2009 and 2008) in which a short term memory item is a tagged long term memory item. The tagging (linear in time) corresponds to the synaptic process of exocytosis and the loss of tagging (logarithmic in time) corresponds to synaptic endocytosis. The Murdock recent item recall probabilities follow a logarithmic decay with time of recall. The slope of the d...

  13. Modelling the effects of (short-term solar variability on stratospheric chemistry

    Directory of Open Access Journals (Sweden)

    R. Muncaster

    2011-12-01

    Full Text Available The photochemical response of the stratosphere to short-term solar variability is investigated using a photochemistry column model with interactive photolysis calculation. The solar variability is here simply represented using the Lean (1997 solar minimum and maximum spectra. In order to isolate the photochemistry effect, simulations are devoid of diffusion or any other external forcing and the temperature is held constant. The solar mininum/maximum response is estimated for all chemical families and partitioning ratios, and the underlying photochemical mechanisms are described in detail. The ozone response peaks at 0.18 ppmv (approximatively 3% at 37 km altitude. In an attempt to find the simplest statistical model able to represent the effect of solar variability in the stratosphere, the diurnal-average response of ozone from an ensemble of 200 simulations is regressed linearly following two auto-regressive models. In the simplest case, an adjusted coefficient of determination R2 larger than 0.97 is found throughout the stratosphere using two predictors, namely the previous day's ozone perturbation and the current day's solar irradiance perturbation. A better accuracy (R2 larger than 0.9992 is achieved with an additional predictor, the previous day's solar irradiance perturbation. The skills of the two auto-regressive models at representing the effect of solar variability are then evaluated independently when coupled either on-line or off-line with the comprehensive photochemistry column model driven by the solar average spectrum. In all cases, the magnitude of the bias and the RMS error are found smaller than 5% and 20% of the ozone response, respectively. When used on-line, the 3-predictor model captures the ozone response to solar variability throughout the stratosphere with bias and RMS error

  14. Analysts forecast error : A robust prediction model and its short term trading

    NARCIS (Netherlands)

    Boudt, Kris; de Goeij, Peter; Thewissen, James; Van Campenhout, Geert

    2015-01-01

    We examine the profitability of implementing a short term trading strategy based on predicting the error in analysts' earnings per share forecasts using publicly available information. Since large earnings surprises may lead to extreme values in the forecast error series that disrupt their smooth au

  15. Short Term Intervention Model for Enhancing Divergent Thinking among School Aged Children

    Science.gov (United States)

    Doron, Eyal

    2016-01-01

    Creative ability can be developed and improved through intervention and training. This study presents a unique and innovative intervention program for enhancing creative thinking among children, focusing on divergent thinking skills. The program was designed as a short-term (10 weeks) training and conducted with 150 school students ranging in age…

  16. Enhanced Neuroactivation during Verbal Memory Processing in Postmenopausal Women Receiving Short Term Hormone Therapy

    Science.gov (United States)

    Persad, Carol C.; Zubieta, Jon-Kar; Love, Tiffany; Wang, Heng; Tkaczyk, Anne; Smith, Yolanda R.

    2012-01-01

    Capsule Using a randomized, double-blind placebo-controlled cross-over design, we showed that short-term hormone replacement therapy increases brain activation in parietal and prefrontal areas during verbal memory tasks in postmenopausal women. Objective To study the effects of hormone therapy on brain activation patterns during verbal memory in postmenopausal women. Design A randomized, double-blind placebo-controlled cross-over study was performed. Setting A tertiary care university medical center. Participants Ten healthy postmenopausal women (age range 50-60 years) were recruited from the local community. Interventions Women were randomized to the order they received combined hormone therapy, 5 ug ethinyl estradiol and 1 mg norethindrone acetate, and placebo. Volunteers received hormone therapy or placebo for 4 weeks, followed by a one month washout period, and then received the other treatment for 4 weeks. An fMRI was performed at the end of each 4 week treatment utilizing a verbal memory task. Main Outcome Measure Brain activation patterns were compared between hormone therapy and placebo. Results Hormone therapy was associated with increased activation in left middle/superior frontal cortex (BA 6,9), medial frontal cortex and dorsal anterior cingulate (BA 24,32), posterior cingulate (BA 6), and left inferior parietal (BA 40) during memory encoding. All regions were significant at p ≤ 0.05 with correction for multiple comparisons. Conclusions Hormone therapy increased neural activation in frontal and parietal areas in postmenopausal women during a verbal memory task. PMID:18692790

  17. Comfrey (Symphytum officinale. L. and Experimental Hepatic Carcinogenesis: A Short-Term Carcinogenesis Model Study

    Directory of Open Access Journals (Sweden)

    Maria Fernanda Pereira Lavieri Gomes

    2010-01-01

    Full Text Available Comfrey or Symphytum officinale (L. (Boraginaceae is a very popular plant used for therapeutic purposes. Since the 1980s, its effects have been studied in long-term carcinogenesis studies, in which Comfrey extract is administered at high doses during several months and the neoplastic hepatic lesions are evaluated. However, the literature on this topic is very poor considering the studies performed under short-term carcinogenesis protocols, such as the ‘resistant hepatocyte model’ (RHM. In these studies, it is possible to observe easily the phenomena related to the early phases of tumor development, since pre-neoplastic lesions (PNLs rise in about 1–2 months of chemical induction. Herein, the effects of chronic oral treatment of rats with 10% Comfrey ethanolic extract were evaluated in a RHM. Wistar rats were sequentially treated with N-nitrosodiethylamine (ip and 2-acetilaminofluorene (po, and submitted to hepatectomy to induce carcinogenesis promotion. Macroscopic/microscopic quantitative analysis of PNL was performed. Non-parametric statistical tests (Mann–Whitney and χ2 were used, and the level of significance was set at P ≤ 0.05. Comfrey treatment reduced the number of pre-neoplastic macroscopic lesions up to 1 mm (P ≤ 0.05, the percentage of oval cells (P = 0.0001 and mitotic figures (P = 0.007, as well as the number of Proliferating Cell Nuclear Antigen (PCNA positive cells (P = 0.0001 and acidophilic pre-neoplastic nodules (P = 0.05. On the other hand, the percentage of cells presenting megalocytosis (P = 0.0001 and vacuolar degeneration (P = 0.0001 was increased. Scores of fibrosis, glycogen stores and the number of nucleolus organizing regions were not altered. The study indicated that oral treatment of rats with 10% Comfrey alcoholic extract reduced cell proliferation in this model.

  18. Comprehensive optimized GM(1,1) model and application for short term forecasting of Chinese energy consumption and production

    Institute of Scientific and Technical Information of China (English)

    Ning Xu; Yaoguo Dang; Jie Cui

    2015-01-01

    In order to improve prediction accuracy of the grey prediction model and forecast China energy consumption and production in a short term, this paper proposes a novel com-prehensively optimized GM(1,1) model, also named COGM(1,1), based on the grey modeling mechanism. First, the relationship of the background value formula and its whitenization equation is analyzed and a new method optimizing background values is proposed to eliminate systemic errors in the modeling process. Second, the solving process of the new model is derived. For parameter estimation, a set of auxiliary parameters are used to change grey equation’s form. Then, original parameters are re-stored by an equations system. After solving the whitenization equation, initial value in time response function is established by least errors criteria. Final y, a numerical case and comparison with other grey prediction models are made to testify the new model’s effectiveness, and the computational results show that the COGM(1,1) model has a better property and achieves higher precision. The new model is used to forecast China energy con-sumption and production, and the ability of energy self-sufficiency is further analyzed. Results indicate that gaps between consump-tion and production in future are predicted to decline.

  19. Impact of Participating in a Short-Term Intervention Model of Sports Education Camps for Children with Visual Impairments

    Science.gov (United States)

    Mc Mahon, John M.

    2013-01-01

    This three-paper format dissertation explores three topics relevant to participating in a short-term model Sports Education Camp for youth with vision impairments. The three papers are independent studies, yet build upon each other by first measuring physical performance in certain skills, then exploring their levels of self-perception, body mass…

  20. Evaluation of the E mu-pim-1 transgenic mouse model for short-term carcinogenicity testing

    DEFF Research Database (Denmark)

    van Kreijl, C. F.; van Oordt, C. W. V.; Kroese, E. D.;

    1998-01-01

    . In the present article, the results from 2 recent short-term assays (with mitomycin C and x-rays) are briefly presented, together with a review of all 11 performed bioassays and their corresponding histopathologic and molecular data. The overall results allow the first evaluation of the E mu-pim-1 mouse model...

  1. Accident tolerant clad material modeling by MELCOR: Benchmark for SURRY Short Term Station Black Out

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jun, E-mail: jwang564@wisc.edu [College of Engineering, The University of Wisconsin-Madison, Madison 53706 (United States); Mccabe, Mckinleigh [College of Engineering, The University of Wisconsin-Madison, Madison 53706 (United States); Wu, Lei [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China); Dong, Xiaomeng [College of Engineering, The University of Wisconsin-Madison, Madison 53706 (United States); Wang, Xianmao [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China); Haskin, Troy Christopher [College of Engineering, The University of Wisconsin-Madison, Madison 53706 (United States); Corradini, Michael L., E-mail: corradini@engr.wisc.edu [College of Engineering, The University of Wisconsin-Madison, Madison 53706 (United States)

    2017-03-15

    Highlights: • Thermo-physical and oxidation kinetics properties calculation and analysis of FeCrAl. • Properties modelling of FeCrAl in MELCOR. • Benchmark calculation of Surry nuclear power plant. - Abstract: Accident tolerant fuel and cladding materials are being investigated to provide a greater resistance to fuel degradation, oxidation and melting if long-term cooling is lost in a Light Water Reactor (LWR) following an accident such as a Station Blackout (SBO) or Loss of Coolant Accident (LOCA). Researchers at UW-Madison are analyzing an SBO sequence and examining the effect of a loss of auxiliary feedwater (AFW) with the MELCOR systems code. Our research work considers accident tolerant cladding materials (e.g., FeCrAl alloy) and their effect on the accident behavior. We first gathered the physical properties of this alternative cladding material via literature review and compared it to the usual zirconium alloys used in LWRs. We then developed a model for the Surry reactor for a Short-term SBO sequence and examined the effect of replacing FeCrAl for Zircaloy cladding. The analysis uses MELCOR, Version 1.8.6 YR, which is developed by Idaho National Laboratory in collaboration with MELCOR developers at Sandia National Laboratories. This version allows the user to alter the cladding material considered, and our study examines the behavior of the FeCrAl alloy as a substitute for Zircaloy. Our benchmark comparisons with the Sandia National Laboratory’s analysis of Surry using MELCOR 1.8.6 and the more recent MELCOR 2.1 indicate good overall agreement through the early phases of the accident progression. When FeCrAl is substituted for Zircaloy to examine its performance, we confirmed that FeCrAl slows the accident progression and reduce the amount of hydrogen generated. Our analyses also show that this special version of MELCOR can be used to evaluate other potential ATF cladding materials, e.g., SiC as well as innovative coatings on zirconium cladding

  2. Comparative Study of the Effects of Long and Short Term Biological Processes on the Cycling of Colloidal Trace Metals

    Science.gov (United States)

    Pinedo, P.; Sanudo-Wilhelmy, S. A.; West, A.

    2013-05-01

    Nanoparticle (or colloids), with sizes operationally defined as ranging from 1nm to 1000nm diameter, are thought to play an important role in metal cycling in the ocean due to their high surface area to volume ratio and abundance in marine systems. In coastal waters, the bulk of marine nanoparticles are organic, so short and long term biological processes are expected to influence the dynamics of these types of particles in marine environments. This is, in turn, expected to influence metal concentrations. Here we selected two different environments to study the influence of long-term biological events (phytoplankton blooms) and short-term biological events (diel cycles of photosynthesis and respiration) on the cycling of colloidal trace metals. We focus on Cu and Fe, both biogeochemically important metals but with differing colloidal behavior. Long term processes (West Neck Bay): A bay (West Neck Bay, Long Island) with predictable natural phytoplankton blooms, but with limited inputs of freshwater, nutrients and metals, was selected to study the partitioning of Cu and Fe between colloidal and soluble pools over the course of a bloom. During the bloom, there was a significant build-up of Cu associated with DOM accumulation and a removal of Fe via particle stripping. Fraction-specific metal concentrations, and metal accumulation and removal rates, were found to be significantly correlated with chlorophyll-a concentration and with dissolved organic matter (DOM). Short term processes (Catalina Island): To identify the cyclical variation in metal speciation during diel (24-hour) cycles of photosynthesis and respiration, we conducted a study off Catalina Island, a pristine environment where trace metal cycling is solely controlled by biological processes and changes in the phytoplankton community are well characterized. The speciation of Fe between soluble and colloidal pools showed that Fe has a high affinity for colloidal material and that the distribution between

  3. A fast network solution by the decoupled procedure during short-term dynamic processes in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Popovic, D.P.; Stefanovic, M.D. (Nikola Tesla Inst., Belgrade (YU). Power System Dept.)

    1990-01-01

    A simple, fast and reliable decoupled procedure for solving the network problems during short-term dynamic processes in power systems is presented. It is based on the Newton-Raphson method applied to the power balance equations, which include the effects of generator saliency and non-impedance loads, with further modifications resulting from the physical properties of the phenomena under study. The good convergence characteristics of the developed procedure are demonstrated, and a comparison is made with the traditional method based on the current equation and the triangularized admittance matrix, using the example of stability analysis of the Yugoslav power grid. (author).

  4. Model predictions of features in microsaccade-related neural responses in a feedforward network with short-term synaptic depression.

    Science.gov (United States)

    Zhou, Jian-Fang; Yuan, Wu-Jie; Zhou, Zhao; Zhou, Changsong

    2016-02-08

    Recently, the significant microsaccade-induced neural responses have been extensively observed in experiments. To explore the underlying mechanisms of the observed neural responses, a feedforward network model with short-term synaptic depression has been proposed [Yuan, W.-J., Dimigen, O., Sommer, W. and Zhou, C. Front. Comput. Neurosci. 7, 47 (2013)]. The depression model not only gave an explanation for microsaccades in counteracting visual fading, but also successfully reproduced several microsaccade-related features in experimental findings. These results strongly suggest that, the depression model is very useful to investigate microsaccade-related neural responses. In this paper, by using the model, we extensively study and predict the dependance of microsaccade-related neural responses on several key parameters, which could be tuned in experiments. Particularly, we provide a significant prediction that microsaccade-related neural response also complies with the property "sharper is better" observed in many contexts in neuroscience. Importantly, the property exhibits a power-law relationship between the width of input signal and the responsive effectiveness, which is robust against many parameters in the model. By using mean field theory, we analytically investigate the robust power-law property. Our predictions would give theoretical guidance for further experimental investigations of the functional role of microsaccades in visual information processing.

  5. Short-term earthquake forecasting based on an epidemic clustering model

    Science.gov (United States)

    Console, Rodolfo; Murru, Maura; Falcone, Giuseppe

    2016-04-01

    . The implementation of this step could be problematic for seismicity characterized by long-term recurrence. However, the separation of the data base of the data base collected in the past in two separate sections (one on which the best fit of the parameters is carried out, and the other on which the hypothesis is tested) can be a viable solution, known as retrospective-forward testing. In this study we show examples of application of the above mentioned concepts to the analysis of the Italian catalog of instrumental seismicity, making use of an epidemic algorithm developed to model short-term clustering features. This model, for which a precursory anomaly is just the occurrence of seismic activity, doesn't need the retrospective categorization of earthquakes in terms of foreshocks, mainshocks and aftershocks. It was introduced more than 15 years ago and tested so far in a number of real cases. It is now being run by several seismological centers around the world in forward real-time mode for testing purposes.

  6. Effects of acute subcutaneous nicotine on attention, information processing and short-term memory in Alzheimer's disease.

    Science.gov (United States)

    Jones, G M; Sahakian, B J; Levy, R; Warburton, D M; Gray, J A

    1992-01-01

    This single-blind, placebo controlled study reports on the effects of administering three acute doses of nicotine (0.4, 0.6 and 0.8 mg) subcutaneously to a group of Alzheimer's disease (DAT) patients (n = 22), young adult controls (n = 24), and normal aged controls (n = 24). The study extends our previous findings obtained using smaller groups of subjects. Drug effects were examined on three computerised tests: the first measuring rapid visual information processing, sustained visual attention and reaction time (RVIP task); a delayed response matching to location-order task measuring sustained visual attention and visual short-term memory (DRMLO task); and a finger tapping test measuring simple reaction time (FT task). The critical flicker fusion test (CFF) was used as a measure of perception and the WAIS digit span forwards (DS), of auditory short-term memory. Tests were graded in difficulty, titrated to avoid floor and ceiling effects so that meaningful, direct comparisons between groups could be made. Nicotine significantly improved sustained visual attention (in both RVIP and DRMLO tasks), reaction time (in both FT and RVIP tasks), and perception (CFF task--both ascending and descending thresholds). Nicotine administration did not improve auditory and visual short-term memory. There were no consistent, overall patterns of difference in performance between smokers and non-smokers in the control groups, or between males and females in any group. Despite the absence of change in memory functioning, these results demonstrate that DAT patients have significant perceptual and visual attentional deficits which are improved by nicotine administration.(ABSTRACT TRUNCATED AT 250 WORDS)

  7. Short-Term Memory and Aphasia: From Theory to Treatment.

    Science.gov (United States)

    Minkina, Irene; Rosenberg, Samantha; Kalinyak-Fliszar, Michelene; Martin, Nadine

    2017-02-01

    This article reviews existing research on the interactions between verbal short-term memory and language processing impairments in aphasia. Theoretical models of short-term memory are reviewed, starting with a model assuming a separation between short-term memory and language, and progressing to models that view verbal short-term memory as a cognitive requirement of language processing. The review highlights a verbal short-term memory model derived from an interactive activation model of word retrieval. This model holds that verbal short-term memory encompasses the temporary activation of linguistic knowledge (e.g., semantic, lexical, and phonological features) during language production and comprehension tasks. Empirical evidence supporting this model, which views short-term memory in the context of the processes it subserves, is outlined. Studies that use a classic measure of verbal short-term memory (i.e., number of words/digits correctly recalled in immediate serial recall) as well as those that use more intricate measures (e.g., serial position effects in immediate serial recall) are discussed. Treatment research that uses verbal short-term memory tasks in an attempt to improve language processing is then summarized, with a particular focus on word retrieval. A discussion of the limitations of current research and possible future directions concludes the review. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

  8. Downscaling of Short-Term Precipitation from Regional Climate Models for Sustainable Urban Planning

    Directory of Open Access Journals (Sweden)

    Holger Hoppe

    2012-05-01

    Full Text Available A framework for downscaling precipitation from RCM projections to the high resolutions in time and space required in the urban hydrological climate change impact assessment is outlined and demonstrated. The basic approach is that of Delta Change, developed for both continuous and event-based applications. In both cases, Delta Change Factors (DCFs are calculated which represent the expected future change of some key precipitation statistics. In the continuous case, short-term precipitation from climate projections are analysed in order to estimate DCFs associated with different percentiles in the frequency distribution of non-zero intensities. The DCFs may then be applied to an observed time series, producing a realisation of a future time series. The event-based case involves downscaling of Intensity-Duration-Frequency (IDF curves based on extreme value analysis of annual maxima using the Gumbel distribution. The resulting DCFs are expressed as a function of duration and frequency (i.e., return period and may be used to estimate future design storms. The applications are demonstrated in case studies focusing on the expected changes in short-term precipitation statistics until 2100 in the cities of Linz (Austria and Wuppertal (Germany. The downscaling framework is implemented in the climate service developed within the EU-project SUDPLAN.

  9. Interacting adaptive processes with different timescales underlie short-term motor learning.

    Directory of Open Access Journals (Sweden)

    Maurice A Smith

    2006-06-01

    Full Text Available Multiple processes may contribute to motor skill acquisition, but it is thought that many of these processes require sleep or the passage of long periods of time ranging from several hours to many days or weeks. Here we demonstrate that within a timescale of minutes, two distinct fast-acting processes drive motor adaptation. One process responds weakly to error but retains information well, whereas the other responds strongly but has poor retention. This two-state learning system makes the surprising prediction of spontaneous recovery (or adaptation rebound if error feedback is clamped at zero following an adaptation-extinction training episode. We used a novel paradigm to experimentally confirm this prediction in human motor learning of reaching, and we show that the interaction between the learning processes in this simple two-state system provides a unifying explanation for several different, apparently unrelated, phenomena in motor adaptation including savings, anterograde interference, spontaneous recovery, and rapid unlearning. Our results suggest that motor adaptation depends on at least two distinct neural systems that have different sensitivity to error and retain information at different rates.

  10. Complex Network Structure Influences Processing in Long-Term and Short-Term Memory

    Science.gov (United States)

    Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven

    2012-01-01

    Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological…

  11. Complex Network Structure Influences Processing in Long-Term and Short-Term Memory

    Science.gov (United States)

    Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven

    2012-01-01

    Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological…

  12. Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

    Science.gov (United States)

    Mohammed, E.; Wang, S.; Yu, J.

    2017-05-01

    Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

  13. Modeling circadian and sleep-homeostatic effects on short-term interval timing

    Science.gov (United States)

    Späti, Jakub; Aritake, Sayaka; Meyer, Andrea H.; Kitamura, Shingo; Hida, Akiko; Higuchi, Shigekazu; Moriguchi, Yoshiya; Mishima, Kazuo

    2015-01-01

    Short-term interval timing i.e., perception and action relating to durations in the seconds range, has been suggested to display time-of-day as well as wake dependent fluctuations due to circadian and sleep-homeostatic changes to the rate at which an underlying pacemaker emits pulses; pertinent human data being relatively sparse and lacking in consistency however, the phenomenon remains elusive and its mechanism poorly understood. To better characterize the putative circadian and sleep-homeostatic effects on interval timing and to assess the ability of a pacemaker-based mechanism to account for the data, we measured timing performance in eighteen young healthy male subjects across two epochs of sustained wakefulness of 38.67 h each, conducted prior to (under entrained conditions) and following (under free-running conditions) a 28 h sleep-wake schedule, using the methods of duration estimation and duration production on target intervals of 10 and 40 s. Our findings of opposing oscillatory time courses across both epochs of sustained wakefulness that combine with increasing and, respectively, decreasing, saturating exponential change for the tasks of estimation and production are consistent with the hypothesis that a pacemaker emitting pulses at a rate controlled by the circadian oscillator and increasing with time awake determines human short-term interval timing; the duration-specificity of this pattern is interpreted as reflecting challenges to maintaining stable attention to the task that progressively increase with stimulus magnitude and thereby moderate the effects of pacemaker-rate changes on overt behavior. PMID:25741253

  14. On Spatiotemporal Series Analysis and Its Application to Predict the Regional Short Term Climate Process

    Institute of Scientific and Technical Information of China (English)

    王革丽; 杨培才; 吕达仁

    2004-01-01

    Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.

  15. Genetic algorithm for short-term scheduling of make-and-pack batch production process

    Institute of Scientific and Technical Information of China (English)

    Wuthichai Wongthatsanekorn; Busaba Phruksaphanrat

    2015-01-01

    This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy al constraints while meeting demand requirement of packed products from various product fam-ilies. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore, we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromo-somes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to de-termine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for com-parison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, al heuristics show the capability to solve large instances within reason-able computational time. In al problem instances, genetic algorithm averagely outperforms ant colony optimiza-tion and Tabu search with slightly longer computational time.

  16. Dynamic Updating Process of Readers' Temporal Situation Model: From Short-term Working Memory to Long-term Working Memory%时间情景模型的动态更新:从短时工作记忆到长时工作记忆

    Institute of Scientific and Technical Information of China (English)

    何先友; 杨惠兰; 张维; 赵雪汝; 谢毅

    2015-01-01

    The situation model is a hot topic in current narrative comprehension research. The Event-indexing model proposed by Zwaan, Langson, and Graesser (1995) suggests that readers establish a mental representation of events by tracking them through five dimensions: time, space, characters, causality, and protagonist/object. A large number of previous studies have shown that the temporal dimension plays an important role in constructing the situation model. The Scenario Account (Anderson, 1983) argues that scene provides clues for temporal shifts, but the Strong Iconicity Assumption (Zwaan, 1996) argues that readers update the situation model as soon as temporal shifts appear. In this study, we designed two experiments to resolve the disagreement between the Scenario Account and the Strong Iconicity Assumption. We assume that the Scenario Model and the Strong Iconicity Assumption do not contradict each other due to how the updating of a situation model has a variable processing mode in different stages of memory processing. We designed two experiments to test this hypothesis: Experiment 1 examined the effects of temporal shifts on the updating of the situation model in short-term working memory, and Experiment 2 examined this effect in long-term working memory.In this study, a moving-window technique was used to explore the extent to which temporal shifts (long/short) affect updating of readers' situation model. Experiment 1a and 1b examined whether long temporal shifts or short temporal shifts affected updating of readers' situation model in short-term working memory. A single factor within-subjects design (time shift of a moment after or a day later) was used. We predicted the long temporal shifts (Experiment 1a) would not result in the updating of readers' situation model due to the time limitation and difficulties of processing in short-term memory, but that short temporal shifts (Experiment 1b) would. Experiment 2 further examined the extent to which long temporal

  17. Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

    Directory of Open Access Journals (Sweden)

    Stepčenko Artūrs

    2016-12-01

    Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.

  18. Short term preservation of hide using vacuum: influence on properties of hide and of processed leather.

    Science.gov (United States)

    Gudro, Ilze; Valeika, Virgilijus; Sirvaitytė, Justa

    2014-01-01

    The objective of this work was to investigate vacuum influence on hide preservation time and how it affects hide structure. It was established that vacuum prolongs the storage time without hide tissue putrefaction up to 21 days when the storage temperature is 4°C. The microorganisms act for all storage times, but the action is weak and has no observable influence on the quality of hide during the time period mentioned. The hide shrinkage temperature decrease is negligible, which shows that breaking of intermolecular bonds does not occur. Optical microscopy, infrared spectroscopy and differential scanning calorimetry also did not show any structural changes which can influence the quality of leather produced from such hide. The qualitative indexes of wet blue processed under laboratory conditions and of leather produced during industrial trials are presented. Indexes such as chromium compounds exhaustion, content of chromium in leather, content of soluble matter in dichloromethane, strength properties, and shrinkage temperature were determined. Properties of the leather produced from vacuumed hide under industrial conditions conformed to the requirements of shoe upper leather.

  19. Short-term post-wildfire dry-ravel processes in a chaparral fluvial system

    Science.gov (United States)

    Florsheim, Joan L.; Chin, Anne; O'Hirok, Linda S.; Storesund, Rune

    2016-01-01

    Dry ravel, the transport of sediment by gravity, transfers material from steep hillslopes to valley bottoms during dry conditions. Following wildfire, dry ravel greatly increases in the absence of vegetation on hillslopes, thereby contributing to sediment supply at the landscape scale. Dry ravel has been documented as a dominant hillslope erosion mechanism following wildfire in chaparral environments in southern California. However, alteration after initial deposition is not well understood, making prediction of post-fire flood hazards challenging. The majority of Big Sycamore Canyon burned during the May 2013 Springs Fire leaving ash and a charred layer that covered hillslopes and ephemeral channels. Dry-ravel processes following the fire produced numerous deposits in the hillslope-channel transition zone. Field data focus on: 1) deposition from an initial post-wildfire dry-ravel pulse; and 2) subsequent alteration of dry ravel deposits over a seven-month period between September 2013 and April 2014. We quantify geomorphic responses in dry ravel deposits including responses during the one small winter storm that generated runoff following the fire. Field measurements document volumetric changes after initial post-wildfire deposition of sediment derived from dry ravel. Erosion and deposition mechanisms that occurred within dry-ravel deposits situated in the hillslope-channel transition zone included: 1) mobilization and transport of a portion or the entire deposit by fluvial erosion; 2) rilling on the surface of the unconsolidated deposits; 3) deposition on deposits via continued hillslope sediment supply; and 4) mass wasting that transfers sediment within deposits where surface profiles are near the angle of repose. Terrestrial LiDAR scanning point clouds were analyzed to generate profiles quantifying depth of sediment erosion or deposition over remaining dry ravel deposits after the first storm season. This study contributes to the understanding of potential

  20. Long-term versus short-term warming effects on microbial processes

    Science.gov (United States)

    Walker, Tom; Leblans, Niki; Sigurdsson, Bjarni D.; Richter, Andreas

    2016-04-01

    Rapid warming in high latitude ecosystems is predicted to drive massive losses of carbon dioxide (CO2) from soils to the atmosphere, raising concerns that it will create a positive feedback to climate change. However, such predictions expect that temperature effects on soil microbes, as chief producers of CO2, will persist over time scales meaningful to the climate system (i.e. decades to centuries). There is increasing awareness that the soil microbial community can acclimate to temperature change over time scales from months to years, resulting in attenuating responses of CO2 release to the atmosphere. Despite this, nothing is currently known about long-term warming effects on the activity or physiology of high latitude soil microbes, and, through this, the longevity of CO2 losses from these ecosystems. We conducted a study at a unique research site that makes use of natural (geothermal) gradients in soil temperature that have been in place for over 35 years as a natural warming treatment. We determined long-term warming effects (+0.5 °C, +1.5 °C, +3 °C and +6 °C) on soil CO2 release through microbial respiration in a laboratory incubation experiment, and explored microbial carbon use efficiency and soil carbon and nitrogen pools as mechanisms. We also performed a companion experiment to compare long-term warming effects on microbial processes to those caused by six weeks of warming of ambient soil to +3 °C and +6 °C. We show that while six weeks of warming consistently increased microbial respiration by up to 30%, this effect did not persist in soils exposed to 35 years of warming. We present further data linking such long-term thermal acclimation to shifts in microbial carbon use efficiency and soil carbon and nitrogen availability, and discuss our findings in the context of warming-driven feedbacks from high latitude soils to future climate change.

  1. Short-Term Molecular Acclimation Processes of Legume Nodules to Increased External Oxygen Concentration

    Science.gov (United States)

    Avenhaus, Ulrike; Cabeza, Ricardo A.; Liese, Rebecca; Lingner, Annika; Dittert, Klaus; Salinas-Riester, Gabriela; Pommerenke, Claudia; Schulze, Joachim

    2016-01-01

    Nitrogenase is an oxygen labile enzyme. Microaerobic conditions within the infected zone of nodules are maintained primarily by an oxygen diffusion barrier (ODB) located in the nodule cortex. Flexibility of the ODB is important for the acclimation processes of nodules in response to changes in external oxygen concentration. The hypothesis of the present study was that there are additional molecular mechanisms involved. Nodule activity of Medicago truncatula plants were continuously monitored during a change from 21 to 25 or 30% oxygen around root nodules by measuring nodule H2 evolution. Within about 2 min of the increase in oxygen concentration, a steep decline in nitrogenase activity occurred. A quick recovery commenced about 8 min later. A qPCR-based analysis of the expression of genes for nitrogenase components showed a tendency toward upregulation during the recovery. The recovery resulted in a new constant activity after about 30 min, corresponding to approximately 90% of the pre-treatment level. An RNAseq-based comparative transcriptome profiling of nodules at that point in time revealed that genes for nodule-specific cysteine-rich (NCR) peptides, defensins, leghaemoglobin and chalcone and stilbene synthase were significantly upregulated when considered as a gene family. A gene for a nicotianamine synthase-like protein (Medtr1g084050) showed a strong increase in count number. The gene appears to be of importance for nodule functioning, as evidenced by its consistently high expression in nodules and a strong reaction to various environmental cues that influence nodule activity. A Tnt1-mutant that carries an insert in the coding sequence (cds) of that gene showed reduced nitrogen fixation and less efficient acclimation to an increased external oxygen concentration. It was concluded that sudden increases in oxygen concentration around nodules destroy nitrogenase, which is quickly counteracted by an increased neoformation of the enzyme. This reaction might be

  2. Short-term treatment with flumazenil restores long-term object memory in a mouse model of Down syndrome.

    Science.gov (United States)

    Colas, Damien; Chuluun, Bayarsaikhan; Garner, Craig C; Heller, H Craig

    2017-04-01

    Down syndrome (DS) is a common genetic cause of intellectual disability yet no pro-cognitive drug therapies are approved for human use. Mechanistic studies in a mouse model of DS (Ts65Dn mice) demonstrate that impaired cognitive function is due to excessive neuronal inhibitory tone. These deficits are normalized by chronic, short-term low doses of GABAA receptor (GABAAR) antagonists in adult animals, but none of the compounds investigated are approved for human use. We explored the therapeutic potential of flumazenil (FLUM), a GABAAR antagonist working at the benzodiazepine binding site that has FDA approval. Long-term memory was assessed by the Novel Object Recognition (NOR) testing in Ts65Dn mice after acute or short-term chronic treatment with FLUM. Short-term, low, chronic dose regimens of FLUM elicit long-lasting (>1week) normalization of cognitive function in both young and aged mice. FLUM at low dosages produces long lasting cognitive improvements and has the potential of fulfilling an unmet therapeutic need in DS. Copyright © 2017. Published by Elsevier Inc.

  3. A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates

    Science.gov (United States)

    Stojković, Milan; Kostić, Srđan; Plavšić, Jasna; Prohaska, Stevan

    2017-01-01

    The authors present a detailed procedure for modelling of mean monthly flow time-series using records of the Great Morava River (Serbia). The proposed procedure overcomes a major challenge of other available methods by disaggregating the time series in order to capture the main properties of the hydrologic process in both long-run and short-run. The main assumption of the conducted research is that a time series of monthly flow rates represents a stochastic process comprised of deterministic, stochastic and random components, the former of which can be further decomposed into a composite trend and two periodic components (short-term or seasonal periodicity and long-term or multi-annual periodicity). In the present paper, the deterministic component of a monthly flow time-series is assessed by spectral analysis, whereas its stochastic component is modelled using cross-correlation transfer functions, artificial neural networks and polynomial regression. The results suggest that the deterministic component can be expressed solely as a function of time, whereas the stochastic component changes as a nonlinear function of climatic factors (rainfall and temperature). For the calibration period, the results of the analysis infers a lower value of Kling-Gupta Efficiency in the case of transfer functions (0.736), whereas artificial neural networks and polynomial regression suggest a significantly better match between the observed and simulated values, 0.841 and 0.891, respectively. It seems that transfer functions fail to capture high monthly flow rates, whereas the model based on polynomial regression reproduces high monthly flows much better because it is able to successfully capture a highly nonlinear relationship between the inputs and the output. The proposed methodology that uses a combination of artificial neural networks, spectral analysis and polynomial regression for deterministic and stochastic components can be applied to forecast monthly or seasonal flow rates.

  4. Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction

    Institute of Scientific and Technical Information of China (English)

    WENG Jian-cheng; HU Zhong-wei; YU Quan; REN Fu-tian

    2007-01-01

    A K-nearest neighbor (K-NN) based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series,collected from the floating car data (FCD) system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.

  5. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

    Science.gov (United States)

    Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

    2016-01-01

    Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

  6. The role of short-term memory and visuo-spatial skills in numerical magnitude processing: Evidence from Turner syndrome.

    Science.gov (United States)

    Attout, Lucie; Noël, Marie-Pascale; Nassogne, Marie-Cécile; Rousselle, Laurence

    2017-01-01

    Most studies on magnitude representation have focused on the visual modality with no possibility of disentangling the influence of visuo-spatial skills and short-term memory (STM) abilities on quantification processes. This study examines this issue in patients with Turner syndrome (TS), a genetic condition characterized by a specific cognitive profile frequently associating poor mathematical achievement, low spatial skills and reduced STM abilities. In order to identify the influence of visuo-spatial and STM processing on numerical magnitude abilities, twenty female participants with TS and twenty control female participants matched for verbal IQ and education level were administered a series of magnitude comparison tasks. The tasks differed on the nature of the magnitude to be processed (continuous, discrete and symbolic magnitude), on visuo-spatial processing requirement (no/high) and on STM demands (low in simultaneous presentation vs. high in sequential presentation). Our results showed a lower acuity when participants with TS compared the numerical magnitudes of stimuli presented sequentially (low visuo-spatial processing and high STM load: Dot sequence and Sound sequence) while no difference was observed in the numerical comparison of sets presented simultaneously. In addition, the group difference in sequential tasks disappeared when controlling for STM abilities. Finally, both groups demonstrated similar performance when comparing continuous or symbolic magnitude stimuli and they exhibited comparable subitizing abilities. These results highlight the importance of STM abilities in extracting numerosity through a sequential presentation and underline the importance of considering the impact of format presentation on magnitude judgments.

  7. Get the gist? The effects of processing depth on false recognition in short-term and long-term memory.

    Science.gov (United States)

    Flegal, Kristin E; Reuter-Lorenz, Patricia A

    2014-07-01

    Gist-based processing has been proposed to account for robust false memories in the converging-associates task. The deep-encoding processes known to enhance verbatim memory also strengthen gist memory and increase distortions of long-term memory (LTM). Recent research has demonstrated that compelling false memory illusions are relatively delay-invariant, also occurring under canonical short-term memory (STM) conditions. To investigate the contributions of gist to false memory at short and long delays, processing depth was manipulated as participants encoded lists of four semantically related words and were probed immediately, following a filled 3- to 4-s retention interval, or approximately 20 min later, in a surprise recognition test. In two experiments, the encoding manipulation dissociated STM and LTM on the frequency, but not the phenomenology, of false memory. Deep encoding at STM increases false recognition rates at LTM, but confidence ratings and remember/know judgments are similar across delays and do not differ as a function of processing depth. These results suggest that some shared and some unique processes underlie false memory illusions at short and long delays.

  8. The role of short-term memory and visuo-spatial skills in numerical magnitude processing: Evidence from Turner syndrome

    Science.gov (United States)

    Noël, Marie-Pascale; Nassogne, Marie-Cécile; Rousselle, Laurence

    2017-01-01

    Most studies on magnitude representation have focused on the visual modality with no possibility of disentangling the influence of visuo-spatial skills and short-term memory (STM) abilities on quantification processes. This study examines this issue in patients with Turner syndrome (TS), a genetic condition characterized by a specific cognitive profile frequently associating poor mathematical achievement, low spatial skills and reduced STM abilities. In order to identify the influence of visuo-spatial and STM processing on numerical magnitude abilities, twenty female participants with TS and twenty control female participants matched for verbal IQ and education level were administered a series of magnitude comparison tasks. The tasks differed on the nature of the magnitude to be processed (continuous, discrete and symbolic magnitude), on visuo-spatial processing requirement (no/high) and on STM demands (low in simultaneous presentation vs. high in sequential presentation). Our results showed a lower acuity when participants with TS compared the numerical magnitudes of stimuli presented sequentially (low visuo-spatial processing and high STM load: Dot sequence and Sound sequence) while no difference was observed in the numerical comparison of sets presented simultaneously. In addition, the group difference in sequential tasks disappeared when controlling for STM abilities. Finally, both groups demonstrated similar performance when comparing continuous or symbolic magnitude stimuli and they exhibited comparable subitizing abilities. These results highlight the importance of STM abilities in extracting numerosity through a sequential presentation and underline the importance of considering the impact of format presentation on magnitude judgments. PMID:28222116

  9. Earthquake catalogs for the 2017 Central and Eastern U.S. short-term seismic hazard model

    Science.gov (United States)

    Mueller, Charles S.

    2017-01-01

    The U. S. Geological Survey (USGS) makes long-term seismic hazard forecasts that are used in building codes. The hazard models usually consider only natural seismicity; non-tectonic (man-made) earthquakes are excluded because they are transitory or too small. In the past decade, however, thousands of earthquakes related to underground fluid injection have occurred in the central and eastern U.S. (CEUS), and some have caused damage.  In response, the USGS is now also making short-term forecasts that account for the hazard from these induced earthquakes. Seismicity statistics are analyzed to develop recurrence models, accounting for catalog completeness. In the USGS hazard modeling methodology, earthquakes are counted on a map grid, recurrence models are applied to estimate the rates of future earthquakes in each grid cell, and these rates are combined with maximum-magnitude models and ground-motion models to compute the hazard The USGS published a forecast for the years 2016 and 2017.Here, we document the development of the seismicity catalogs for the 2017 CEUS short-term hazard model.  A uniform earthquake catalog is assembled by combining and winnowing pre-existing source catalogs. The initial, final, and supporting earthquake catalogs are made available here.

  10. Modelling short-term variability in carbon and water exchange in a temperate Scots pine forest

    Directory of Open Access Journals (Sweden)

    M. H. Vermeulen

    2015-02-01

    Full Text Available Vegetation – atmosphere carbon and water exchange at one particular site can strongly vary from year to year, and understanding this interannual variability in carbon and water exchange (IAVcw is a critical factor in projecting future ecosystem changes. However, the mechanisms driving this IAVcw are not well understood. We used data on carbon and water fluxes from a multi-year Eddy Covariance study (1997–2009 in a Dutch Scots pine forest and forced a process-based ecosystem model (LPJ-GUESS with local data to, firstly, test whether the model can explain IAVcw and seasonal carbon and water exchange from direct environmental factors only. Initial model runs showed low correlations with estimated annual gross primary productivity (GPP and annual actual evapotranspiration (AET, while monthly and daily fluxes showed high correlations. The model underestimated GPP and AET during winter and drought events. Secondly, we adapted the temperature inhibition function of photosynthesis to account for the observation that at this particular site, trees continue to assimilate at very low atmospheric temperatures (up to daily averages of −10 °C, resulting in a net carbon sink in winter. While we were able to improve daily and monthly simulations during winter by lowering the modelled minimum temperature threshold for photosynthesis, this did not increase explained IAVcw at the site. Thirdly, we implemented three alternative hypotheses concerning water uptake by plants in order to test which one best corresponds with the data. In particular, we analyse the effects during the 2003 heatwave. These simulations revealed a strong sensitivity of the modelled fluxes during dry and warm conditions, but no single formulation was consistently superior in reproducing the data for all time scales and the overall model-data match for IAVcw could not be improved. Most probably access to deep soil water leads to higher AET and GPP simulated during the heat wave of 2003

  11. Application of short-term water demand prediction model to Seoul.

    Science.gov (United States)

    Joo, C N; Koo, J Y; Yu, M J

    2002-01-01

    To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.

  12. Functional diversity and functional traits of periphytic algae during a short-term successional process in a Neotropical floodplain lake.

    Science.gov (United States)

    Dunck, B; Rodrigues, L; Bicudo, D C

    2015-08-01

    Due to the lack of knowledge in periphytic algae functional diversity patterns during successional processes in floodplains, the present study aimed to analyze the dynamics of the functional traits and functional diversity of periphytic species during a short-term successional process in a floodplain lake. The functional traits analyzed were size class, growth form, strength of attachment to the substratum, and functional strategies. We evaluated the dynamics of these traits, considering richness, density and biovolume during an 18-day colonization in two hydrological periods. The functional diversity was assessed using the mean pairwise distance index (MPD). Dominant functional traits during the colonization changed in association with the flood pulse. Under the pulse effect, higher development of C-S strategist, loosely attached, filamentous and nanoperiphytic species occurred. The highest values of functional diversity were associated with the algal biomass peak during the colonization and the high water hydrological period, possibly indicating greater efficiency in the ecosystem functioning. These findings show the importance of the functional traits approach in periphyton studies and that the selection of functional traits must be performed taking into account traits that represent the species niche.

  13. Modeling of short-term mechanism of arterial pressure control in the cardiovascular system: object-oriented and acausal approach.

    Science.gov (United States)

    Kulhánek, Tomáš; Kofránek, Jiří; Mateják, Marek

    2014-11-01

    This letter introduces an alternative approach to modeling the cardiovascular system with a short-term control mechanism published in Computers in Biology and Medicine, Vol. 47 (2014), pp. 104-112. We recommend using abstract components on a distinct physical level, separating the model into hydraulic components, subsystems of the cardiovascular system and individual subsystems of the control mechanism and scenario. We recommend utilizing an acausal modeling feature of Modelica language, which allows model variables to be expressed declaratively. Furthermore, the Modelica tool identifies which are the dependent and independent variables upon compilation. An example of our approach is introduced on several elementary components representing the hydraulic resistance to fluid flow and the elastic response of the vessel, among others. The introduced model implementation can be more reusable and understandable for the general scientific community.

  14. A mathematical model of non-photochemical quenching to study short-term light memory in plants.

    Science.gov (United States)

    Matuszyńska, Anna; Heidari, Somayyeh; Jahns, Peter; Ebenhöh, Oliver

    2016-12-01

    Plants are permanently exposed to rapidly changing environments, therefore it is evident that they had to evolve mechanisms enabling them to dynamically adapt to such fluctuations. Here we study how plants can be trained to enhance their photoprotection and elaborate on the concept of the short-term illumination memory in Arabidopsis thaliana. By monitoring fluorescence emission dynamics we systematically observe the extent of non-photochemical quenching (NPQ) after previous light exposure to recognise and quantify the memory effect. We propose a simplified mathematical model of photosynthesis that includes the key components required for NPQ activation, which allows us to quantify the contribution to photoprotection by those components. Due to its reduced complexity, our model can be easily applied to study similar behavioural changes in other species, which we demonstrate by adapting it to the shadow-tolerant plant Epipremnum aureum. Our results indicate that a basic mechanism of short-term light memory is preserved. The slow component, accumulation of zeaxanthin, accounts for the amount of memory remaining after relaxation in darkness, while the fast one, antenna protonation, increases quenching efficiency. With our combined theoretical and experimental approach we provide a unifying framework describing common principles of key photoprotective mechanisms across species in general, mathematical terms.

  15. Facilitation or disengagement? Attention bias in facial affect processing after short-term violent video game exposure

    Science.gov (United States)

    Liu, Yanling; Lan, Haiying; Teng, Zhaojun; Guo, Cheng; Yao, Dezhong

    2017-01-01

    Previous research has been inconsistent on whether violent video games exert positive and/or negative effects on cognition. In particular, attentional bias in facial affect processing after violent video game exposure continues to be controversial. The aim of the present study was to investigate attentional bias in facial recognition after short term exposure to violent video games and to characterize the neural correlates of this effect. In order to accomplish this, participants were exposed to either neutral or violent video games for 25 min and then event-related potentials (ERPs) were recorded during two emotional search tasks. The first search task assessed attentional facilitation, in which participants were required to identify an emotional face from a crowd of neutral faces. In contrast, the second task measured disengagement, in which participants were required to identify a neutral face from a crowd of emotional faces. Our results found a significant presence of the ERP component, N2pc, during the facilitation task; however, no differences were observed between the two video game groups. This finding does not support a link between attentional facilitation and violent video game exposure. Comparatively, during the disengagement task, N2pc responses were not observed when participants viewed happy faces following violent video game exposure; however, a weak N2pc response was observed after neutral video game exposure. These results provided only inconsistent support for the disengagement hypothesis, suggesting that participants found it difficult to separate a neutral face from a crowd of emotional faces. PMID:28249033

  16. Environmental monitoring and assessment of short-term exposures to hazardous chemicals of a sterilization process in hospital working environments.

    Directory of Open Access Journals (Sweden)

    Koda S

    1999-10-01

    Full Text Available In order to assess short-term exposures to ethylene oxide, formaldehyde and glutaraldehyde in a sterilization process, the authors conducted continuous environmental monitoring of these chemicals in the breathing zone of workers in 2 hospitals. The arithmetic mean of ethylene oxide was 1.2 ppm near unventilated cabinets housing sterilizing materials, and environmental concentrations of ethylene oxide could not be reduced under threshold limit values time weighted average by only managing general ventilation. Environmental concentration of formaldehyde was lower in a properly ventilated pathology division in which no large specimens were stored (0.3 ppm than in the pathology division where large specimens were stored (2.3 ppm. Although environmental concentrations of glutaraldehyde in an endoscopy unit with proper general ventilation were not detectable, environmental concentration levels in an endoscopy unit without general ventilation system were 0.2 and 0.5 ppm. According to the results of environmental monitoring in the breathing zone of workers, extremely high concentrations were observed in some work practices (ethylene oxide, 300 ppm; formaldehyde, 8.6 ppm; glutaraldehyde, 2.6 ppm. In order to avoid occupational exposures to these chemicals and prevent potential chronic and acute health hazards, good communications with these chemicals, good work practices, appropriate personal protective equipment, and engineering controls should be required.

  17. Why chunking should be considered as an explanation for developmental change before short-term memory capacity and processing speed

    Directory of Open Access Journals (Sweden)

    Gary eJones

    2012-06-01

    Full Text Available The chunking hypothesis suggests that during the repeated exposure of stimulus material, information is organized into increasingly larger chunks. Many researchers have not considered the full power of the chunking hypothesis as both a learning mechanism and as an explanation of human behavior. Indeed, in developmental psychology there is relatively little mention of chunking and yet it can be the underlying cause of some of the mechanisms of development that have been proposed. This paper illustrates the chunking hypothesis in the domain of nonword repetition, a task that is a strong predictor of a child’s language learning. A computer simulation of nonword repetition that instantiates the chunking mechanism shows that: (1 chunking causes task behavior to improve over time, consistent with children’s performance; and (2 chunking causes perceived changes in areas such as short-term memory capacity and processing speed that are often cited as mechanisms of child development. Researchers should be cautious when considering explanations of developmental data, since chunking may be able to explain differences in performance without the need for additional mechanisms of development.

  18. Short-term creep properties of Ti-6Al-4V alloy subjected to surface plasma carburizing process

    Directory of Open Access Journals (Sweden)

    Verônica Mara Cortez Alves de Oliveira

    2015-10-01

    Full Text Available The aim of this study was to investigate the short-time creep behavior of Ti-6Al-4V by plasma carburizing, which was performed at 725 °C for 6 h in a 50% Ar – 45% H2 – 5% CH4 gas mixture. Nano and microhardness testing, optical microscopy, TEM, X-ray diffraction and optical profilometry were used to characterize the samples. Furthermore, short-term creep tests were performed under a constant tensile load in air at 600 °C using a dead-weight-creep-rupture machine. The carburizing treatment resulted in a compound layer measuring approximately 1.7 μm in thickness with a hardness of 815 HV and a composition of TiC0.66. The creep properties of the “Widmanstätten + carburized” specimens were improved relative to those of untreated specimens. TEM and fracture analysis indicated creep deformation process attributed mainly to α phase deformation and fracture by intergranular decohesion.

  19. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

    OpenAIRE

    Fay, D; Ringwood, John; Condon, M.

    2004-01-01

    Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...

  20. A PSO-SVM Model for Short-Term Travel Time Prediction Based on Bluetooth Technology

    Institute of Scientific and Technical Information of China (English)

    Qun Wang; Zhuyun Liu; Zhongren Peng

    2015-01-01

    The accurate prediction of travel time along roadway provides valuable traffic information for travelers and traffic managers. Aiming at short⁃term travel time forecasting on urban arterials, a prediction model ( PSO⁃SVM) combining support vector machine ( SVM) and particle swarm optimization ( PSO) is developed. Travel time data collected with Bluetooth devices are used to calibrate the proposed model. Field experiments show that the PSO⁃SVM model ’ s error indicators are lower than the single SVM model and the BP neural network (BPNN)model. Particularly, the mean⁃absolute percentage error (MAPE) of PSO⁃SVM is only 9�453 4 %which is less than that of the single SVM model ( 12�230 2 %) and the BPNN model ( 15�314 7 %) . The results indicate that the proposed PSO⁃SVM model is feasible and more effective than other models for short⁃term travel time prediction on urban arterials.

  1. The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

    Directory of Open Access Journals (Sweden)

    César Hernández-Hernández

    2017-06-01

    Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.

  2. Alteration of Neuronal Excitability and Short-Term Synaptic Plasticity in the Prefrontal Cortex of a Mouse Model of Mental Illness.

    Science.gov (United States)

    Crabtree, Gregg W; Sun, Ziyi; Kvajo, Mirna; Broek, Jantine A C; Fénelon, Karine; McKellar, Heather; Xiao, Lan; Xu, Bin; Bahn, Sabine; O'Donnell, James M; Gogos, Joseph A

    2017-04-12

    also upon their families and the broader society. Although the underlying causes of schizophrenia remain poorly understood, a growing body of studies has identified and strongly implicated various specific risk genes in schizophrenia pathogenesis. Here, using a genetic mouse model, we explored the impact of one of the most highly penetrant schizophrenia risk genes, DISC1, upon the medial prefrontal cortex, the region believed to be most prominently dysfunctional in schizophrenia. We found substantial derangements in both neuronal excitability and short-term synaptic plasticity-parameters that critically govern neural circuit information processing-suggesting that similar changes may critically, and more broadly, underlie the neural computational dysfunction prototypical of schizophrenia. Copyright © 2017 the authors 0270-6474/17/374159-23$15.00/0.

  3. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas;

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  4. Comparison Of Short Term Rainfall Forecasts For Model Based Flow Prediction In Urban Drainage Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas;

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  5. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  6. Comparison Of Short Term Rainfall Forecasts For Model Based Flow Prediction In Urban Drainage Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  7. Noise model based ν-support vector regression with its application to short-term wind speed forecasting.

    Science.gov (United States)

    Hu, Qinghua; Zhang, Shiguang; Xie, Zongxia; Mi, Jusheng; Wan, Jie

    2014-09-01

    Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique.

  8. A model of competition between employed, short-term and long-term unemployed job searchers

    NARCIS (Netherlands)

    Broersma, Lourens

    1995-01-01

    This paper presents a model in which not only employed job search is endogenized, but also the phenomenon that long-term unemployed may becomediscouraged and stop searching for a job. When this model is applied to Dutch flow data, we find that this discouragement particularly took place in the early

  9. Modeling short-term dynamics and variability for realistic interactive facial animation.

    Science.gov (United States)

    Stoiber, Nicolas; Breton, Gaspard; Seguier, Renaud

    2010-01-01

    Modern modeling and rendering techniques have produced nearly photorealistic face models, but truly expressive digital faces also require natural-looking movements. Virtual characters in today's applications often display unrealistic facial expressions. Indeed, facial animation with traditional schemes such as keyframing and motion capture demands expertise. Moreover, the traditional schemes aren't adapted to interactive applications that require the real-time generation of context-dependent movements. A new animation system produces realistic expressive facial motion at interactive speed. The system relies on a set of motion models controlling facial-expression dynamics. The models are fitted on captured motion data and therefore retain the dynamic signature of human facial expressions. They also contain a nondeterministic component that ensures the variety of the long-term visual behavior. This system can efficiently animate any synthetic face. The video illustrates interactive use of a system that generates facial-animation sequences.

  10. Short-term monitoring of the Spanish Government balance with mixed-frequencies models

    OpenAIRE

    Teresa Leal; Diego J. Pedregal; Javier J. Pérez

    2009-01-01

    We construct multivariate, state-space mixed-frequencies models for the main componentsof the Spanish General Government sector made up of blocks for each one of its subsectors: Central Government, Social Security and aggregate of Regional and Local government sectors. Each block is modelled through its total revenue and expenditure categories, and encompasses a number of indicators, depending on data availability. The mixed-frequencies approach is particularly relevant for the case of Spain,...

  11. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Hang T.; Nabney, Ian T. [Non-linearity and Complexity Research Group, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET (United Kingdom)

    2010-09-15

    This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. (author)

  12. A search for short-term meteorological effects of solar variability in an atmospheric circulation model

    Science.gov (United States)

    Somerville, R. C. J.; Quirk, W. J.; Hansen, J. E.; Lacis, A. A.; Stone, P. H.

    1976-01-01

    A set of numerical experiments is carried out to test the short-range sensitivity of the Goddard Institute for Space Studies global atmospheric general-circulation model to changes in solar constant and ozone amount. These experiments consist of forecasts initiated with actual atmospheric data. One set of forecasts is made with a standard version of the model; another set uses the model modified by very different values of the solar constant (two-thirds and three-halves of the standard value) and of the ozone amount (zero and twice the standard amount). Twelve-day integrations with these very large variations show such small effects that the effects of realistic variations would almost certainly be insignificant meteorologically on this time scale.

  13. Measuring genetic distances between breeds: use of some distances in various short term evolution models

    Directory of Open Access Journals (Sweden)

    SanCristobal Magali

    2002-07-01

    Full Text Available Abstract Many works demonstrate the benefits of using highly polymorphic markers such as microsatellites in order to measure the genetic diversity between closely related breeds. But it is sometimes difficult to decide which genetic distance should be used. In this paper we review the behaviour of the main distances encountered in the literature in various divergence models. In the first part, we consider that breeds are populations in which the assumption of equilibrium between drift and mutation is verified. In this case some interesting distances can be expressed as a function of divergence time, t, and therefore can be used to construct phylogenies. Distances based on allele size distribution (such as (δμ2 and derived distances, taking a mutation model of microsatellites, the Stepwise Mutation Model, specifically into account, exhibit large variance and therefore should not be used to accurately infer phylogeny of closely related breeds. In the last section, we will consider that breeds are small populations and that the divergence times between them are too small to consider that the observed diversity is due to mutations: divergence is mainly due to genetic drift. Expectation and variance of distances were calculated as a function of the Wright-Malécot inbreeding coefficient, F. Computer simulations performed under this divergence model show that the Reynolds distance [57]is the best method for very closely related breeds.

  14. Improved short-term variability in the thermosphere-ionosphere-mesosphere-electrodynamics general circulation model

    NARCIS (Netherlands)

    Häusler, K.; Hagan, M.E.; Baumgaertner, A.J.G.; Maute, A.; Lu, G.; Doornbos, E.N.; Bruinsma, S.; Forbes, J.M.; Gasperini, F.

    2014-01-01

    We report on a new source of tidal variability in the National Center for Atmospheric Research thermosphere-ionosphere-mesosphere-electrodynamics general circulation model (TIME-GCM). Lower boundary forcing of the TIME-GCM for a simulation of November–December 2009 based on 3-hourly Modern-Era Retro

  15. Short-term impacts of enhanced Greenland freshwater fluxes in an eddy-permitting ocean model

    Directory of Open Access Journals (Sweden)

    R. Marsh

    2009-11-01

    Full Text Available In a sensitivity experiment, an eddy-permitting ocean general circulation model is forced with freshwater fluxes from the Greenland Ice Sheet, averaged for the period 1991–2000. The fluxes are obtained with a mass balance model for the ice sheet, forced with the ERA-40 reanalysis dataset. The freshwater flux is distributed around Greenland as an additional term in prescribed runoff, representing seasonal melting of the ice sheet and a fixed year-round iceberg calving flux, for 8.5 model years. The impacts on regional hydrography and circulation are investigated by comparing the sensitivity experiment to a control experiment, without Greenland fluxes. By the end of the sensitivity experiment, the majority of additional fresh water has accumulated in Baffin Bay, and only a small fraction has reached the interior of the Labrador Sea, where winter mixed layer depth is sensitive to small changes in salinity. As a consequence, the impact on large-scale circulation is very slight. An indirect impact of strong freshening off the west coast of Greenland is a small anti-cyclonic circulation around Greenland which opposes the wind-driven cyclonic circulation and reduces net southward flow through the Canadian Archipelago by ~10%. Implications for the post-2000 acceleration of Greenland mass loss are discussed.

  16. Improved short-term variability in the thermosphere-ionosphere-mesosphere-electrodynamics general circulation model

    Science.gov (United States)

    Häusler, K.; Hagan, M. E.; Baumgaertner, A. J. G.; Maute, A.; Lu, G.; Doornbos, E.; Bruinsma, S.; Forbes, J. M.; Gasperini, F.

    2014-08-01

    We report on a new source of tidal variability in the National Center for Atmospheric Research thermosphere-ionosphere-mesosphere-electrodynamics general circulation model (TIME-GCM). Lower boundary forcing of the TIME-GCM for a simulation of November-December 2009 based on 3-hourly Modern-Era Retrospective Analysis for Research and Application (MERRA) reanalysis data includes day-to-day variations in both diurnal and semidiurnal tides of tropospheric origin. Comparison with TIME-GCM results from a heretofore standard simulation that includes climatological tropospheric tides from the global-scale wave model reveal evidence of the impacts of MERRA forcing throughout the model domain, including measurable tidal variability in the TIME-GCM upper thermosphere. Additional comparisons with measurements made by the Gravity field and steady-state Ocean Circulation Explorer satellite show improved TIME-GCM capability to capture day-to-day variations in thermospheric density for the November-December 2009 period with the new MERRA lower boundary forcing.

  17. Teen Tobacco Court: A Determination of the Short-Term Outcomes of Judicial Processes with Teens Engaging in Tobacco Possession.

    Science.gov (United States)

    Langer, Lilly M.; Warheit, George J.

    2000-01-01

    Investigated the impact of a tobacco citation and subsequent court appearance on teens who possessed tobacco, examining offenders' attitudes and behaviors following citation and court appearance. Surveys and interviews indicated that being ticketed and appearing in teen tobacco court had significant, positive, short-term impacts on large numbers…

  18. Multi-level prediction of short-term outcome of depression : non-verbal interpersonal processes, cognitions and personality traits

    NARCIS (Netherlands)

    Geerts, E; Bouhuys, N

    1998-01-01

    It was hypothesized that personality factors determine the short-term outcome of depression, and that they may do this via non-verbal interpersonal interactions and via cognitive interpretations of non-verbal behaviour. Twenty-six hospitalized depressed patients entered the study. Personality factor

  19. Combining process indicators to evaluate quality of care for surgical patients with colorectal cancer: are scores consistent with short-term outcome?

    NARCIS (Netherlands)

    Kolfschoten, N.E.; Gooiker, G.A.; Bastiaannet, E.; Leersum, N.J. van; Velde, C.J. van de; Eddes, E.H.; Marang-van de Mheen, P.J.; Kievit, J.; Harst, E. van der; Wiggers, T.; Wouters, M.W.; Tollenaar, R.A.E.M.; Krieken, J.H. van

    2012-01-01

    OBJECTIVE: To determine if composite measures based on process indicators are consistent with short-term outcome indicators in surgical colorectal cancer care. DESIGN: Longitudinal analysis of consistency between composite measures based on process indicators and outcome indicators for 85 Dutch hosp

  20. Combining process indicators to evaluate quality of care for surgical patients with colorectal cancer : are scores consistent with short-term outcome?

    NARCIS (Netherlands)

    Kolfschoten, N. E.; Gooiker, G. A.; Bastiaannet, E.; van Leersum, N. J.; van de Velde, C. J. H.; Eddes, E. H.; Marang-van de Mheen, P. J.; Kievit, J.; van der Harst, E.; Wiggers, T.; Wouters, M. W. J. M.; Tollenaar, R. A. E. M.

    2012-01-01

    Objective: To determine if composite measures based on process indicators are consistent with short-term outcome indicators in surgical colorectal cancer care. Design: Longitudinal analysis of consistency between composite measures based on process indicators and outcome indicators for 85 Dutch hosp

  1. Short-term impacts of enhanced Greenland freshwater fluxes in an eddy-permitting ocean model

    Directory of Open Access Journals (Sweden)

    R. Marsh

    2010-07-01

    Full Text Available In a sensitivity experiment, an eddy-permitting ocean general circulation model is forced with realistic freshwater fluxes from the Greenland Ice Sheet, averaged for the period 1991–2000. The fluxes are obtained with a mass balance model for the ice sheet, forced with the ERA-40 reanalysis dataset. The freshwater flux is distributed around Greenland as an additional term in prescribed runoff, representing seasonal melting of the ice sheet and a fixed year-round iceberg calving flux, for 8.5 model years. By adding Greenland freshwater fluxes with realistic geographical distribution and seasonality, the experiment is designed to investigate the oceanic response to a sudden and spatially/temporally uniform amplification of ice sheet melting and discharge, rather than localized or gradual changes in freshwater flux. The impacts on regional hydrography and circulation are investigated by comparing the sensitivity experiment to a control experiment, without additional fluxes. By the end of the sensitivity experiment, the majority of additional fresh water has accumulated in Baffin Bay, and only a small fraction has reached the interior of the Labrador Sea, where winter mixed layer depth is sensitive to small changes in salinity. As a consequence, the impact on large-scale circulation is very slight. An indirect impact of strong freshening off the west coast of Greenland is a small anti-cyclonic component to the circulation around Greenland, which opposes the wind-driven cyclonic circulation and reduces net southward flow through the Canadian Archipelago by ~10%. Implications for the post-2000 acceleration of Greenland mass loss are discussed.

  2. A short term prediction model for surface ozone at southwest part of Mexico valley

    Energy Technology Data Exchange (ETDEWEB)

    Bravo, J. L.; Diaz, M. T. [Instituto de Geofisica, UNAM, Mexico, D.F. (Mexico); Gay, C. [Centro de Ciencias de la Atmosfera, Mexico, D.F. (Mexico); Fajardo, J. [Instituto Nacional de Pediatria, Mexico, D.F. (Mexico)

    1996-01-01

    Mexico City is located in a valley in the tropical zone with high intensity of total solar radiation; it has poor wind circulation and a great deal of industrial and transportation activity, as a consequence of this it has a serious problem with air pollution. Ozone is representative of total atmospheric oxidants and of air pollution. In this work a multiplicative model is proposed and adjusted to 3 years of daily observations at the Pedregal de San Angel Station, located at the Southwest part of Mexico's Valley. The importance of some common meteorological parameters in the explanation of daily variance is evaluated. 50% of the total variance is explained with total solar radiation and the previous day ozone concentration and 61% using all variables. This model could be useful in the prediction of ozone concentrations with help of a model to predict solar radiation and could be used in the establishment of criteria for environmental alerts. [Spanish] La Ciudad de Mexico esta situada en un valle en una zona tropical con elevadas cantidades de radiacion solar, tiene poca ventilacion y una gran actividad industrial y de transporte, en consecuencia, presenta problemas graves de contaminacion atmosferica. Se ha escogido al ozono como representativo de los oxidantes atmosfericos y de la contaminacion ambiental. Se propone en este trabajo un modelo multiplicado y se ajusta a 3 anos de observaciones diarias para la estacion Pedregal de San Angel, situada al Suroeste del Valle de Mexico, se evalua la importancia de algunos parametros de uso general en las estaciones meteorologicas. El modelo explica 50% de la variabilidad diaria empleando la radiacion solar y la concentracion de ozono del dia anterior y el 61% con la totalidad de las variables usadas. El modelo podria ser util para predecir la concentracion de ozono con el auxilio de una prediccion de la radiacion solar y emplearse en alertas ambientales.

  3. Vascular and hepatic impact of short-term intermittent hypoxia in a mouse model of metabolic syndrome.

    Directory of Open Access Journals (Sweden)

    Wojciech Trzepizur

    Full Text Available Experimental models of intermittent hypoxia (IH have been developed during the last decade to investigate the consequences of obstructive sleep apnea. IH is usually associated with detrimental metabolic and vascular outcomes. However, paradoxical protective effects have also been described depending of IH patterns and durations applied in studies. We evaluated the impact of short-term IH on vascular and metabolic function in a diet-induced model of metabolic syndrome (MS.Mice were fed either a standard diet or a high fat diet (HFD for 8 weeks. During the final 14 days of each diet, animals were exposed to either IH (1 min cycle, FiO2 5% for 30s, FiO2 21% for 30s; 8 h/day or intermittent air (FiO2 21%. Ex-vivo vascular reactivity in response to acetylcholine was assessed in aorta rings by myography. Glucose, insulin and leptin levels were assessed, as well as serum lipid profile, hepatic mitochondrial activity and tissue nitric oxide (NO release.Mice fed with HFD developed moderate markers of dysmetabolism mimicking MS, including increased epididymal fat, dyslipidemia, hepatic steatosis and endothelial dysfunction. HFD decreased mitochondrial complex I, II and IV activities and increased lactate dehydrogenase (LDH activity in liver. IH applied to HFD mice induced a major increase in insulin and leptin levels and prevented endothelial dysfunction by restoring NO production. IH also restored mitochondrial complex I and IV activities, moderated the increase in LDH activity and liver triglyceride accumulation in HFD mice.In a mouse model of MS, short-term IH increases insulin and leptin levels, restores endothelial function and mitochondrial activity and limits liver lipid accumulation.

  4. Long-term earthquake forecasts based on the epidemic-type aftershock sequence (ETAS model for short-term clustering

    Directory of Open Access Journals (Sweden)

    Jiancang Zhuang

    2012-07-01

    Full Text Available Based on the ETAS (epidemic-type aftershock sequence model, which is used for describing the features of short-term clustering of earthquake occurrence, this paper presents some theories and techniques related to evaluating the probability distribution of the maximum magnitude in a given space-time window, where the Gutenberg-Richter law for earthquake magnitude distribution cannot be directly applied. It is seen that the distribution of the maximum magnitude in a given space-time volume is determined in the longterm by the background seismicity rate and the magnitude distribution of the largest events in each earthquake cluster. The techniques introduced were applied to the seismicity in the Japan region in the period from 1926 to 2009. It was found that the regions most likely to have big earthquakes are along the Tohoku (northeastern Japan Arc and the Kuril Arc, both with much higher probabilities than the offshore Nankai and Tokai regions.

  5. Mechanical stress induces bone formation in the maxillary sinus in a short-term mouse model.

    Science.gov (United States)

    Kuroda, Shingo; Wazen, Rima; Moffatt, Pierre; Tanaka, Eiji; Nanci, Antonio

    2013-01-01

    Clinicians occasionally face the challenge of moving a tooth through the maxillary sinus. The objective of this study was to evaluate tissue remodeling during tooth movement into the maxillary sinus, more specifically as regards to bone formation. The maxillary first molar of 20 male mice was moved toward the palatal side by a nickel-titanium super elastic wire for 1 to 14 days, and the bone remodeling around the root was evaluated using histomorphometry and immunodetection of bone-restricted Ifitm-like (Bril) protein, a novel marker of active bone formation. When mechanical stress was applied to the tooth, the periodontal ligament on the palatal side was immediately compressed to approximately half of its original width by the tipping movement of the tooth. At the same time, osteoblasts deposited new bone on the wall of the maxillary sinus prior to bone resorption by osteoclasts on the periodontal side, as evidenced by the high level of expression of Bril at this site. As a result of these sequential processes, bone on the sinus side maintained a consistent thickness during the entire observation period. No root resorption was observed. Bone formation on the surface of the maxillary sinus was evoked by mechanotransduction of mechanical stress applied to a tooth over a 2-week period, and was induced ahead of bone resorption on the periodontal ligament side. Mechanical stress can be exploited to induce bone formation in the maxillary sinus so that teeth can be moved into the sinus without losing bone or causing root damage.

  6. Predictive model of short-term amputation during hospitalization of patients due to acute diabetic foot infections.

    Science.gov (United States)

    Barberán, José; Granizo, Juan-José; Aguilar, Lorenzo; Alguacil, Rafael; Sainz, Felipe; Menéndez, Maria-Antonia; Giménez, Maria-José; Martínez, David; Prieto, José

    2010-12-01

    Factors predicting short-term amputation during hospital treatment of patients admitted for acute diabetic foot infections are of interest for clinicians managing the acute episode. A retrospective clinical records analysis of 78 consecutive patients hospitalized for acute diabetic foot infections was performed to identify predictive factors for short-term amputation by comparing the data of patients who ultimately required amputation and those who did not. Clinical/epidemiological, laboratory, imaging, and treatment variables were comparatively analyzed. A logistic regression model was performed, with amputation as the dependent variable and factors showing significant differences in the bivariate analysis as independent variables. A prediction score was calculated (and validated by ROC curve analysis) using beta coefficients for significant variables in the regression analysis to predict amputation. Of the 78 patients (70.5% with peripheral vasculopathy) included, 26 ultimately required amputation. In the bivariate analysis, white blood cell count, previous homolateral lesions, odor, lesion depth, sedimentation rate, Wagner ulcer grade, and arterial obstruction on Doppler study were significantly higher in patients ending in amputation. In the multivariate analysis, the risk of amputation was increased only by Wagner grade 4 or 5 (20-fold higher), obstruction (12.5-fold higher), and elevated sedimentation rate (6% higher per unit). Logistic regression predicted outcome in 76.9% of patients who underwent amputation and 92.3% of those who did not. The score calculated using beta coefficients for significant variables in the regression model (Wagner grades 4 and 5, obstruction on Doppler, and elevated sedimentation rate for the clinical, imaging, and laboratory data, respectively) correctly predicted amputation during hospital management of acute diabetic foot infections. Copyright © 2009 Elsevier España, S.L. All rights reserved.

  7. Short Term Evaluation of an Anatomically Shaped Polycarbonate Urethane Total Meniscus Replacement in a Goat Model.

    Directory of Open Access Journals (Sweden)

    A C T Vrancken

    Full Text Available Since the treatment options for symptomatic total meniscectomy patients are still limited, an anatomically shaped, polycarbonate urethane (PCU, total meniscus replacement was developed. This study evaluates the in vivo performance of the implant in a goat model, with a specific focus on the implant location in the joint, geometrical integrity of the implant and the effect of the implant on synovial membrane and articular cartilage histopathological condition.The right medial meniscus of seven Saanen goats was replaced by the implant. Sham surgery (transection of the MCL, arthrotomy and MCL suturing was performed in six animals. The contralateral knee joints of both groups served as control groups. After three months follow-up the following aspects of implant performance were evaluated: implant position, implant deformation and the histopathological condition of the synovium and cartilage.Implant geometry was well maintained during the three month implantation period. No signs of PCU wear were found and the implant did not induce an inflammatory response in the knee joint. In all animals, implant fixation was compromised due to suture breakage, wear or elongation, likely causing the increase in extrusion observed in the implant group. Both the femoral cartilage and tibial cartilage in direct contact with the implant showed increased damage compared to the sham and sham-control groups.This study demonstrates that the novel, anatomically shaped PCU total meniscal replacement is biocompatible and resistant to three months of physiological loading. Failure of the fixation sutures may have increased implant mobility, which probably induced implant extrusion and potentially stimulated cartilage degeneration. Evidently, redesigning the fixation method is necessary. Future animal studies should evaluate the improved fixation method and compare implant performance to current treatment standards, such as allografts.

  8. Research on Electrical Remodeling After Short Term Pacing in Canine Model

    Institute of Scientific and Technical Information of China (English)

    Kebbati A Hafid; Huang Congxin; Wang Xi; Zhao Qingyan

    2007-01-01

    Objectives To evaluate the changes in atrial effective refractory period (AERP) proprieties and in ionic currents in PVs myocytes from dogs subjected to rapid atrial pacing in PVs and right atrial appendage (RAA) and to relate these changes to the ability to induce AF. Methods Twelve mongrel dogs in normal sinus rhythm were paced from the superior left PVs or RAA at 500 bpm for 4 hours. Electrophysiologic studies conducted to determine changes in AERP, dispersion and rhythm. Ionic currents were studies with the patch clamp technique in single PVs myocytes in sham operated dogs and compared with those from PVs pacing and RAA pacing groups. Results The presence of rapid atrial pacing was associated with a marked shortening in AERP in both PVs and RAA pacing group with a marked increase of AERP dispersion in PVs pacing. Both L-type calcium current (ICa, L) and the transient outward current (Ito)were reduced in both groups with an increased significance in PVs pacing group. The density of ICa-L WaS decreased significantly from ( - 6.03 ± 0.63 ) pA/pF in the control group to ( -3.21±0.34) pA/pF in PVs pacing group and ( - 4,75 ± 0.41 ) pA/pF in RAA pacing group (n = 6, P<0.05) while the density of Ito was decreased significantly from (8.45±0.71 ) pA/pF in the control group to (5.21 ±0.763 ) pA/pF in PVs pacing group and (6,84 ± 0.69) pA/pF in RAA pacing group (n = 6, P<0.05). Conclusions Our findings provide likely ionic mechanisms of shortened repolarization in induced atrial tachycardia with a decrease in Ica,L and Ito current densities which is the likely mechanism for a decrease in Action potential duration (APD) rate adaptation in the canine rapid pacing model more pronounced in PVs pacing group underlying the crucial role of PVs in initiating AF.

  9. Analyzing the efficiency of short-term air quality plans in European cities, using the CHIMERE air quality model.

    Science.gov (United States)

    Thunis, P; Degraeuwe, B; Pisoni, E; Meleux, F; Clappier, A

    2017-01-01

    Regional and local authorities have the obligation to design air quality plans and assess their impacts when concentration levels exceed the limit values. Because these limit values cover both short- (day) and long-term (year) effects, air quality plans also follow these two formats. In this work, we propose a methodology to analyze modeled air quality forecast results, looking at emission reduction for different sectors (residential, transport, agriculture, etc.) with the aim of supporting policy makers in assessing the impact of short-term action plans. Regarding PM10, results highlight the diversity of responses across European cities, in terms of magnitude and type that raises the necessity of designing area-specific air quality plans. Action plans extended from 1 to 3 days (i.e., emissions reductions applied for 24 and 72 h, respectively) point to the added value of trans-city coordinated actions. The largest benefits are seen in central Europe (Vienna, Prague) while major cities (e.g., Paris) already solve a large part of the problem on their own. Eastern Europe would particularly benefit from plans based on emission reduction in the residential sectors; while in northern cities, agriculture seems to be the key sector on which to focus attention. Transport is playing a key role in most cities whereas the impact of industry is limited to a few cities in south-eastern Europe. For NO2, short-term action plans focusing on traffic emission reductions are efficient in all cities. This is due to the local character of this type of pollution. It is important, however, to stress that these results remain dependent on the selected months available for this study.

  10. Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data

    Science.gov (United States)

    Rusyn, Ivan; Sedykh, Alexander; Guyton, Kathryn Z.; Tropsha, Alexander

    2012-01-01

    Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR–like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage. PMID:22387746

  11. Short-term Dispersal of Fukushima-derived Radionuclides off Japan: Modeling Efforts and Model-data Inter-comparison

    Science.gov (United States)

    Rypina, I. I.; Jayne, S. R.; Yoshida, S.; Macdonald, A. M.; Douglass, E.; Buesseler, K.

    2012-12-01

    As a result of the Tohoku earthquake and tsunami on March 11, 2011, the Fukushima nuclear power plants were damaged and radioactive isotopes were released to the atmosphere and into the ocean. In order to assess the levels of contamination, a field study was conducted on June 4-18 that focused on measuring radionuclide isotopes including Cs-137 in surface and subsurface waters and biota off Japan coast. To interpret these field measurements, we carried out numerical simulations of the short-term spreading of the Fukushima-derived radionuclides. The results are used to investigate the dominant mechanisms governing the short-term spread of radiation within the North Pacific, and to place the measured radioactive isotope concentrations in the context of the physical oceanographic circulation.

  12. The chorioallantoic membrane (CAM) assay for biomaterial testing in tissue engineering: a short term in vivo preclinical model.

    Science.gov (United States)

    Moreno-Jimenez, Ines; Kanczler, Janos M; Hulsart-Billstrom, Gry S; Inglis, Stefanie; Oreffo, Richard O C

    2017-09-06

    The fields of regenerative medicine and tissue engineering offer significant promise to address the urgent unmet need for therapeutic strategies in a number of debilitating conditions, diseases and tissue needs of an aging population. Critically, the safety and efficacy of these pioneering strategies needs to be assessed prior to clinical application, often necessitating animal research as a prerequisite. The growing number of newly developed potential treatments, together with the ethical concerns involved in the application of in vivo studies, requires the implementation of alternative models to facilitate such screening of new treatments. The present review examines the current in vitro and in vivo models of preclinical research with particular emphasis on the chorioallantoic membrane (CAM) assay as a minimally invasive, short-term in vivo alternative. Traditionally used as an angiogenic assay, the CAM of the developing chick embryo provides a non-innervated rapidly growing vascular bed which can serve as a surrogate blood supply for organ culture, and hence a platform for biomaterial testing. This review offers an overview of the CAM assay and its applications in biomedicine as an in vivo model for organ culture and angiogenesis. Moreover, the application of imaging techniques (magnetic resonance imaging, micro computed tomography, fluorescence labelling for tracking) will be discussed for the evaluation of biomaterials cultured on the CAM. Finally, an overview of the CAM assay methodology will be provided to facilitate the adoption of this technique across laboratories and the regenerative medicine community, and thus aid the reduction, replacement and refinements of animal experiments in research.

  13. A Comparison Of Primitive Model Results Of The Short Term Wind Energy Prediction System (Sweps): WRF vs MM5

    Science.gov (United States)

    Unal, E.; Tan, E.; Mentes, S. S.; Caglar, F.; Turkmen, M.; Unal, Y. S.; Onol, B.; Ozdemir, E. T.

    2012-04-01

    Although discontinuous behavior of wind field makes energy production more difficult, wind energy is the fastest growing renewable energy sector in Turkey which is the 6th largest electricity market in Europe. Short-term prediction systems, which capture the dynamical and statistical nature of the wind field in spatial and time scales, need to be advanced in order to increase the wind power prediction accuracy by using appropriate numerical weather forecast models. Therefore, in this study, performances of the next generation mesoscale Numerical Weather Forecasting model, WRF, and The Fifth-Generation NCAR/Penn State Mesoscale Model, MM5, have been compared for the Western Part of Turkey. MM5 has been widely used by Turkish State Meteorological Service from which MM5 results were also obtained. Two wind farms of the West Turkey have been analyzed for the model comparisons by using two different model domain structures. Each model domain has been constructed by 3 nested domains downscaling from 9km to 1km resolution by the ratio of 3. Since WRF and MM5 models have no exactly common boundary layer, cumulus, and microphysics schemes, the similar physics schemes have been chosen for these two models in order to have reasonable comparisons. The preliminary results show us that, depending on the location of the wind farms, MM5 wind speed RMSE values are 1 to 2 m/s greater than that of WRF values. Since 1 to 2 m/s errors can be amplified when wind speed is converted to wind power; it is decided that the WRF model results are going to be used for the rest of the project.

  14. A short-term mouse model that reproduces the immunopathological features of rhinovirus-induced exacerbation of COPD.

    Science.gov (United States)

    Singanayagam, Aran; Glanville, Nicholas; Walton, Ross P; Aniscenko, Julia; Pearson, Rebecca M; Pinkerton, James W; Horvat, Jay C; Hansbro, Philip M; Bartlett, Nathan W; Johnston, Sebastian L

    2015-08-01

    Viral exacerbations of chronic obstructive pulmonary disease (COPD), commonly caused by rhinovirus (RV) infections, are poorly controlled by current therapies. This is due to a lack of understanding of the underlying immunopathological mechanisms. Human studies have identified a number of key immune responses that are associated with RV-induced exacerbations including neutrophilic inflammation, expression of inflammatory cytokines and deficiencies in innate anti-viral interferon. Animal models of COPD exacerbation are required to determine the contribution of these responses to disease pathogenesis. We aimed to develop a short-term mouse model that reproduced the hallmark features of RV-induced exacerbation of COPD. Evaluation of complex protocols involving multiple dose elastase and lipopolysaccharide (LPS) administration combined with RV1B infection showed suppression rather than enhancement of inflammatory parameters compared with control mice infected with RV1B alone. Therefore, these approaches did not accurately model the enhanced inflammation associated with RV infection in patients with COPD compared with healthy subjects. In contrast, a single elastase treatment followed by RV infection led to heightened airway neutrophilic and lymphocytic inflammation, increased expression of tumour necrosis factor (TNF)-α, C-X-C motif chemokine 10 (CXCL10)/IP-10 (interferon γ-induced protein 10) and CCL5 [chemokine (C-C motif) ligand 5]/RANTES (regulated on activation, normal T-cell expressed and secreted), mucus hypersecretion and preliminary evidence for increased airway hyper-responsiveness compared with mice treated with elastase or RV infection alone. In summary, we have developed a new mouse model of RV-induced COPD exacerbation that mimics many of the inflammatory features of human disease. This model, in conjunction with human models of disease, will provide an essential tool for studying disease mechanisms and allow testing of novel therapies with potential to

  15. The mind and brain of short-term memory.

    Science.gov (United States)

    Jonides, John; Lewis, Richard L; Nee, Derek Evan; Lustig, Cindy A; Berman, Marc G; Moore, Katherine Sledge

    2008-01-01

    The past 10 years have brought near-revolutionary changes in psychological theories about short-term memory, with similarly great advances in the neurosciences. Here, we critically examine the major psychological theories (the "mind") of short-term memory and how they relate to evidence about underlying brain mechanisms. We focus on three features that must be addressed by any satisfactory theory of short-term memory. First, we examine the evidence for the architecture of short-term memory, with special attention to questions of capacity and how--or whether--short-term memory can be separated from long-term memory. Second, we ask how the components of that architecture enact processes of encoding, maintenance, and retrieval. Third, we describe the debate over the reason about forgetting from short-term memory, whether interference or decay is the cause. We close with a conceptual model tracing the representation of a single item through a short-term memory task, describing the biological mechanisms that might support psychological processes on a moment-by-moment basis as an item is encoded, maintained over a delay with some forgetting, and ultimately retrieved.

  16. Energy storage systems impact on the short-term frequency stability of distributed autonomous microgrids, an analysis using aggregate models

    DEFF Research Database (Denmark)

    Serban, Ioan; Teodorescu, Remus; Marinescu, Corneliu

    2013-01-01

    of storing and releasing energy when required by the system. Therefore the need of boosting the MG power reserves by adding energy storage systems is often a requirement. The study highlights the improvement in the MG short-term frequency stability brought by an original BESS control structure enhanced......This study analyses the integration impact of battery energy storage systems (BESSs) on the short-term frequency control in autonomous microgrids (MGs). Short-term frequency stability relates with the primary or speed control level, as defined in the regulations of the classical grids. The focus...... is on autonomous MGs that dynamically behave similarly to the classical power systems. This is the systems case with classical distributed generators (DGs), but which can also contain renewable energy sources (RESs) in a certain penetration level. During MG islanded operation, the local generators take over most...

  17. Sedimentary process and recent morphological evolution in the Arcahon lagoon, France: a long and short term approaches

    Science.gov (United States)

    Arriagada-Gonzalez, Joselyn; Sottolichio, Aldo

    2017-04-01

    The Arcachon lagoon is a mesotidal embayment in the south Atlantic coast of France. Its total surface is about 174 km2, where 65% is formed of tidal flats. Previous studies have shown a relative stable morphology over a period of 126 years, and a very long infilling trend, with a total accretion rarely exceeding + 0.5m in some areas. This is consistent with the fact that fine sediment input from rivers is very low. However at the tidal short term, erosion of mudflats can reach several centimeters, especially under energetic windy conditions. Additionally, recent high-frequency monitoring showed that tidal flats experience erosion and accretion of several dm at the seasonal scale, following the annual cycle of seagrass Zostera noltei, which develops on the intertidal areas. These patterns support the most recent observations made by end-users of the lagoon, which point out relative infilling of the channels and increase of turbidity in the water. The whole set the observations suggest that a mobile stock of surficial sediment is available in the lagoon, which contributes to the accretion of the flats, but is also transported towards the channels, when erosive conditions prevail. The aim of this presentation is to show the patterns and conditions of mobility of this stock of sediment. In this work, a set of unpublished data of physical forcing, sediment dynamics and bathymetry of the lagoon, are analyzed over a period of 148 years (1864-2012), which an intermediate scale between the long-term and short-term scales, with bathymetric and LIDAR surveys. In addition, we performed a short-term analysis based on the monitoring of altimetric and granulometric variations in the northern area of the lagoon. We show that accretion and erosion rates are significant at the annual scale with clear trends of exchanges between the center of the lagoon and the internal banks. There is a spatial and temporal difference in the long-term sedimentary balance between each period analyzed

  18. The short-term effects of an integrated care model for the frail elderly on health, quality of life, health care use and satisfaction with care

    Directory of Open Access Journals (Sweden)

    Wilhelmina Mijntje Looman

    2014-12-01

    Full Text Available Purpose: This study explores the short-term value of integrated care for the frail elderly by evaluating the effects of the Walcheren Integrated Care Model on health, quality of life, health care use and satisfaction with care after three months.Intervention: Frailty was preventively detected in elderly living at home with the Groningen Frailty Indicator. Geriatric nurse practitioners and secondary care geriatric nursing specialists were assigned as case managers and co-ordinated the care agreed upon in a multidisciplinary meeting. The general practitioner practice functions as a single entry point and supervises the co-ordination of care. The intervention encompasses task reassignment between nurses and doctors and consultations between primary, secondary and tertiary care providers. The entire process was supported by multidisciplinary protocols and web-based patient files.Methods: The design of this study was quasi-experimental. In this study, 205 frail elderly patients of three general practitioner practices that implemented the integrated care model were compared with 212 frail elderly patients of five general practitioner practices that provided usual care. The outcomes were assessed using questionnaires. Baseline measures were compared with a three-month follow-up by chi-square tests, t-tests and regression analysis.Results and conclusion: In the short term, the integrated care model had a significant effect on the attachment aspect of quality of life. The frail elderly patients were better able to obtain the love and friendship they desire. The use of care did not differ despite the preventive element and the need for assessments followed up with case management in the integrated care model. In the short term, there were no significant changes in health. As frailty is a progressive state, it is assumed that three months are too short to influence changes in health with integrated care models. A more longitudinal approach is required

  19. The short-term effects of an integrated care model for the frail elderly on health, quality of life, health care use and satisfaction with care

    Directory of Open Access Journals (Sweden)

    Wilhelmina Mijntje Looman

    2014-12-01

    Full Text Available Purpose: This study explores the short-term value of integrated care for the frail elderly by evaluating the effects of the Walcheren Integrated Care Model on health, quality of life, health care use and satisfaction with care after three months. Intervention: Frailty was preventively detected in elderly living at home with the Groningen Frailty Indicator. Geriatric nurse practitioners and secondary care geriatric nursing specialists were assigned as case managers and co-ordinated the care agreed upon in a multidisciplinary meeting. The general practitioner practice functions as a single entry point and supervises the co-ordination of care. The intervention encompasses task reassignment between nurses and doctors and consultations between primary, secondary and tertiary care providers. The entire process was supported by multidisciplinary protocols and web-based patient files. Methods: The design of this study was quasi-experimental. In this study, 205 frail elderly patients of three general practitioner practices that implemented the integrated care model were compared with 212 frail elderly patients of five general practitioner practices that provided usual care. The outcomes were assessed using questionnaires. Baseline measures were compared with a three-month follow-up by chi-square tests, t-tests and regression analysis. Results and conclusion: In the short term, the integrated care model had a significant effect on the attachment aspect of quality of life. The frail elderly patients were better able to obtain the love and friendship they desire. The use of care did not differ despite the preventive element and the need for assessments followed up with case management in the integrated care model. In the short term, there were no significant changes in health. As frailty is a progressive state, it is assumed that three months are too short to influence changes in health with integrated care models. A more longitudinal approach is

  20. A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

    Directory of Open Access Journals (Sweden)

    Zeng Jie

    2016-01-01

    Full Text Available Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.

  1. Hemispheric specialisation in selective attention and short-term memory: A fine-coarse model of left and right ear disadvantages

    Directory of Open Access Journals (Sweden)

    John E. Marsh

    2013-12-01

    Full Text Available Serial short-term memory is impaired by irrelevant sound, particularly when the sound changes acoustically. This acoustic effect is larger when the sound is presented to the left compared to the right ear (a left-ear disadvantage. Serial memory appears relatively insensitive to distraction from the semantic properties of a background sound. In contrast, short-term free recall of semantic-category exemplars is impaired by the semantic properties of background speech and relatively insensitive to the sound’s acoustic properties. This semantic effect is larger when the sound is presented to the right compared to the left ear (a right-ear disadvantage. In this paper, we outline a speculative neurocognitive fine-coarse model of these hemispheric differences in relation to short-term memory and selective attention, and explicate empirical directions in which this model can be critically evaluated.

  2. An Exemplar-Familiarity Model Predicts Short-Term and Long-Term Probe Recognition across Diverse Forms of Memory Search

    Science.gov (United States)

    Nosofsky, Robert M.; Cox, Gregory E.; Cao, Rui; Shiffrin, Richard M.

    2014-01-01

    Experiments were conducted to test a modern exemplar-familiarity model on its ability to account for both short-term and long-term probe recognition within the same memory-search paradigm. Also, making connections to the literature on attention and visual search, the model was used to interpret differences in probe-recognition performance across…

  3. An Exemplar-Familiarity Model Predicts Short-Term and Long-Term Probe Recognition across Diverse Forms of Memory Search

    Science.gov (United States)

    Nosofsky, Robert M.; Cox, Gregory E.; Cao, Rui; Shiffrin, Richard M.

    2014-01-01

    Experiments were conducted to test a modern exemplar-familiarity model on its ability to account for both short-term and long-term probe recognition within the same memory-search paradigm. Also, making connections to the literature on attention and visual search, the model was used to interpret differences in probe-recognition performance across…

  4. Short-term prediction of UT1-UTC by combination of the grey model and neural networks

    Science.gov (United States)

    Lei, Yu; Guo, Min; Hu, Dan-dan; Cai, Hong-bing; Zhao, Dan-ning; Hu, Zhao-peng; Gao, Yu-ping

    2017-01-01

    UT1-UTC predictions especially short-term predictions are essential in various fields linked to reference systems such as space navigation and precise orbit determinations of artificial Earth satellites. In this paper, an integrated model combining the grey model GM(1, 1) and neural networks (NN) are proposed for predicting UT1-UTC. In this approach, the effects of the Solid Earth tides and ocean tides together with leap seconds are first removed from observed UT1-UTC data to derive UT1R-TAI. Next the derived UT1R-TAI time-series are de-trended using the GM(1, 1) and then residuals are obtained. Then the residuals are used to train a network. The subsequently predicted residuals are added to the GM(1, 1) to obtain the UT1R-TAI predictions. Finally, the predicted UT1R-TAI are corrected for the tides together with leap seconds to obtain UT1-UTC predictions. The daily values of UT1-UTC between January 7, 2010 and August 6, 2016 from the International Earth Rotation and Reference Systems Service (IERS) 08 C04 series are used for modeling and validation of the proposed model. The results of the predictions up to 30 days in the future are analyzed and compared with those by the GM(1, 1)-only model and combination of the least-squares (LS) extrapolation of the harmonic model including the linear part, annual and semi-annual oscillations and NN. It is found that the proposed model outperforms the other two solutions. In addition, the predictions are compared with those from the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) lasting from October 1, 2005 to February 28, 2008. The results show that the prediction accuracy is inferior to that of those methods taking into account atmospheric angular momentum (AAM), i.e., Kalman filter and adaptive transform from AAM to LODR, but noticeably better that of the other existing methods and techniques, e.g., autoregressive filtering and least-squares collocation.

  5. An exemplar-familiarity model predicts short-term and long-term probe recognition across diverse forms of memory search.

    Science.gov (United States)

    Nosofsky, Robert M; Cox, Gregory E; Cao, Rui; Shiffrin, Richard M

    2014-11-01

    Experiments were conducted to test a modern exemplar-familiarity model on its ability to account for both short-term and long-term probe recognition within the same memory-search paradigm. Also, making connections to the literature on attention and visual search, the model was used to interpret differences in probe-recognition performance across diverse conditions that manipulated relations between targets and foils across trials. Subjects saw lists of from 1 to 16 items followed by a single item recognition probe. In a varied-mapping condition, targets and foils could switch roles across trials; in a consistent-mapping condition, targets and foils never switched roles; and in an all-new condition, on each trial a completely new set of items formed the memory set. In the varied-mapping and all-new conditions, mean correct response times (RTs) and error proportions were curvilinear increasing functions of memory set size, with the RT results closely resembling ones from hybrid visual-memory search experiments reported by Wolfe (2012). In the consistent-mapping condition, new-probe RTs were invariant with set size, whereas old-probe RTs increased slightly with increasing study-test lag. With appropriate choice of psychologically interpretable free parameters, the model accounted well for the complete set of results. The work provides support for the hypothesis that a common set of processes involving exemplar-based familiarity may govern long-term and short-term probe recognition across wide varieties of memory- search conditions.

  6. Empirical evidence for musical syntax processing? Computer simulations reveal the contribution of auditory short-term memory

    Directory of Open Access Journals (Sweden)

    Emmanuel eBigand

    2014-06-01

    Full Text Available During the last decade, it has been argued that 1 music processing involves syntactic representations similar to those observed in language, and 2 that music and language share similar syntactic-like processes and neural resources. This claim is important for understanding the origin of music and language abilities and, furthermore, it has clinical implications. The Western musical system, however, is rooted in psychoacoustic properties of sound, and this is not the case for linguistic syntax. Accordingly, musical syntax processing could be parsimoniously understood as an emergent property of auditory memory rather than a property of abstract processing similar to linguistic processing. To support this view, we simulated numerous empirical studies that investigated the processing of harmonic structures, using a model based on the accumulation of sensory information in auditory memory. The simulations revealed that most of the musical syntax manipulations used with behavioral and neurophysiological methods as well as with developmental and cross-cultural approaches can be accounted for by the auditory memory model. This led us to question whether current research on musical syntax can really be compared with linguistic processing. Our simulation also raises methodological and theoretical challenges to study musical syntax while disentangling the confounded low-level sensory influences. In order to investigate syntactic abilities in music comparable to language, research should preferentially use musical material with structures that circumvent the tonal effect exerted by psychoacoustic properties of sounds.

  7. Empirical evidence for musical syntax processing? Computer simulations reveal the contribution of auditory short-term memory.

    Science.gov (United States)

    Bigand, Emmanuel; Delbé, Charles; Poulin-Charronnat, Bénédicte; Leman, Marc; Tillmann, Barbara

    2014-01-01

    During the last decade, it has been argued that (1) music processing involves syntactic representations similar to those observed in language, and (2) that music and language share similar syntactic-like processes and neural resources. This claim is important for understanding the origin of music and language abilities and, furthermore, it has clinical implications. The Western musical system, however, is rooted in psychoacoustic properties of sound, and this is not the case for linguistic syntax. Accordingly, musical syntax processing could be parsimoniously understood as an emergent property of auditory memory rather than a property of abstract processing similar to linguistic processing. To support this view, we simulated numerous empirical studies that investigated the processing of harmonic structures, using a model based on the accumulation of sensory information in auditory memory. The simulations revealed that most of the musical syntax manipulations used with behavioral and neurophysiological methods as well as with developmental and cross-cultural approaches can be accounted for by the auditory memory model. This led us to question whether current research on musical syntax can really be compared with linguistic processing. Our simulation also raises methodological and theoretical challenges to study musical syntax while disentangling the confounded low-level sensory influences. In order to investigate syntactic abilities in music comparable to language, research should preferentially use musical material with structures that circumvent the tonal effect exerted by psychoacoustic properties of sounds.

  8. Empirical evidence for musical syntax processing? Computer simulations reveal the contribution of auditory short-term memory

    Science.gov (United States)

    Bigand, Emmanuel; Delbé, Charles; Poulin-Charronnat, Bénédicte; Leman, Marc; Tillmann, Barbara

    2014-01-01

    During the last decade, it has been argued that (1) music processing involves syntactic representations similar to those observed in language, and (2) that music and language share similar syntactic-like processes and neural resources. This claim is important for understanding the origin of music and language abilities and, furthermore, it has clinical implications. The Western musical system, however, is rooted in psychoacoustic properties of sound, and this is not the case for linguistic syntax. Accordingly, musical syntax processing could be parsimoniously understood as an emergent property of auditory memory rather than a property of abstract processing similar to linguistic processing. To support this view, we simulated numerous empirical studies that investigated the processing of harmonic structures, using a model based on the accumulation of sensory information in auditory memory. The simulations revealed that most of the musical syntax manipulations used with behavioral and neurophysiological methods as well as with developmental and cross-cultural approaches can be accounted for by the auditory memory model. This led us to question whether current research on musical syntax can really be compared with linguistic processing. Our simulation also raises methodological and theoretical challenges to study musical syntax while disentangling the confounded low-level sensory influences. In order to investigate syntactic abilities in music comparable to language, research should preferentially use musical material with structures that circumvent the tonal effect exerted by psychoacoustic properties of sounds. PMID:24936174

  9. Verbal learning on depressive pseudodementia: accentuate impairment of free recall, moderate on learning processes, and spared short-term and recognition memory

    Directory of Open Access Journals (Sweden)

    Jonas Jardim de Paula

    2013-09-01

    Full Text Available Objective Depressive pseudodementia (DPD is a clinical condition characterized by depressive symptoms followed by cognitive and functional impairment characteristics of dementia. Memory complaints are one of the most related cognitive symptoms in DPD. The present study aims to assess the verbal learning profile of elderly patients with DPD. Methods Ninety-six older adults (34 DPD and 62 controls were assessed by neuropsychological tests including the Rey auditory-verbal learning test (RAVLT. A multivariate general linear model was used to assess group differences and controlled for demographic factors. Results Moderate or large effects were found on all RAVLT components, except for short-term and recognition memory. Conclusion DPD impairs verbal memory, with large effect size on free recall and moderate effect size on the learning. Short-term storage and recognition memory are useful in clinical contexts when the differential diagnosis is required.

  10. THE PROCESS OF SHORT-TERM AND LONG-TERM PRICE INTEGRATION IN THE BENIN MAIZE MARKET

    NARCIS (Netherlands)

    LUTZ, C; VANTILBURG, A; VANDERKAMP, BJ

    1995-01-01

    This paper reviews the methodology used to study the price integration process in spatially separated spot markets, and applies if to the Benin maize market. An Autoregressive Distributed Lag Model is derived to take into account the sluggishness of price adjustments. Hypothesis testing concerns sta

  11. Comparison of the Short-Term Forecasting Accuracy on Battery Electric Vehicle between Modified Bass and Lotka-Volterra Model: A Case Study of China

    Directory of Open Access Journals (Sweden)

    Shunxi Li

    2017-01-01

    Full Text Available The potential demand of battery electric vehicle (BEV is the base of the decision-making to the government policy formulation, enterprise manufacture capacity expansion, and charging infrastructure construction. How to predict the future amount of BEV accurately is very important to the development of BEV both in practice and in theory. The present paper tries to compare the short-term accuracy of a proposed modified Bass model and Lotka-Volterra (LV model, by taking China’s BEV development as the case study. Using the statistics data of China’s BEV amount of 21 months from Jan 2015 to Sep 2016, we compare the simulation accuracy based on the value of mean absolute percentage error (MAPE and discuss the forecasting capacity of the two models according to China’s government expectation. According to the MAPE value, the two models have good prediction accuracy, but the Bass model is more accurate than LV model. Bass model has only one dimension and focuses on the diffusion trend, while LV model has two dimensions and mainly describes the relationship and competing process between the two populations. In future research, the forecasting advantages of Bass model and LV model should be combined to get more accurate predicting effect.

  12. Short-Term Memory Performance in 7- and 8-Year-Old Children: The Relationship Between Phonological and Pitch Processing.

    Science.gov (United States)

    Flagge, Ashley Gaal; Estis, Julie M; Moore, Robert E

    2016-10-01

    The relationship between short-term memory for phonology and pitch was explored by examining accuracy scores for typically developing children for 5 experimental tasks: immediate nonword repetition (NWR), nonword repetition with an 8-s silent interference (NWRS), pitch discrimination (PD), pitch discrimination with an 8-s silent interference (PDS), and pitch matching (PM). Thirty-six 7- and 8-year-old children (21 girls, 15 boys) with normal hearing, language, and cognition were asked to listen to and repeat nonsense words (NWR, NWRS), make a same versus different decision between 2 tones (PD, PDS), and listen to and then vocally reproduce a tone (PM). Results showed no significant correlations between tasks of phonological memory and tests of pitch memory, that participants scored significantly better on nonword repetition tasks than PD and PM tasks, and that participants performed significantly better on tasks with no silent interference. These findings suggest that, for typically developing children, pitch may be stored and rehearsed in a separate location than phonological information. Because of fundamental task differences, further research is needed to corroborate these data and determine the presence of developmental effects and neuroanatomical locations where a potential language/music overlap is occurring in children.

  13. Development of a short-term irradiance prediction system using post-processing tools on WRF-ARW meteorological forecasts in Spain

    Science.gov (United States)

    Rincón, A.; Jorba, O.; Baldasano, J. M.

    2010-09-01

    The increased contribution of solar energy in power generation sources requires an accurate estimation of surface solar irradiance conditioned by geographical, temporal and meteorological conditions. The knowledge of the variability of these factors is essential to estimate the expected energy production and therefore help stabilizing the electricity grid and increase the reliability of available solar energy. The use of numerical meteorological models in combination with statistical post-processing tools may have the potential to satisfy the requirements for short-term forecasting of solar irradiance for up to several days ahead and its application in solar devices. In this contribution, we present an assessment of a short-term irradiance prediction system based on the WRF-ARW mesoscale meteorological model (Skamarock et al., 2005) and several post-processing tools in order to improve the overall skills of the system in an annual simulation of the year 2004 in Spain. The WRF-ARW model is applied with 4 km x 4 km horizontal resolution and 38 vertical layers over the Iberian Peninsula. The hourly model irradiance is evaluated against more than 90 surface stations. The stations are used to assess the temporal and spatial fluctuations and trends of the system evaluating three different post-processes: Model Output Statistics technique (MOS; Glahn and Lowry, 1972), Recursive statistical method (REC; Boi, 2004) and Kalman Filter Predictor (KFP, Bozic, 1994; Roeger et al., 2003). A first evaluation of the system without post-processing tools shows an overestimation of the surface irradiance, due to the lack of atmospheric absorbers attenuation different than clouds not included in the meteorological model. This produces an annual BIAS of 16 W m-2 h-1, annual RMSE of 106 W m-2 h-1 and annual NMAE of 42%. The largest errors are observed in spring and summer, reaching RMSE of 350 W m-2 h-1. Results using Kalman Filter Predictor show a reduction of 8% of RMSE, 83% of BIAS

  14. The short-term effects of an integrated care model for the frail elderly on health, quality of life, health care use and satisfaction with care

    NARCIS (Netherlands)

    W.M. Looman (Willemijn); I.N. Fabbricotti (Isabelle); R. Huijsman (Robbert)

    2014-01-01

    markdownabstract__Abstract__ Purpose: This study explores the short-term value of integrated care for the frail elderly by evaluating the effects of the Walcheren Integrated Care Model on health, quality of life, health care use and satisfaction with care after three months. Intervention: Frailty w

  15. Domain-Generality of Timing-Based Serial Order Processes in Short-Term Memory: New Insights from Musical and Verbal Domains.

    Science.gov (United States)

    Gorin, Simon; Kowialiewski, Benjamin; Majerus, Steve

    2016-01-01

    Several models in the verbal domain of short-term memory (STM) consider a dissociation between item and order processing. This view is supported by data demonstrating that different types of time-based interference have a greater effect on memory for the order of to-be-remembered items than on memory for the items themselves. The present study investigated the domain-generality of the item versus serial order dissociation by comparing the differential effects of time-based interfering tasks, such as rhythmic interference and articulatory suppression, on item and order processing in verbal and musical STM domains. In Experiment 1, participants had to maintain sequences of verbal or musical information in STM, followed by a probe sequence, this under different conditions of interference (no-interference, rhythmic interference, articulatory suppression). They were required to decide whether all items of the probe list matched those of the memory list (item condition) or whether the order of the items in the probe sequence matched the order in the memory list (order condition). In Experiment 2, participants performed a serial order probe recognition task for verbal and musical sequences ensuring sequential maintenance processes, under no-interference or rhythmic interference conditions. For Experiment 1, serial order recognition was not significantly more impacted by interfering tasks than was item recognition, this for both verbal and musical domains. For Experiment 2, we observed selective interference of the rhythmic interference condition on both musical and verbal order STM tasks. Overall, the results suggest a similar and selective sensitivity to time-based interference for serial order STM in verbal and musical domains, but only when the STM tasks ensure sequential maintenance processes.

  16. Chronic nicotine restores normal Aβ levels and prevents short-term memory and E-LTP impairment in Aβ rat model of Alzheimer's disease.

    Science.gov (United States)

    Srivareerat, Marisa; Tran, Trinh T; Salim, Samina; Aleisa, Abdulaziz M; Alkadhi, Karim A

    2011-05-01

    Alzheimer's disease (AD) is a devastating neurodegenerative disorder characterized by increased deposition of beta-amyloid (Aβ) peptides and progressive cholinergic dysfunction in regions of the brain involved in learning and memory processing. In AD, progressive accumulation of Aβ peptide impairs nicotinic acetylcholine receptor (nAChR) function by an unknown mechanism believed to involve α(7)- and α(4)β(2)-nAChR blockade. The three approaches of the current study evaluated the effects of chronic nicotine treatment in the prevention of Aβ-induced impairment of learning and short-term memory. Rat AD model was induced by 14-day i.c.v. osmotic pump infusion of a 1:1 mixture of 300 pmol/day Aβ(1-40)/Aβ(1-42) or Aβ(40-1) (inactive peptide, control). The effect of nicotine (2 mg/(kg day)) on Aβ-induced spatial learning and memory impairments was assessed by evaluation of performance in the radial arm water maze (RAWM), in vivo electrophysiological recordings of early-phase long-term potentiation (E-LTP) in urethane-anesthetized rats, and immunoblot analysis to determine changes in the levels of beta-site amyloid precursor protein (APP)-cleaving enzyme (BACE), Aβ and memory-related proteins. The results indicate that 6 weeks of nicotine treatment reduced the levels of Aβ(1-40) and BACE1 peptides in hippocampal area CA1 and prevented Aβ-induced impairment of learning and short-term memory. Chronic nicotine also prevented the Aβ-induced inhibition of basal synaptic transmission and LTP in hippocampal area CA1. Furthermore, chronic nicotine treatment prevented the Aβ-induced reduction of α(7)- and α(4)-nAChR. These effects of nicotine may be due, at least in part, to upregulation of brain derived neurotropic factor (BDNF).

  17. Health economic modeling to assess short-term costs of maternal overweight, gestational diabetes, and related macrosomia - a pilot evaluation.

    Science.gov (United States)

    Lenoir-Wijnkoop, Irene; van der Beek, Eline M; Garssen, Johan; Nuijten, Mark J C; Uauy, Ricardo D

    2015-01-01

    Despite the interest in the impact of overweight and obesity on public health, little is known about the social and economic impact of being born large for gestational age or macrosomic. Both conditions are related to maternal obesity and/or gestational diabetes mellitus (GDM) and associated with increased morbidity for mother and child in the perinatal period. Poorly controlled diabetes during pregnancy, pre- pregnancy maternal obesity and/or excessive maternal weight gain during pregnancy are associated with intermittent periods of fetal exposure to hyperglycemia and subsequent hyperinsulinemia, leading to increased birth weight (e.g., macrosomia), body adiposity, and glycogen storage in the liver. Macrosomia is associated with an increased risk of developing obesity and type 2 diabetes mellitus later in life. Provide insight in the short-term health-economic impact of maternal overweight, GDM, and related macrosomia. To this end, a health economic framework was designed. This pilot study also aims to encourage further health technology assessments, based on country- and population-specific data. The estimation of the direct health-economic burden of maternal overweight, GDM and related macrosomia indicates that associated healthcare expenditures are substantial. The calculation of a budget impact of GDM, based on a conservative approach of our model, using USA costing data, indicates an annual cost of more than $1,8 billion without taking into account long-term consequences. Although overweight and obesity are a recognized concern worldwide, less attention has been given to the health economic consequences of these conditions in women of child-bearing age and their offspring. The presented outcomes underline the need for preventive management strategies and public health interventions on life style, diet and physical activity. Also, the predisposition in people of Asian ethnicity to develop diabetes emphasizes the urgent need to collect more country

  18. An autoregressive integrated moving average model for short-term prediction of hepatitis C virus seropositivity among male volunteer blood donors in Karachi, Pakistan

    Institute of Scientific and Technical Information of China (English)

    Saeed Akhtar; Shafquat Rozi

    2009-01-01

    AIM: To identify the stochastic autoregressive integrated moving average (ARIMA) model for short term forecasting of hepatitis C virus (HCV) seropositivity among volunteer blood donors in Karachi, Pakistan. METHODS: Ninety-six months (1998-2005) data on volunteer blood donors tested at four major blood banks in Karachi, Pakistan were subjected to ARIMA modeling. Subsequently, a fitted ARIMA model was used to forecast HCV seropositive donors for 91-96 mo to contrast with observed series of the same months. To assess the forecast accuracy, the mean absolute error rate (%) between the observed and predicted HCV seroprevalence was calculated. Finally, a fitted ARIMA model was used for short-term forecasts beyond the observed series. RESULTS: The goodness-of-fit test of the optimum ARIMA (2,1,7) model showed non- s igni f icant autocorrelations in the residuals of the model. The forecasts by ARIMA for 91-96 mo closely followed the pattern of observed series for the same months, with mean monthly absolute forecast errors (%) over 6 mo of 6.5%. The short-term forecasts beyond the observed series adequately captured the pattern in the data and showed increasing tendency of HCV seropositivity with CONCLUSION: To curtail HCV spread, public health authorities need to educate communities and health care providers about HCV transmission routes based on known HCV epidemiology in Pakistan and its neighboring countries. Future research may focus on factors associated with hyperendemic levels of HCV infection.

  19. Short-term dispersal of Fukushima-derived radionuclides off Japan: modeling efforts and model-data intercomparison

    Directory of Open Access Journals (Sweden)

    I. I. Rypina

    2013-07-01

    Full Text Available The Great East Japan Earthquake and tsunami that caused a loss of power at the Fukushima nuclear power plants (FNPP resulted in emission of radioactive isotopes into the atmosphere and the ocean. In June of 2011, an international survey measuring a variety of radionuclide isotopes, including 137Cs, was conducted in surface and subsurface waters off Japan. This paper presents the results of numerical simulations specifically aimed at interpreting these observations and investigating the spread of Fukushima-derived radionuclides off the coast of Japan and into the greater Pacific Ocean. Together, the simulations and observations allow us to study the dominant mechanisms governing this process, and to estimate the total amount of radionuclides in discharged coolant waters and atmospheric airborne radionuclide fallout. The numerical simulations are based on two different ocean circulation models, one inferred from AVISO altimetry and NCEP/NCAR reanalysis wind stress, and the second generated numerically by the NCOM model. Our simulations determine that > 95% of 137Cs remaining in the water within ~600 km of Fukushima, Japan in mid-June 2011 was due to the direct oceanic discharge. The estimated strength of the oceanic source is 16.2 ± 1.6 PBq, based on minimizing the model-data mismatch. We cannot make an accurate estimate for the atmospheric source strength since most of the fallout cesium had left the survey area by mid-June. The model explained several key features of the observed 137Cs distribution. First, the absence of 137Cs at the southernmost stations is attributed to the Kuroshio Current acting as a transport barrier against the southward progression of 137Cs. Second, the largest 137Cs concentrations were associated with a semi-permanent eddy that entrained 137Cs-rich waters, collecting and stirring them around the eddy perimeter. Finally, the intermediate 137Cs concentrations at the westernmost stations are attributed to younger, and

  20. Short-term dispersal of Fukushima-derived radionuclides off Japan: modeling efforts and model-data intercomparison

    Directory of Open Access Journals (Sweden)

    I. I. Rypina

    2013-01-01

    Full Text Available The March of 2011 earthquake and tsunami that caused a loss of power at the Fukushima nuclear power plants (FNPP resulted in emission of radioactive isotopes into the atmosphere and the ocean. In June of 2011, an international survey of various radionuclide isotopes, including 137Cs, was conducted in surface and subsurface waters off Japan. This paper presents the results of numerical simulations aimed at interpreting these observations, investigating the spread of Fukushima-derived radionuclides off the coast of Japan and into the greater Pacific Ocean, studying the dominant mechanisms governing this process, as well as estimating the total amount of radionuclides in discharged coolant waters and atmospheric airborne radionuclide fallout. The numerical simulations are based on two different ocean circulation models, one inferred from AVISO altimetry and NCEP/NCAR reanalysis wind stress, and the second generated numerically by the NCOM model. Our simulations determine that >95% of 137Cs remaining in the water within ~600 km of Fukushima, Japan in mid-June 2011 was due to the direct oceanic discharge. The estimated strength of the oceanic source is 16.2 ± 1.6 PBq, based on minimizing the model-data mismatch. We cannot make an accurate estimate for the atmospheric source strength since most of the fallout cesium would have moved out of the survey area by mid-June. The model explained several features of the observed 137Cs distribution. First, the absence of 137Cs at the southernmost stations is attributed to the Kuroshio Current acting as a transport barrier against the southward progression of 137Cs. Second, the largest 137Cs concentrations were associated with a semi-permanent eddy that entrained 137Cs-rich waters collecting and stirring them around the eddy perimeter. Finally, the intermediate 137Cs concentrations at the westernmost stations were attributed

  1. The NASA Short-term Prediction Research and Transition (SPoRT) Center: A Collaborative Model for Accelerating Research into Operations

    Science.gov (United States)

    Goodman, S. J.; Lapenta, W.; Jedlovec, G.; Dodge, J.; Bradshaw, T.

    2003-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, Alabama was created to accelerate the infusion of NASA earth science observations, data assimilation and modeling research into NWS forecast operations and decision-making. The principal focus of experimental products is on the regional scale with an emphasis on forecast improvements on a time scale of 0-24 hours. The SPoRT Center research is aligned with the regional prediction objectives of the US Weather Research Program dealing with 0-1 day forecast issues ranging from convective initiation to 24-hr quantitative precipitation forecasting. The SPoRT Center, together with its other interagency partners, universities, and the NASA/NOAA Joint Center for Satellite Data Assimilation, provides a means and a process to effectively transition NASA Earth Science Enterprise observations and technology to National Weather Service operations and decision makers at both the global/national and regional scales. This paper describes the process for the transition of experimental products into forecast operations, current products undergoing assessment by forecasters, and plans for the future.

  2. Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model

    Institute of Scientific and Technical Information of China (English)

    LI Xiaodong; ZENG Guangming; HUANG Guohe; LI Jianbing; JIANG Ru

    2007-01-01

    By predicting influent quantity,a wastewater treatment plant (WWTP) can be well controlled.The nonlinear dynamic characteristic of WWTP influent quantity time series was analyzed,with the assumption that the series was predictable.Based on this,a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction.Reasonable forecasting results were achieved using this method.

  3. Simulation of Short-term Wind Speed Forecast Errors using a Multi-variate ARMA(1,1) Time-series Model

    OpenAIRE

    Boone, Andrew

    2005-01-01

    The short-term (1 to 48 hours) predictability of wind power production from wind power plants in a power system is critical to the value of wind power. Advanced wind power prediction tools, based on numerical weather prediction models and designed for power system operators, are being developed and continuously improved. One objective of the EU-supported WILMAR (Wind power Integration in Liberalised electricity MARkets) project is to simulate the stochastic optimization of the operation of th...

  4. Simulation of Short-Term High-Temperature Impact Processes by Using a High-Energy Laser Beam

    Science.gov (United States)

    Ebert, M.; Hecht, L.; Hamann, C.

    2016-08-01

    This study applies high-energy laser beam experiments to better understand the high-temperature chemical interaction processes between iron meteorite projectiles and siliceous target material analog to meteorite impacts.

  5. Short-term and long-term effects of Zn (II) on the microbial activity and sludge property of partial nitrification process.

    Science.gov (United States)

    Zhang, Xiaojing; Zhou, Yue; Zhang, Nan; Zheng, Kaiwei; Wang, Lina; Han, Guanglu; Zhang, Hongzhong

    2017-03-01

    Autotrophic nitrogen removal was an innovative and economical nitrogen removal technology with less oxygen and no organics consumption, in which partial nitrification (PN) is the key component. It is necessary to clear the impact of metal ions on PN since the development of industry increased their opportunity for entering into wastewater. In this study, PN process was successfully started-up in an SBR, the short-term and long-term effects of Zn (II) on microbial bioactivity and the sludge adsorption ability for Zn (II) were investigated. Results suggested that low Zn (II) were favorable for AOB bioactivity, while the long-term effect also induced NOB bioactivity. The suppression threshold of Zn (II) on AOB in short-term effect was 10mgL(-1), which rose to 50mgL(-1) in the long-term effect due to the self-adaption. The PN sludge presented prominent absorbability for zinc and performed a quadratic relation with the Zn (II) concentration.

  6. Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

    Directory of Open Access Journals (Sweden)

    Che-Jung Chang

    2013-01-01

    Full Text Available The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1 grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.

  7. Simulation of short-term pressure regulation during the tilt test in a coupled 3D-0D closed-loop model of the circulation.

    Science.gov (United States)

    Lau, Kevin D; Figueroa, C Alberto

    2015-08-01

    Short-term fluctuations in arterial pressures arising from normal physiological function are buffered by a negative feedback system known as the arterial baroreflex. Initiated by altered biomechanical stretch in the vessel wall, the baroreflex coordinates a systemic response that alters heart rate, cardiac contractility and peripheral vessel vasoconstriction. In this work, a coupled 3D-0D formulation for the short-term pressure regulation of the systemic circulation is presented. Including the baroreflex feedback mechanisms, a patient-specific model of the large arteries is subjected to a simulated head up tilt test. Comparative simulations with and without baroreflex control highlight the critical role that the baroreflex has in regulating variations in pressures within the systemic circulation.

  8. MEDSLIK-II, a Lagrangian marine oil spill model for short-term forecasting – Part 1: Theory

    Directory of Open Access Journals (Sweden)

    M. De Dominicis

    2013-03-01

    Full Text Available The processes of transport, diffusion and transformation of surface oil in seawater can be simulated using a Lagrangian model formalism coupled with Eulerian circulation models. This paper describes the formalism and the conceptual assumptions of a Lagrangian marine oil slick numerical model and re-writes the constitutive equations in a modern mathematical framework. The Lagrangian numerical representation of the oil slick requires three different state variables: the slick, the particle and the structural state variables. Transformation processes (evaporation, spreading, dispersion and coastal adhesion act on the slick state variables, while particles variables are used to model the transport and diffusion processes. The slick and particle variables are recombined together to compute the oil concentration in water, a structural state variable. The mathematical and numerical formulation of oil transport, diffusion and transformation processes described in this paper, together with the many simplifying hypothesis and parameterizations, form the basis of a new, open source Lagrangian surface oil spill model, so-called MEDSLIK-II. Part 2 of this paper describes the applications of MEDSLIK-II to oil spill simulations that allow the validation of the model results and the study of the sensitivity of the simulated oil slick to different model numerical parameterizations.

  9. Identity processes and coping strategies in college students: short-term longitudinal dynamics and the role of personality.

    Science.gov (United States)

    Luyckx, Koen; Klimstra, Theo A; Duriez, Bart; Schwartz, Seth J; Vanhalst, Janne

    2012-09-01

    Coping strategies and identity processes are hypothesized to influence one another over time. This three-wave longitudinal study (N = 458; 84.9% women) examined, for the first time, how and to what extent identity processes (i.e., commitment making, identification with commitment, exploration in breadth, exploration in depth, and ruminative exploration) and coping strategies (i.e., problem solving, social support seeking, and avoidance) predicted one another over time. Cross-lagged analyses indicated that processes of identity exploration seemed especially to be intertwined with different coping strategies over time, suggesting that identity exploration may resemble problem-solving behavior on the pathway to an achieved identity. Commitment processes were found to be influenced by certain coping strategies, although identification with commitment also negatively influenced avoidance coping. These temporal sequences remained significant when controlling for baseline levels of Big Five personality traits. Hence, evidence was obtained for reciprocal pathways indicating that coping strategies and identity processes reinforce one another over time in college students.

  10. Working Memory and Arithmetic Calculation in Children: The Contributory Roles of Processing Speed, Short-Term Memory, and Reading

    Science.gov (United States)

    Berg, Derek H.

    2008-01-01

    The cognitive underpinnings of arithmetic calculation in children are noted to involve working memory; however, cognitive processes related to arithmetic calculation and working memory suggest that this relationship is more complex than stated previously. The purpose of this investigation was to examine the relative contributions of processing…

  11. Neural markers of age-related reserve and decline in visual processing speed and visual short-term memory capacity

    DEFF Research Database (Denmark)

    Wiegand, Iris

    2013-01-01

    Attentional performance is assumed to be a major source of general cognitive abilities in older age. The present study aimed at identifying neural markers of preserved and declined basic visual attention functions in aging individuals. For groups of younger and older adults, we modeled general ca...

  12. A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion

    Institute of Scientific and Technical Information of China (English)

    Wen-yang DUAN; Li-min HUANG; Yang HAN; Ya-hui ZHANG; Shuo HUANG

    2015-01-01

    题目:用于非线性非平稳船舶运动极短期预报的一种复合自回归经验模态分解支持向量机回归模型  目的:基于支持向量机回归(SVR)模型在非线时间序列的预测能力及经验模态分解(EMD)方法在处理非线性非平稳性的优势,提出一种复合自回归经验模态分解支持向量机回归(AR-EMD-SVR)模型,提高非线性非平稳船舶运动极短期预报精度。  创新点:1.研究非线性非平稳船舶运动的极短期预报问题,提出一种复合的预报方法;2.基于不同层次的预报模型和模型试验数据,分析非线性非平稳性对极短期预报精度的影响。  方法:1.在SVR模型中引入基于自回归(AR)预报端点延拓的 EMD 方法,形成复合的 AR-EMD-SVR 预报模型;2.基于集装箱船模水池试验运动数据将 AR-EMD-SVR 模型与 AR、SVR 和EMD-AR 三种模型进行比较,分析非线性非平稳性对极短期预报的影响以及不同模型的预报性能。  结论:1. AR-EMD 方法能够有效的克服非平稳对极短期预报模型(AR和 SVR)在精度上所带来的不良影响;2.基于船模试验数据的预报结果表明:相较于 AR、SVR 和 EMD-AR 三种预报模型,基于 AR-EMD-SVR模型的非线性非平稳船舶运动极短期预报结果具有更高的精度。%Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operations. Inspired by the satisfactory nonlinear learning capability of a support vector re-gression (SVR) model and the strong non-stationary processing ability of empirical mode decomposition (EMD), this paper develops a hybrid autoregressive (AR)-EMD-SVR model for the short-term forecast of nonlinear and non-stationary ship motion. The proposed hybrid model is designed by coupling the SVR model with an AR-EMD technique, which employs an AR model in ends

  13. Extension of recovery time from fatigue by repeated rest with short-term sleep during continuous fatigue load: Development of chronic fatigue model.

    Science.gov (United States)

    Kanzaki, Akinori; Okauchi, Takashi; Hu, Di; Shingaki, Tomotaka; Katayama, Yumiko; Koyama, Hidenori; Watanabe, Yasuyoshi; Cui, Yilong

    2016-05-01

    Homeostasis is known to be involved in maintaining the optimal internal environment, helping to achieve the best performance of biological functions. At the same time, a deviation from optimal conditions often attenuates the performance of biological functions, and such restricted performance could be considered as individual fatigue, including physical and mental fatigue. The present study seeks to develop an animal model of chronic or subacute fatigue in which the recovery time is extended through the gradual disruption of homeostasis. We show that repeated short-term rest periods with certain lengths of sleep during continuous fatigue loading extend recovery from spontaneous nighttime activity but not physical performance in comparison with a continuous fatigue-loading procedure. Furthermore, the immobility time in a forced swimming test was extended by repeated short-term rests. These results suggest that repeated short-term rest with certain lengths of sleep during continuous fatigue loading is able to extend the recovery from mental fatigue but not from physical fatigue and that this effect might occur via the disruption of a homeostatic mechanism that is involved in restoring the optimal internal environment.

  14. A Short Term Analogue Memory

    DEFF Research Database (Denmark)

    Shah, Peter Jivan

    1992-01-01

    A short term analogue memory is described. It is based on a well-known sample-hold topology in which leakage currents have been minimized partly by circuit design and partly by layout techniques. Measurements on a test chip implemented in a standard 2.4 micron analogue CMOS process show a droop...... rate of 0.075mV per second with a 1pF hold capacitor. This is equivalent to a retention time of approximately 1½ minute with 10 bits accuracy, assuming a full scale of +/-3.5V. It is expected that this can be improved by more than an order of magnitude by improving the layout of the hold capacitor...

  15. Short-term calving processes and ocean-ice interactions at Breidamerkurjökull Glacier, Southeast Iceland

    Science.gov (United States)

    Tinder, P. C.; Howat, I. M.

    2011-12-01

    While iceberg calving is often the principal source of mass loss for marine-terminating glaciers, these dynamics remain poorly represented in predictions of sea-level rise and large-scale climate models, requiring more robust observational datasets. Breidamerkurjökull glacier functions as a uniquely controlled field setting for obtaining a wide variety of environmental and geodetic measurements in conjunction with monitoring calving flux, making it possible to more carefully constrain the sometimes-contradictory relationships between calving and environmental conditions observed in previous studies. A time-lapse photography camera and water level logger were placed roughly 1/2 km from the glacier ice front to monitor ice loss and iceberg-generated tsunamis from April to September 2011. This record was used to estimate the volume of ice lost by calving during this period and obtain calving rates on hourly, daily, and weekly timescales. Weather, tide, and contemporaneous records of the temperature-salinity structure of the lagoon were used to examine relationships between these factors and calving.

  16. Intensive short-term dynamic psychotherapy for severe music performance anxiety: assessment, process, and outcome of psychotherapy with a professional orchestral musician.

    Science.gov (United States)

    Kenny, Dianna T; Arthey, Stephen; Abbass, Allan

    2014-03-01

    This paper reports on the process and outcome of therapy using intensive short-term dynamic psychotherapy (ISTDP) with a professional musician who had suffered severe music performance anxiety over the course of his entire 30-year career. In this paper, we describe the nature of the therapy, the case history of the musician, the first assessment and trial therapy session, and the course and successful outcome of therapy. The patient underwent 10 sessions of ISTDP over a period of 4 months. This paper reports on the first 6 sessions, which were most relevant to the understanding and treatment of the patient's severe music performance anxiety. This case study is the first reported application of ISTDP to a professional musician. We believe that this case study provides initial support that moderate to severe performance anxiety, in at least some cases, has its origins in unresolved complex emotions and defences arising from ruptures to early attachment relationships.

  17. Numerical modeling of short-term slow slip events in the Shikoku region considering the effect of earth tides and plate configuration

    Science.gov (United States)

    Matsuzawa, T.; Tanaka, Y.; Shibazaki, B.

    2016-12-01

    Several studies reported that occurrence of slow slip events (SSEs) in the Nankai region is affected by earth tides (e.g., Nakata et al., 2008; Ide and Tanaka, 2014; Yabe et al., 2015). The tidal effect on the SSEs is also examined by numerical studies (e.g., Hawthorne and Rubin, 2013). In our previous study, repeating SSEs in the Shikoku region are numerically reproduced, incorporating the actual plate configuration (Matsuzawa et al., 2013). In this study, we examined the behavior of SSEs in the Shikoku region, considering stress perturbation by earth tides. Our numerical model is similar to our previous study (Matsuzawa et al., 2013). A plate interface is expressed by small triangular elements. A rate- and state-dependent friction law (RS-law) with cutoff velocities is adopted as the friction law on each element. We assumed that (a-b) value in the RS-law is negative within the short-term SSE region, and positive outside the region. The short-term SSE region is based on the actual distribution of low-frequency tremor. Low effective normal stress is assumed at the depth of short-term SSEs. Calculating stress change by earth tides as in Yabe et al., (2015), we assume that the stress change is represented by periods of 10 major tides. Incorporating this stress perturbation, we calculate the evolution of slip on the plate interface. In the numerical result, repeating short-term SSEs are reproduced in the short-term SSE region. Recurrent intervals of SSEs at an isolated patch (e.g., northeastern Shikoku) have small fluctuation. Introducing tidal effect, peak velocity becomes faster than that in the case without tidal effect. On the other hand, the difference of peak velocities is not clear between the cases with and without tidal effect at widely connected SSE region (e.g., western Shikoku), as the intervals and peak velocities of SSEs are largely fluctuated in both cases. Hirahara (2016) suggested that the recurrence interval of events is synchronized to the period of

  18. A model study of the response of mesospheric ozone to short-term solar ultraviolet flux variations

    Science.gov (United States)

    Summers, M. E.; Bevilacqua, R. M.; Strobel, D. F.; Zhu, Xun; Deland, M. T.; Allen, M.; Keating, G. M.

    1990-01-01

    An investigation is conducted in order to determine the relative importance of several modeled processes in controlling the magnitude and phase of the mesospheric ozone response. A detailed one-dimensional modeling study of the mesospheric ozone response to solar UV flux variations is conducted to remove some of the deficiencies in previous studies. This study is also used to examine specifically the importance of solar zenith angle, self-consistent calculation of water vapor abundance, and temperature feedback with a nonlocal thermodynamic equilibrium radiation model. The photochemical model is described, and the assumptions made for the purpose of comparing model results with the observed ozone response obtained from a statistical analysis of Solar Mesosphere Explorer data (Keating et al., 1987) are discussed. The numerical results for the theoretical ozone response are presented. The results of selected time-dependent calculations are considered to illustrate the degree to which a relatively simple model of the mesosphere is able to capture the major characteristics of the observed response.

  19. Empirical regional models for the short-term forecast of M3000F2 during not quiet geomagnetic conditions over Europe

    Directory of Open Access Journals (Sweden)

    M. Pietrella

    2013-10-01

    Full Text Available Twelve empirical local models have been developed for the long-term prediction of the ionospheric characteristic M3000F2, and then used as starting point for the development of a short-term forecasting empirical regional model of M3000F2 under not quiet geomagnetic conditions. Under the assumption that the monthly median measurements of M3000F2 are linearly correlated to the solar activity, a set of regression coefficients were calculated over 12 months and 24 h for each of 12 ionospheric observatories located in the European area, and then used for the long-term prediction of M3000F2 at each station under consideration. Based on the 12 long-term prediction empirical local models of M3000F2, an empirical regional model for the prediction of the monthly median field of M3000F2 over Europe (indicated as RM_M3000F2 was developed. Thanks to the IFELM_foF2 models, which are able to provide short-term forecasts of the critical frequency of the F2 layer (foF2STF up to three hours in advance, it was possible to considerer the Brudley–Dudeney algorithm as a function of foF2STF to correct RM_M3000F2 and thus obtain an empirical regional model for the short-term forecasting of M3000F2 (indicated as RM_M3000F2_BD up to three hours in advance under not quiet geomagnetic conditions. From the long-term predictions of M3000F2 provided by the IRI model, an empirical regional model for the forecast of the monthly median field of M3000F2 over Europe (indicated as IRI_RM_M3000F2 was derived. IRI_RM_M3000F2 predictions were modified with the Bradley–Dudeney correction factor, and another empirical regional model for the short-term forecasting of M3000F2 (indicated as IRI_RM_M3000F2_BD up to three hours ahead under not quiet geomagnetic conditions was obtained. The main results achieved comparing the performance of RM_M3000F2, RM_M3000F2_BD, IRI_RM_M3000F2, and IRI_RM_M3000F2_BD are (1 in the case of moderate geomagnetic activity, the Bradley–Dudeney correction

  20. Fine-scale refuges can buffer demographic and genetic processes against short-term climatic variation and disturbance: a 22-year case study of an arboreal marsupial.

    Science.gov (United States)

    Banks, Sam C; Lorin, Thibault; Shaw, Robyn E; McBurney, Lachlan; Blair, David; Blyton, Michaela D J; Smith, Annabel L; Pierson, Jennifer C; Lindenmayer, David B

    2015-08-01

    Ecological disturbance and climate are key drivers of temporal dynamics in the demography and genetic diversity of natural populations. Microscale refuges are known to buffer species' persistence against environmental change, but the effects of such refuges on demographic and genetic patterns in response to short-term environmental variation are poorly understood. We quantified demographic and genetic responses of mountain brushtail possums (Trichosurus cunninghami) to rainfall variability (1992-2013) and to a major wildfire. We hypothesized that there would be underlying differences in demographic and genetic processes between an unburnt mesic refuge and a topographically exposed zone that was burnt in 2009. Fire caused a 2-year decrease in survival in the burnt zone, but the population grew after the fire due to immigration, leading to increased expected heterozygosity. We documented a fire-related behavioural shift, where the rate of movement by individuals in the unburnt refuge to the burnt zone decreased after fire. Irrespective of the fire, there were long-term differences in demographic and genetic parameters between the mesic/unburnt refuge and the nonmesic/burnt zone. Survival was high and unaffected by rainfall in the refuge, but lower and rainfall-dependent in the nonmesic zone. Net movement of individuals was directional, from the mesic refuge to the nonmesic zone, suggesting fine-scale source-sink dynamics. There were higher expected heterozygosity (HE ) and temporal genetic stability in the refuge, but lower HE and marked temporal genetic structure in the exposed habitat, consistent with reduced generational overlap caused by elevated mortality and immigration. Thus, fine-scale refuges can mediate the short-term demographic and genetic effects of climate and ecological disturbance. © 2015 John Wiley & Sons Ltd.

  1. Links between long-term and short-term rheology of the lithosphere: insights from strike-slip fault modelling

    Science.gov (United States)

    Le Pourhiet, Laetitia

    2014-05-01

    The study of geodetic data across strike-slip fault zones is believed to play a key role in our understanding of the lithosphere mechanical behaviour. InSAR and GPS measurements permits to determine more and more accurately both large and rapid co-seismic displacements and the slower deformation associated with the inter-seismic and post-seismic phases of the earthquake cycle on continents. However, no modern geodetic observation spans a complete earthquake cycle for any single fault in the world. Understanding this time variability through modelling is therefore crucial to reconstruct a global pattern. It is non trivial to compare the effective parameters retrieved from the different simple models are used to extract effective parameters from the geodetic data. Using the popular visco-elastic relaxation model reaches two paradoxes: - the lower crust must be very strong in order to fit the data long after the earthquake and very weak to fit the data during the early post-seismic period. - the retrieved a mantle lithosphere viscosity is as weak as 10^17 - 10^20 Pa.s and differ significantly from those deduced from post glacial rebound models and long term geodynamic models requirements in order to generate self consistent plate tectonics. Rather than assuming that the rheology of the lithosphere changes with time scale, it would be preferable to go on quest for an Earth's lithosphere rheological model based on some simple physics, which would be equally valid at all time scale from inter-seismic to orogeny. 3D models of long term strain localisation in wrenching context show that localisation of strain across strike slip faults modifies locally the rheological architecture of the lithosphere and lead to some sort of structural weakening. That weakening occurs because as strain localises the "jelly sandwich" type lithosphere evolves self-consistently into a "banana split" type rheological structure. This strain localisation process is very efficient when the lower

  2. Short-term Celecoxib intervention is a safe and effective chemopreventive for gastric carcinogenesis based on a Mongolian gerbil model

    Institute of Scientific and Technical Information of China (English)

    Chao-Hung Kuo; Huang-Ming Hu; Pei-Yun Tsai; I-Chen Wu; Sheau-Fang Yang; Lin-Li Chang; Jaw-Yuan Wang; Chang-Ming Jan; Wen-Ming Wang; Deng-Chyang Wu

    2009-01-01

    AIM: To evaluate the optimal intervention point of a selective cyclooxygenase-2 (COX-2) inhibitor, Celecoxib, for inhibiting Helicobacter pylori ( H pylori)-associated gastric carcinogenesis in Mongolian gerbils (MGs).METHODS: One hundred and twelve MGs were divided into six groups (A-F). One hundred gerbils were inoculated with H pylori (groups A-E). Twelve gerbils were inoculated with vehicle broth only (group F). After 4 wk, they were given N'-methyl-N'-nitro-N-nitroso-guanidine (MNNG) (50 mg/mL) in the drinking water for 20 wk. In groups B-E, the animals were given the stock Celecoxib (10 mg/kg per day) diet from the 21st, 31st, 21st and 41st week respectively. The periods of administering Celecoxib were 30, 20, 20, and 15 wk respectively. On the 51st week, the animals were sacrificed for histological examination. Local PCNA expression was examined by the immunohistochemistry method. The expression of COX-2 protein was assessed by Western Blot. Analysisused the χ~2 test. The difference was regarded as significant when P value was less than 0.05. RESULTS: Seventeen percent (17/100) of H pyloriinfected MGs developed gastric cancer. All of these lesions were well-differentiated adenocarcinoma. The incidence rates of adenocarcinoma in groups A-F were 40%, 0%, 0%, 20%, 25%, and 0% respectively. The inflammatory scores were higher in group B than in other groups. There was no inflammatory response noted in group F. Celecoxib treatment resulted in a significant reduction in the proliferation of H pyloriinfected mucosal cells (groups B, C and D) ( P < 0.01). The expression of COX-2 protein was significantly attenuated in the groups which were Celecoxib-treated for more than 20 wk (groups B, C, D). The groups treated with Celecoxib had a significantly lower rate of advanced gastric cancer (34% vs 75%, P < 0.001) There were no sudden deaths in any of the groups.CONCLUSION: Short-term treatment with Celecoxib has an anti-carcinogenic effect, and resulted in less severe

  3. The interaction between short-term heat-treatment and the formability of an Al-Mg-Si alloy regarding deep drawing processes

    Science.gov (United States)

    Machhammer, M.; Sommitsch, C.

    2016-11-01

    Research conducted in recent years has shown that heat-treatable Al-Mg-Si alloys (6xxx) have great potential concerning the design of lightweight car bodies. Compared to conventional deep drawing steels the field of application is limited by a lower formability. In order to minimize the disadvantage of a lower drawability a short-term heat-treatment (SHT) can be applied before the forming process. The SHT, conducted in selected areas on the initial blank, leads to a local reduction of strength aiming at the decrease of critical stress during the deep drawing process. For the successful procedure of the SHT a solid knowledge about the crucial process parameters such as the design of the SHT layout, the SHT process time and the maximum SHT temperature are urgently required. It also should be noted that the storage time between the SHT and the forming processes affects the mechanical properties of the SHT area. In this paper, the effect of diverse SHT process parameters and various storage time-frames on the major and minor strain situation of a deep drawn part is discussed by the evaluation of the forming limit diagram. For the purpose of achieving short heating times and a homogenous temperature distribution a one side contact heating tool has been used for the heat treatment in this study.

  4. Validation of a short-term shoreline evolution model and coastal risk management implications. The case of the NW Portuguese coast (Ovar-Marinha Grande)

    Science.gov (United States)

    Cenci, Luca; Giuseppina Persichillo, Maria; Disperati, Leonardo; Oliveira, Eduardo R.; de Fátima Lopes Alves, Maria; Boni, Giorgio; Pulvirenti, Luca; Phillips, Mike

    2015-04-01

    Coastal zones are fragile and dynamic environments where environmental, economic and social aspects are interconnected. While these areas are often highly urbanised, they are especially vulnerable to natural hazards (e.g. storms, floods, erosion, storm surges). Hence, high risk affects people and goods in several coastal zones throughout the world. The recent storms that hit the European coasts (Hercules, Christian and Stephanie, among others) showed the high vulnerability of these territories. Integrated Coastal Management (ICM) deals with the sustainable development of coastal zones by taking into account the different aspects that affect them, including risks adaptation and mitigation. Accurate mapping of shoreline position through time and models to predict shoreline evolution play a fundamental role for coastal zone risk management. In this context, spaceborne remote sensing is fundamental because it provides synoptic and multitemporal information that allow the extraction of shorelines' proxies. These are stable coastal features (e.g. the vegetation lines, the foredune toe, etc.) that can be mapped instead of the proper shoreline, which is an extremely dynamic boundary. The use of different proxies may provide different evolutionary patterns for the same study area; therefore it is important to assess which is the most suitable, given the environmental characteristics of a specific area. In Portugal, the coastal stretch between Ovar and Marinha Grande is one of the greatest national challenges in terms of integrated management of resources and risks. This area is characterised by intense erosive processes that largely exceed the shoreline's retreat predictions made in the first Coastal Zone Management Plan, developed in 2000. The aim of this work was to assess the accuracy of a new model of shoreline evolution implemented in 2013 in order to check its robustness for short-term predictions. The method exploited the potentialities of the Landsat archive

  5. Short-term retrospective versus prospective memory processing as emergent properties of the mind and brain: human fMRI evidence.

    Science.gov (United States)

    Mok, L W

    2012-12-13

    The functional-neuroanatomical substrates for short-term retrospective versus prospective memory processing were examined in a delay task, in which associative choices were made conditionally based on the presenting discriminative/cue stimulus. Delay-period prospection could be of the intended choice and/or the expected response outcome, whereas delay-period retrospection would be of the just-presented cue stimulus. Previous results have shown that the spontaneous process of unique outcome prospection did not implicate the lateral prefrontal cortex (PFC) but instead implicated the lateral posterior parietal cortex (LPPC) in a modality-independent fashion (Mok et al., 2009). Spontaneous retrospection was more dependent on the medial temporal lobe (MTL). Nevertheless, it was anticipated that the more explicit process of prospecting an intended choice would implicate the lateral PFC. To verify this, Mok et al.'s data were further analyzed, with new control data. Healthy, young adults performed delayed discriminative choices under procedures that biased them toward different degrees of delay-period prospection: higher-using cue-unique, differential outcomes (DO); versus lower-using a non-unique, common outcome (CO), or unpredictable, non-differential outcomes (NDO). Experimental participants performed the DO versus CO procedures concurrently, while undergoing event-related functional magnetic resonance imaging (fMRI). Separately, control participants provided data for: the NDO condition; related comparison tasks, which biased them toward different degrees of delay-period retrospection; and null-event trials. Expectedly, the more explicit process of prospecting an intended associative choice implicated the lateral PFC, as part of and together with other components of the multiple-demand network. Comparisons against null-event trials indicated that the sustained delay activity observed in MTL and LPPC, respectively, was part of default brain activity. These results

  6. Short-term Influences on Suspended Particulate Matter Distribution in the Northern Gulf of Mexico: Satellite and Model Observations

    Directory of Open Access Journals (Sweden)

    Dong S. Ko

    2008-07-01

    Full Text Available Energetic meteorological events such as frontal passages and hurricanes often impact coastal regions in the northern Gulf of Mexico that influence geochemical processes in the region. Satellite remote sensing data such as winds from QuikSCAT, suspended particulate matter (SPM concentrations derived from SeaWiFS and the outputs (sea level and surface ocean currents of a nested navy coastal ocean model (NCOM were combined to assess the effects of frontal passages between 23-28 March 2005 on the physical properties and the SPM characteristics in the northern Gulf of Mexico. Typical changes in wind speed and direction associated with frontal passages were observed in the latest 12.5 km wind product from QuikSCAT with easterly winds before the frontal passage undergoing systematic shifts in direction and speed and turning northerly, northwesterly during a weak and a strong front on 23 and 27 March, respectively. A quantitative comparison of model sea level results with tide gauge observations suggest better correlations near the delta than in the western part of the Gulf with elevated sea levels along the coast before the frontal passage and a large drop in sea level following the frontal passage on 27 March. Model results of surface currents suggested strong response to wind forcing with westward and onshore currents before the frontal passage reversing into eastward, southeastward direction over a six day period from 23 to 28 March 2005. Surface SPM distribution derived from SeaWiFS ocean color data for two clear days on 23 and 28 March 2005 indicated SPM plumes to be oriented with the current field with increasing concentrations in nearshore waters due to resuspension and discharge from the rivers and bays and its seaward transport following the frontal passage. The backscattering spectral slope γ, a parameter sensitive to particle size distribution also indicated lower γ values (larger particles in nearshore waters that decreased

  7. Mental rotation impairs attention shifting and short-term memory encoding: neurophysiological evidence against the response-selection bottleneck model of dual-task performance.

    Science.gov (United States)

    Pannebakker, Merel M; Jolicœur, Pierre; van Dam, Wessel O; Band, Guido P H; Ridderinkhof, K Richard; Hommel, Bernhard

    2011-09-01

    Dual tasks and their associated delays have often been used to examine the boundaries of processing in the brain. We used the dual-task procedure and recorded event-related potentials (ERPs) to investigate how mental rotation of a first stimulus (S1) influences the shifting of visual-spatial attention to a second stimulus (S2). Visual-spatial attention was monitored by using the N2pc component of the ERP. In addition, we examined the sustained posterior contralateral negativity (SPCN) believed to index the retention of information in visual short-term memory. We found modulations of both the N2pc and the SPCN, suggesting that engaging mechanisms of mental rotation impairs the deployment of visual-spatial attention and delays the passage of a representation of S2 into visual short-term memory. Both results suggest interactions between mental rotation and visual-spatial attention in capacity-limited processing mechanisms indicating that response selection is not pivotal in dual-task delays and all three processes are likely to share a common resource like executive control.

  8. Combined effects of short-term rainfall patterns and soil texture on nitrogen cycling -- A Modeling Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gu, C.; Riley, W.J.

    2009-11-01

    responses to increasing precipitation variability depends on precipitation amount and soil texture, and that accurate prediction of future N cycling and gas effluxes requires models with relatively sophisticated representation of the relevant processes.

  9. Evaluation of indocyanine green clearance and model for end-stage liver disease for estimation of short-term prognosis in decompensated cirrhosis.

    Science.gov (United States)

    Stauber, Rudolf E; Wagner, Doris; Stadlbauer, Vanessa; Palma, Stefan; Gurakuqi, Gerald; Kniepeiss, Daniela; Iberer, Florian; Smolle, Karl-Heinz; Haas, Josef; Trauner, Michael

    2009-11-01

    Indocyanine green (ICG) clearance has been proposed as a quantitative liver function test several decades ago. Interest in this method has been renewed following the development of finger pulse densitometry for noninvasive estimation of the ICG plasma disappearance rate (PDR). On the other hand, the model for end-stage liver disease (MELD), which is based on routine laboratory parameters, is widely used for estimation of short-term survival in cirrhosis, but its prognostic value in critically ill cirrhotic patients is unclear. The aim of the present study was to compare the diagnostic accuracy of ICG PDR vs. MELD for estimation of short-term prognosis in cirrhotic patients. Ninety consecutive cirrhotic patients who were admitted for decompensated disease or were being evaluated for liver transplantation were screened. Patients who underwent liver transplantation within the following 90 days and those with hepatocellular carcinoma were excluded. In the remaining 70 patients, routine laboratory parameters and ICG clearance were analysed. Following an injection of ICG 0.25 mg/kg, PDR was measured by finger pulse densitometry. The diagnostic accuracy of ICG PDR and MELD for prediction of 90-day survival was assessed by receiver-operating characteristic (ROC) curve analysis. ROC curve analysis revealed superior diagnostic accuracy for MELD as compared with ICG PDR in predicting 90-day survival (area under the ROC curve 0.89 vs. 0.71). A MELD cut-off of 22 provided the best discrimination for prediction of 90-day survival. MELD is superior to ICG PDR for estimation of short-term survival in patients with decompensated cirrhosis.

  10. Utility of birefringence changes due to collagen thermal denaturation rate process analysis: vessel wall temperature estimation for new short term heating balloon angioplasty

    Science.gov (United States)

    Kaneko, Kenji; Shimazaki, Natsumi; Gotoh, Maya; Nakatani, Eriko; Arai, Tsunenori

    2007-02-01

    Our photo thermal reaction heating architecture balloon realizes less than 10 s short term heating that can soften vessel wall collagen without damaging surrounding tissue thermally. New thermal balloon angioplasty, photo-thermo dynamic balloon angioplasty (PTDBA) has experimentally shown sufficient opening with 2 atm low pressure dilation and prevention of chronic phase restenosis and acute phase thrombus in vivo. Even though PTDBA has high therapeutic potential, the most efficient heating condition is still under study, because relationship of treatment and thermal dose to vessel wall is not clarified yet. To study and set the most efficient heating condition, we have been working on establishment of temperature history estimation method from our previous experimental results. Heating target of PTDBA, collagen, thermally denatures following rate process. Denaturation is able to be quantified with measured collagen birefringence value. To express the denaturation with equation of rate process, the following ex vivo experiments were performed. Porcine extracted carotid artery was soaked in two different temperature saline baths to enforce constant temperature heating. Higher temperature bath was set to 40 to 80 degree Celsius and soaking duration was 5 to 40 s. Samples were observed by a polarizing microscope and a scanning electron microscope. The birefringence was measured by polarizing microscopic system using Brace-Koehler compensator 1/30 wavelength. The measured birefringence showed temperature dependency and quite fit with the rate process equation. We think vessel wall temperature is able to be estimated using the birefringence changes due to thermal denaturation.

  11. Modelling Short-Term Maximum Individual Exposure from Airborne Hazardous Releases in Urban Environments. Part ΙI: Validation of a Deterministic Model with Wind Tunnel Experimental Data

    Directory of Open Access Journals (Sweden)

    George C. Efthimiou

    2015-06-01

    Full Text Available The capability to predict short-term maximum individual exposure is very important for several applications including, for example, deliberate/accidental release of hazardous substances, odour fluctuations or material flammability level exceedance. Recently, authors have proposed a simple approach relating maximum individual exposure to parameters such as the fluctuation intensity and the concentration integral time scale. In the first part of this study (Part I, the methodology was validated against field measurements, which are governed by the natural variability of atmospheric boundary conditions. In Part II of this study, an in-depth validation of the approach is performed using reference data recorded under truly stationary and well documented flow conditions. For this reason, a boundary-layer wind-tunnel experiment was used. The experimental dataset includes 196 time-resolved concentration measurements which detect the dispersion from a continuous point source within an urban model of semi-idealized complexity. The data analysis allowed the improvement of an important model parameter. The model performed very well in predicting the maximum individual exposure, presenting a factor of two of observations equal to 95%. For large time intervals, an exponential correction term has been introduced in the model based on the experimental observations. The new model is capable of predicting all time intervals giving an overall factor of two of observations equal to 100%.

  12. Short-term versus long-term water maze training effects on hippocampal neuronal synaptic plasticity in a rat model of senile dementia

    Institute of Scientific and Technical Information of China (English)

    Guogui Li

    2008-01-01

    BACKGROUND: Changes in synaptic plasticity might underlie senile dementia, and might be the neurobiological basis for learning and memory dysfunctions in patients with Alzheimer's Disease. OBJECTIVE: To investigate the effects of water maze training on hippocampal neuronal synaptic plasticity in rats with senile dementia, and to compare changes in synaptic plasticity between short- and long-term water maze training sessions.DESIGN, TIME AND SETTING: A randomized, controlled, neuromorphological observation with animal models of senile dementia was performed at the laboratory of College of Pharmacy, Chongqing Medical University between November 2006 and April 2007.MATERIALS: Fifty male, Sprague Dawley rats were randomized into five groups, with 10 rats per group: model, control, sham-operated, short-term water maze training, and long-term water maze training. METHODS: In the model group, senile dementia was induced by fimbria-fornix lesion method. The control rats remained untreated. In the sham-operated group, water maze training was performed without fimbria-fornix lesion induction. Rats from the short-term water maze training group underwent 20-day water maze training from day 26 after fimbria-fornix lesion induction. The long-term water maze training group underwent 40-day water maze training beginning at day 6 following fimbria-fornix lesion induction. Beginning at day 41, each group underwent 5-day spatial learning and memory training. MAIN OUTCOME MEASURES: Following experimentation, the morphological parameters of synapses, including synaptic numerical density, synaptic surface density, and the average synapse size were stereologically measured. Through the use of an electron microscope, synaptic morphological changes in the hippocampai CA3 region were observed.RESULTS: Compared with the control group, synaptic numerical and surface densities were significantly decreased in the model group (P < 0.01). Synaptic numerical and surface densities significantly

  13. Unit-Specific Event-Based and Slot-Based Hybrid Model Framework with Hierarchical Structure for Short-Term Scheduling

    Directory of Open Access Journals (Sweden)

    Yue Wang

    2015-01-01

    Full Text Available Unit-specific event-based continuous-time model has inaccurate calculation problems in involving resource constraints, due to the heterogeneous locations of the event points for different units. In order to address this limitation, a continuous-time unit-specific event-based and slot-based hybrid model framework with hierarchical structure is proposed in this work. A unit-specific event-based model without utility constraints is formulated in upper layer, and a slot-based model is introduced in lower layer. In the hierarchical structure, the two layers jointly address the short-term production scheduling problem of batch plants under utility consideration. The key features of this work include the following: (a eliminating overstrict constraints on utility resources, (b solving multiple counting problems, and (c considering duration time of event points in calculating utility utilization level. The effectiveness and advantages of proposed model are illustrated through two benchmark examples from the literatures.

  14. The BACHD Rat Model of Huntington Disease Shows Signs of Fronto-Striatal Dysfunction in Two Operant Conditioning Tests of Short-Term Memory

    Science.gov (United States)

    Clemensson, Laura Emily; Riess, Olaf; Nguyen, Huu Phuc

    2017-01-01

    The BACHD rat is a recently developed transgenic animal model of Huntington disease, a progressive neurodegenerative disorder characterized by extensive loss of striatal neurons. Cognitive impairments are common among patients, and characterization of similar deficits in animal models of the disease is therefore of interest. The present study assessed the BACHD rats' performance in the delayed alternation and the delayed non-matching to position test, two Skinner box-based tests of short-term memory function. The transgenic rats showed impaired performance in both tests, indicating general problems with handling basic aspects of the tests, while short-term memory appeared to be intact. Similar phenotypes have been found in rats with fronto-striatal lesions, suggesting that Huntington disease-related neuropathology might be present in the BACHD rats. Further analyses indicated that the performance deficit in the delayed alternation test might be due to impaired inhibitory control, which has also been implicated in Huntington disease patients. The study ultimately suggests that the BACHD rats might suffer from neuropathology and cognitive impairments reminiscent of those of Huntington disease patients. PMID:28045968

  15. Long and short-term atmospheric radiation analyses based on coupled measurements at high altitude remote stations and extensive air shower modeling

    Science.gov (United States)

    Hubert, G.; Federico, C. A.; Pazianotto, M. T.; Gonzales, O. L.

    2016-02-01

    In this paper are described the ACROPOL and OPD high-altitude stations devoted to characterize the atmospheric radiation fields. The ACROPOL platform, located at the summit of the Pic du Midi in the French Pyrenees at 2885 m above sea level, exploits since May 2011 some scientific equipment, including a BSS neutron spectrometer, detectors based on semiconductor and scintillators. In the framework of a IEAv and ONERA collaboration, a second neutron spectrometer was simultaneously exploited since February 2015 at the summit of the Pico dos Dias in Brazil at 1864 m above the sea level. The both high station platforms allow for investigating the long period dynamics to analyze the spectral variation of cosmic-ray- induced neutron and effects of local and seasonal changes, but also the short term dynamics during solar flare events. This paper presents long and short-term analyses, including measurement and modeling investigations considering the both high altitude stations data. The modeling approach, based on ATMORAD computational platform, was used to link the both station measurements.

  16. The BACHD Rat Model of Huntington Disease Shows Signs of Fronto-Striatal Dysfunction in Two Operant Conditioning Tests of Short-Term Memory.

    Science.gov (United States)

    Clemensson, Erik Karl Håkan; Clemensson, Laura Emily; Riess, Olaf; Nguyen, Huu Phuc

    2017-01-01

    The BACHD rat is a recently developed transgenic animal model of Huntington disease, a progressive neurodegenerative disorder characterized by extensive loss of striatal neurons. Cognitive impairments are common among patients, and characterization of similar deficits in animal models of the disease is therefore of interest. The present study assessed the BACHD rats' performance in the delayed alternation and the delayed non-matching to position test, two Skinner box-based tests of short-term memory function. The transgenic rats showed impaired performance in both tests, indicating general problems with handling basic aspects of the tests, while short-term memory appeared to be intact. Similar phenotypes have been found in rats with fronto-striatal lesions, suggesting that Huntington disease-related neuropathology might be present in the BACHD rats. Further analyses indicated that the performance deficit in the delayed alternation test might be due to impaired inhibitory control, which has also been implicated in Huntington disease patients. The study ultimately suggests that the BACHD rats might suffer from neuropathology and cognitive impairments reminiscent of those of Huntington disease patients.

  17. A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data

    Directory of Open Access Journals (Sweden)

    Peng Sun

    2016-10-01

    Full Text Available This paper presents a short-term wind turbine (WT outage model based on the data collected from a wind farm supervisory control and data acquisition (SCADA system. Neural networks (NNs are used to establish prediction models of the WT condition parameters that are dependent on environmental conditions such as ambient temperature and wind speed. The prediction error distributions are discussed and used to calculate probabilities of the operation of protection relays (POPRs that were caused by the threshold exceedance of the environmentally sensitive parameters. The POPRs for other condition parameters are based on the setting time of the operation of protection relays. The union probability method is used to integrate the probabilities of operation of each protection relay to predict the WT short term outage probability. The proposed method has been used for real 1.5 MW WTs with doubly fed induction generators (DFIGs. The results show that the proposed method is more effective in WT outage probability prediction than traditional methods.

  18. Short-term memory across eye blinks.

    Science.gov (United States)

    Irwin, David E

    2014-01-01

    The effect of eye blinks on short-term memory was examined in two experiments. On each trial, participants viewed an initial display of coloured, oriented lines, then after a retention interval they viewed a test display that was either identical or different by one feature. Participants kept their eyes open throughout the retention interval on some blocks of trials, whereas on others they made a single eye blink. Accuracy was measured as a function of the number of items in the display to determine the capacity of short-term memory on blink and no-blink trials. In separate blocks of trials participants were instructed to remember colour only, orientation only, or both colour and orientation. Eye blinks reduced short-term memory capacity by approximately 0.6-0.8 items for both feature and conjunction stimuli. A third, control, experiment showed that a button press during the retention interval had no effect on short-term memory capacity, indicating that the effect of an eye blink was not due to general motoric dual-task interference. Eye blinks might instead reduce short-term memory capacity by interfering with attention-based rehearsal processes.

  19. REMI and ROUSE: Quantitative Models for Long-Term and Short-Term Priming in Perceptual Identification

    NARCIS (Netherlands)

    E.J. Wagenmakers (Eric-Jan); R. Zeelenberg (René); D.E. Huber (David); J.G.W. Raaijmakers (Jeroen)

    2003-01-01

    textabstractThe REM model originally developed for recognition memory (Shiffrin & Steyvers, 1997) has recently been extended to implicit memory phenomena observed during threshold identification of words. We discuss two REM models based on Bayesian principles: a model for long-term priming (REMI; Sc

  20. REMI and ROUSE: Quantitative Models for Long-Term and Short-Term Priming in Perceptual Identification

    NARCIS (Netherlands)

    E.J. Wagenmakers (Eric-Jan); R. Zeelenberg (René); D.E. Huber (David); J.G.W. Raaijmakers (Jeroen)

    2003-01-01

    textabstractThe REM model originally developed for recognition memory (Shiffrin & Steyvers, 1997) has recently been extended to implicit memory phenomena observed during threshold identification of words. We discuss two REM models based on Bayesian principles: a model for long-term priming (REMI;

  1. Micromechanical modelling of short-term and long-term large-strain behaviour of polyethylene terephthalate

    Science.gov (United States)

    Poluektov, M.; van Dommelen, J. A. W.; Govaert, L. E.; Yakimets, I.; Geers, M. G. D.

    2013-12-01

    A micromechanically based model is used to describe the mechanical behaviour of polyethylene terephthalate (PET) under uniaxial compression up to large strains and at different temperatures. The creep behaviour of isotropic PET is simulated and compared to experimental data to demonstrate the applicability of the model to describe the long-term response. The material is modelled as an aggregate of two-phase layered domains, where different constitutive laws are used for the phases. A hybrid interaction law between the domains is adopted. The crystalline phase is modelled with crystal plasticity and the amorphous phase with the Eindhoven Glassy Polymer model, taking into account material ageing effects. Model parameters for the selected constitutive laws of the phases are identified from uniaxial compression tests for fully amorphous material and semicrystalline material. Texture evolution during the deformation predicted by the model adequately matches previously observed texture evolution.

  2. Can the Gulf Stream induce coherent short-term fluctuations in sea level along the US East Coast? A modeling study

    Science.gov (United States)

    Ezer, Tal

    2016-02-01

    Much attention has been given in recent years to observations and models that show that variations in the transport of the Atlantic Meridional Overturning Circulation (AMOC) and in the Gulf Stream (GS) can contribute to interannual, decadal, and multi-decadal variations in coastal sea level (CSL) along the US East Coast. However, less is known about the impact of short-term (time scales of days to weeks) fluctuations in the GS and their impact on CSL anomalies. Some observations suggest that these anomalies can cause unpredictable minor tidal flooding in low-lying areas when the GS suddenly weakens. Can these short-term CSL variations be attributed to changes in the transport of the GS? An idealized numerical model of the GS has been set up to test this proposition. The regional model uses a 1/12° grid with a simplified coastline to eliminate impacts from estuaries and small-scale coastal features and thus isolate the GS impact. The GS in the model is driven by inflows/outflows, representing the Florida Current (FC), the Slope Current (SC), and the Sargasso Sea (SS) flows. Forcing the model with an oscillatory FC transport with a period of 2, 5, and 10 days produced coherent CSL variations from Florida to the Gulf of Maine with similar periods. However, when imposing variations in the transports of the SC or the SS, they induce CSL variations only north of Cape Hatteras. The suggested mechanism is that variations in GS transport produce variations in sea level gradient across the entire GS length and this large-scale signal is then transmitted into the shelf by the generation of coastal-trapped waves (CTW). In this idealized model, the CSL variations induced by variations of ˜10 Sv in the transport of the GS are found to resemble CSL variations induced by ˜5 m s-1 zonal wind fluctuations, though the mechanisms of wind-driven and GS-driven sea level are quite different. Better understanding of the relation between variations in offshore currents and CSL will help

  3. Serotonin Depletion Does not Modify the Short-Term Brain Hypometabolism and Hippocampal Neurodegeneration Induced by the Lithium-Pilocarpine Model of Status Epilepticus in Rats.

    Science.gov (United States)

    García-García, Luis; Shiha, Ahmed Anis; Bascuñana, Pablo; de Cristóbal, Javier; Fernández de la Rosa, Rubén; Delgado, Mercedes; Pozo, Miguel A

    2016-05-01

    It has been reported that fluoxetine, a selective serotonin (5-hydroxytryptamine; 5-HT) reuptake inhibitor, has neuroprotective properties in the lithium-pilocarpine model of status epilepticus (SE) in rats. The aim of the present study was to investigate the effect of 5-HT depletion by short-term administration of p-chlorophenylalanine (PCPA), a specific tryptophan hydroxylase inhibitor, on the brain hypometabolism and neurodegeneration induced in the acute phase of this SE model. Our results show that 5-HT depletion did modify neither the brain basal metabolic activity nor the lithium-pilocarpine-induced hypometabolism when evaluated 3 days after the insult. In addition, hippocampal neurodegeneration and astrogliosis triggered by lithium-pilocarpine were not exacerbated by PCPA treatment. These findings point out that in the early latent phase of epileptogenesis, non-5-HT-mediated actions may contribute, at least in some extent, to the neuroprotective effects of fluoxetine in this model of SE.

  4. Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics

    Directory of Open Access Journals (Sweden)

    Alexandre Ferreira do Nascimento

    2012-12-01

    Full Text Available Insight of nutrient release patterns associated with the decomposition of plant residues is important for their effective use as a green manure in food production systems. Thus, this study aimed to evaluate the ability of the Century, APSIM and NDICEA simulation models for predicting the decomposition and N mineralization of crop residues in the tropical Atlantic forest biome, Brazil. The simulation models were calibrated based on actual decomposition and N mineralization rates of three types of crop residues with different chemical and biochemical composition. The models were also validated for different pedo-climatic conditions and crop residues conditions. In general, the accuracy of decomposition and N mineralization improved after calibration. Overall RMSE values for the decomposition and N mineralization of the crop materials varied from 7.4 to 64.6% before models calibration compared to 3.7 to 16.3 % after calibration. Therefore, adequate calibration of the models is indispensable for use them under humid tropical conditions. The NDICEA model generally outperformed the other models. However, the decomposition and N mineralization was not very accurate during the first 30 days of incubation, especially for easily decomposable crop residues. An additional model variable may be required to capture initial microbiological growth as affected by the moisture dynamics of the residues, as is the case in surface residues decomposition models.

  5. Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

    Directory of Open Access Journals (Sweden)

    Christopher Bennett

    2014-04-01

    Full Text Available This paper set out to identify the significant variables which affect residential low voltage (LV network demand and develop next day total energy use (NDTEU and next day peak demand (NDPD forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX and neural network (NN techniques. The data used for this research was collected from a LV transformer serving 128 residential customers. It was observed that temperature accounted for half of the residential LV network demand. The inclusion of the double exponential smoothing algorithm, autoregressive terms, relative humidity and day of the week dummy variables increased model accuracy. In terms of R2 and for each modelling technique and phase, NDTEU hindcast accuracy ranged from 0.77 to 0.87 and forecast accuracy ranged from 0.74 to 0.84. NDPD hindcast accuracy ranged from 0.68 to 0.74 and forecast accuracy ranged from 0.56 to 0.67. The NDTEU models were more accurate than the NDPD models due to the peak demand time series being more variable in nature. The NN models had slight accuracy gains over the ARIMAX models. A hybrid model was developed which combined the best traits of the ARIMAX and NN techniques, resulting in improved hindcast and forecast fits across the all three phases.

  6. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias; Zhang, Jie

    2017-03-01

    With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

  7. Utilizing NEXRAD-based QPEs and short-term QPFs in a TIN-based Distributed Hydrologic Model for Hydrologic Forecasting

    Science.gov (United States)

    Vivoni, E. R.; Grassotti, C.; Ivanov, V. Y.; Van Horne, M.; Bras, R. L.; Entekhabi, D.; Hoffman, R. N.

    2001-12-01

    The principal reasons motivating the use of meteorological radar for hydrologic modeling have been the potential for extending the spatial and temporal coverage of rainfall data as compared to sparse rain gauge networks. NEXRAD reflectivity measurements and derived rainfall products open the door to real-time availability of extensive rainfall coverage over watersheds in the United States. For hydrologic modeling purposes, the value of radar rainfall data is increased with the use of distributed hydrologic models capable of ingesting this new data source and taking full advantage of its spatial and temporal variability. This study presents preliminary results of applying a TIN-based distributed model with quantitative precipitation estimates (QPEs) and short-term quantitative precipitation forecasts (QPFs) derived from two radar rainfall products (operational Stage III estimates produced by the Arkansas-Red Basin River Forecast Center, and commercially available NOWrad estimates marketed by WSI, Inc.). Although both are based on NEXRAD reflectivity measurements, the NEXRAD Stage III and the WSI rainfall products can at times differ considerably in their estimation of the values and distribution of rainfall. Comparisons will be presented of the two radar rainfall products for a selected set of storm events in multiple basins within the Arkansas Red-River watershed. In addition, the difference in the forecasted rainfall fields (nowcasts product) derived from the MIT Lincoln Lab Storm Growth and Decay Model will be presented. Hydrologic modeling predictions from the use of the TIN-based, Real-time Integrated Basin Simulator (tRIBS) with the rainfall estimates and forecasts will be also be discussed in light of the differences in the rainfall inputs. Through this study, the strengths and/or weaknesses of two different radar rainfall sources and their corresponding short-term extrapolations will be highlighted as they relate to the interior hydrologic response and

  8. DEVELOPING A MODULAR PORTFOLIO SELECTION MODEL FOR SHORT-TERM AND LONGTERM MARKET TRENDS AND MASS PSYCHOLOGY

    Directory of Open Access Journals (Sweden)

    M. Jasemi

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: In an effort to model stock markets, many researchers have developed portfolio selection models to maximise investor satisfaction. However, this field still needs more accurate and comprehensive models. Development of these models is difficult because of unpredictable economic, social, and political variables that affect stock market behaviour. In this paper, a new model with three modules for portfolio optimisation is presented. The first module derives the efficient frontier through a new approach; the second presents an intelligent mechanism for emitting trading signals; while the third module integrates the outputs of the first two modules. Some important features of the model in comparison with others are: 1 consideration of investors’ emotions – the psychology of the market – that arises from the three above-mentioned factors; 2 significant loosening of simplifying assumptions about markets and stocks; and 3 greater sensitivity to new data.

    AFRIKAANSE OPSOMMING: In ‘n poging om aandelemarkte te modelleer het verskeie navorsers portefeulje-seleksiemodelle ontwikkel om beleggers se tevredenheid te maksimiseer. Desnieteenstaande word meer akkurate en omvattende modelle benodig. Die ontwikkeling van hierdie modelle word bemoeilik deur die onvoorspelbare ekonomiese, sosiale en politiese veranderlikes wat aandelemarkte se gedrag raak. In hierdie artikel word ‘n nuwe model voorgehou wat bestaan uit drie modules vir portefeulje-optimisering. Die eerste module bepaal die doelmatigheidsgrens op ‘n nuwe metode; die tweede hou ‘n intelligente meganisme voor om transaksieseine te lewer terwyl die derde module die uitsette van die eerste twee modules integreer. Sommige van die belangrike eienskappe van die model wat dit van ander onderskei is: 1 konsiderasie van die beleggers se emosies – die sielkunde van die mark – wat ontstaan vanweë die genoemde faktore; 2 betekenisvolle verslapping van die

  9. Short-term bulk energy storage system scheduling for load leveling in unit commitment: modeling, optimization, and sensitivity analysis.

    Science.gov (United States)

    Hemmati, Reza; Saboori, Hedayat

    2016-05-01

    Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics.

  10. Short-Term Variability of X-rays from Accreting Neutron Star Vela X-1: II. Monte-Carlo Modeling

    CERN Document Server

    Odaka, Hirokazu; Tanaka, Yasuyuki T; Watanabe, Shin; Takahashi, Tadayuki; Makishima, Kazuo

    2013-01-01

    We develop a Monte Carlo Comptonization model for the X-ray spectrum of accretion-powered pulsars. Simple, spherical, thermal Comptonization models give harder spectra for higher optical depth, while the observational data from Vela X-1 show that the spectra are harder at higher luminosity. This suggests a physical interpretation where the optical depth of the accreting plasma increases with mass accretion rate. We develop a detailed Monte-Carlo model of the accretion flow, including the effects of the strong magnetic field ($\\sim 10^{12}$ G) both in geometrically constraining the flow into an accretion column, and in reducing the cross section. We treat bulk-motion Comptonization of the infalling material as well as thermal Comptonization. These model spectra can match the observed broad-band {\\it Suzaku} data from Vela X-1 over a wide range of mass accretion rates. The model can also explain the so-called "low state", in which the uminosity decreases by an order of magnitude. Here, thermal Comptonization sh...

  11. Energy storage systems impact on the short-term frequency stability of distributed autonomous microgrids, an analysis using aggregate models

    DEFF Research Database (Denmark)

    Serban, Ioan; Teodorescu, Remus; Marinescu, Corneliu

    2013-01-01

    is on autonomous MGs that dynamically behave similarly to the classical power systems. This is the systems case with classical distributed generators (DGs), but which can also contain renewable energy sources (RESs) in a certain penetration level. During MG islanded operation, the local generators take over most...... with both inertial response and an adaptive droop characteristic during battery state-of-charge limitations. The conducted analysis is accomplished by adopting aggregated models for the involved control mechanisms. The developed model is analysed in frequency domain, whereas an experimental test bench...

  12. Using a Process Dissociation Approach to Assess Verbal Short-Term Memory for Item and Order Information in a Sample of Individuals with a Self-Reported Diagnosis of Dyslexia.

    Science.gov (United States)

    Wang, Xiaoli; Xuan, Yifu; Jarrold, Christopher

    2016-01-01

    Previous studies have examined whether difficulties in short-term memory for verbal information, that might be associated with dyslexia, are driven by problems in retaining either information about to-be-remembered items or the order in which these items were presented. However, such studies have not used process-pure measures of short-term memory for item or order information. In this work we adapt a process dissociation procedure to properly distinguish the contributions of item and order processes to verbal short-term memory in a group of 28 adults with a self-reported diagnosis of dyslexia and a comparison sample of 29 adults without a dyslexia diagnosis. In contrast to previous work that has suggested that individuals with dyslexia experience item deficits resulting from inefficient phonological representation and language-independent order memory deficits, the results showed no evidence of specific problems in short-term retention of either item or order information among the individuals with a self-reported diagnosis of dyslexia, despite this group showing expected difficulties on separate measures of word and non-word reading. However, there was some suggestive evidence of a link between order memory for verbal material and individual differences in non-word reading, consistent with other claims for a role of order memory in phonologically mediated reading. The data from the current study therefore provide empirical evidence to question the extent to which item and order short-term memory are necessarily impaired in dyslexia.

  13. Using a process dissociation approach to assess verbal short-term memory for item and order information in a sample of individuals with a self-reported diagnosis of dyslexia

    Directory of Open Access Journals (Sweden)

    Xiaoli eWang

    2016-02-01

    Full Text Available Previous studies have examined whether difficulties in short-term memory for verbal information, that might be associated with dyslexia, are driven by problems in retaining either information about to-be-remembered items or the order in which these items were presented. However, such studies have not used process-pure measures of short-term memory for item or order information. In this work we adapt a process dissociation procedure to properly distinguish the contributions of item and order processes to verbal short-term memory in a group of 28 adults with a self-reported diagnosis of dyslexia and a comparison sample of 29 adults without a dyslexia diagnosis. In contrast to previous work that has suggested that individuals with dyslexia experience item deficits resulting from inefficient phonological representation and language-independent order memory deficits, the results showed no evidence of specific problems in short-term retention of either item or order information among the individuals with a self-reported diagnosis of dyslexia, despite this group showing expected difficulties on separate measures of word and non-word reading. However, there was some suggestive evidence of a link between order memory for verbal material and individual differences in non-word reading, consistent with other claims for a role of order memory in phonologically-mediated reading. The data from the current study therefore provide empirical evidence to question the extent to which item and order short-term memory are necessarily impaired in dyslexia.

  14. Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics

    NARCIS (Netherlands)

    Nascimento, do A.F.; Mendona, E.D.; Leite, L.F.C.; Scholberg, J.M.S.; Neves, J.C.L.

    2012-01-01

    Insight of nutrient release patterns associated with the decomposition of plant residues is important for their effective use as a green manure in food production systems. Thus, this study aimed to evaluate the ability of the Century, APSIM and NDICEA simulation models for predicting the decompositi

  15. Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City.

    Science.gov (United States)

    Johnson, Nicholas E; Ianiuk, Olga; Cazap, Daniel; Liu, Linglan; Starobin, Daniel; Dobler, Gregory; Ghandehari, Masoud

    2017-04-01

    Historical municipal solid waste (MSW) collection data supplied by the New York City Department of Sanitation (DSNY) was used in conjunction with other datasets related to New York City to forecast municipal solid waste generation across the city. Spatiotemporal tonnage data from the DSNY was combined with external data sets, including the Longitudinal Employer Household Dynamics data, the American Community Survey, the New York City Department of Finance's Primary Land Use and Tax Lot Output data, and historical weather data to build a Gradient Boosting Regression Model. The model was trained on historical data from 2005 to 2011 and validation was performed both temporally and spatially. With this model, we are able to accurately (R2>0.88) forecast weekly MSW generation tonnages for each of the 232 geographic sections in NYC across three waste streams of refuse, paper and metal/glass/plastic. Importantly, the model identifies regularity of urban waste generation and is also able to capture very short timescale fluctuations associated to holidays, special events, seasonal variations, and weather related events. This research shows New York City's waste generation trends and the importance of comprehensive data collection (especially weather patterns) in order to accurately predict waste generation. Copyright © 2017. Published by Elsevier Ltd.

  16. A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting

    Directory of Open Access Journals (Sweden)

    Youzhu Li

    2014-01-01

    Full Text Available This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.

  17. An Examination of the Dual Model of Perfectionism and Adolescent Athlete Burnout: A Short-Term Longitudinal Research

    Science.gov (United States)

    Chen, Lung Hung; Kee, Ying Hwa; Tsai, Ying-Mei

    2009-01-01

    The dual model of perfectionism (Slade and Owens, Behav Modificat 22(3):372-390, 1998) is adopted to examine the influence of adaptive and maladaptive perfectionism on adolescent athlete burnout in Taiwan. Participants were 188 high school adolescent student-athletes (M = 16.48, SD = 0.59). They were administered the Multidimensional Inventory of…

  18. Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics

    NARCIS (Netherlands)

    Nascimento, do A.F.; Mendona, E.D.; Leite, L.F.C.; Scholberg, J.M.S.; Neves, J.C.L.

    2012-01-01

    Insight of nutrient release patterns associated with the decomposition of plant residues is important for their effective use as a green manure in food production systems. Thus, this study aimed to evaluate the ability of the Century, APSIM and NDICEA simulation models for predicting the

  19. Study of the footprints of short-term variation in XCO2 observed by TCCON sites using NIES and FLEXPART atmospheric transport models

    Science.gov (United States)

    Belikov, Dmitry A.; Maksyutov, Shamil; Ganshin, Alexander; Zhuravlev, Ruslan; Deutscher, Nicholas M.; Wunch, Debra; Feist, Dietrich G.; Morino, Isamu; Parker, Robert J.; Strong, Kimberly; Yoshida, Yukio; Bril, Andrey; Oshchepkov, Sergey; Boesch, Hartmut; Dubey, Manvendra K.; Griffith, David; Hewson, Will; Kivi, Rigel; Mendonca, Joseph; Notholt, Justus; Schneider, Matthias; Sussmann, Ralf; Velazco, Voltaire A.; Aoki, Shuji

    2017-01-01

    The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier transform spectrometers (FTSs) that record near-infrared (NIR) spectra of the sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fractions (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future and possible TCCON sites. We propose a footprint-based method for the collocation of satellite and TCCON XCO2 observations and estimate the performance of the method using the NIES model and five GOSAT (Greenhouse Gases Observing Satellite) XCO2 product data sets. Comparison of the proposed approach with a standard geographic method shows a higher number of collocation points and an average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasises that the collocation is sensitive to local meteorological conditions and flux distributions.

  20. Vertical distribution of buoyant Microcystis blooms in a Lagrangian particle tracking model for short-term forecasts in Lake Erie

    Science.gov (United States)

    Rowe, M. D.; Anderson, E. J.; Wynne, T. T.; Stumpf, R. P.; Fanslow, D. L.; Kijanka, K.; Vanderploeg, H. A.; Strickler, J. R.; Davis, T. W.

    2016-07-01

    Cyanobacterial harmful algal blooms (CHABs) are a problem in western Lake Erie, and in eutrophic fresh waters worldwide. Western Lake Erie is a large (3000 km2), shallow (8 m mean depth), freshwater system. CHABs occur from July to October, when stratification is intermittent in response to wind and surface heating or cooling (polymictic). Existing forecast models give the present location and extent of CHABs from satellite imagery, then predict two-dimensional (surface) CHAB movement in response to meteorology. In this study, we simulated vertical distribution of buoyant Microcystis colonies, and 3-D advection, using a Lagrangian particle model forced by currents and turbulent diffusivity from the Finite Volume Community Ocean Model (FVCOM). We estimated the frequency distribution of Microcystis colony buoyant velocity from measured size distributions and buoyant velocities. We evaluated several random-walk numerical schemes to efficiently minimize particle accumulation artifacts. We selected the Milstein scheme, with linear interpolation of the diffusivity profile in place of cubic splines, and varied the time step at each particle and step based on the curvature of the local diffusivity profile to ensure that the Visser time step criterion was satisfied. Inclusion of vertical mixing with buoyancy significantly improved model skill statistics compared to an advection-only model, and showed greater skill than a persistence forecast through simulation day 6, in a series of 26 hindcast simulations from 2011. The simulations and in situ observations show the importance of subtle thermal structure, typical of a polymictic lake, along with buoyancy in determining vertical and horizontal distribution of Microcystis.

  1. Impact of Bio-optical Data Assimilation on Short-term Coupled Physical, Bio-optical Model Predictions

    Science.gov (United States)

    2013-04-30

    photosynthetically active radiation (PAR) which drives photosynthesis of the ecosystem model, and relevant to the forecast of the underwater light ...state (e.g., their spe- cific chlorophyll to carbon ratio). This requires specification of high/low light absorption coefficients for each phyto...the whole phytoplankton populations at locations of R/V Point Sur water samples. Green , HPLC observed fractions; blue, run 1; light blue, run 2

  2. Short-Term Wind Speed Forecasting Study and Its Application Using a Hybrid Model Optimized by Cuckoo Search

    Directory of Open Access Journals (Sweden)

    Xuejun Chen

    2015-01-01

    Full Text Available The support vector regression (SVR and neural network (NN are both new tools from the artificial intelligence field, which have been successfully exploited to solve various problems especially for time series forecasting. However, traditional SVR and NN cannot accurately describe intricate time series with the characteristics of high volatility, nonstationarity, and nonlinearity, such as wind speed and electricity price time series. This study proposes an ensemble approach on the basis of 5-3 Hanning filter (5-3H and wavelet denoising (WD techniques, in conjunction with artificial intelligence optimization based SVR and NN model. So as to confirm the validity of the proposed model, two applicative case studies are conducted in terms of wind speed series from Gansu Province in China and electricity price from New South Wales in Australia. The computational results reveal that cuckoo search (CS outperforms both PSO and GA with respect to convergence and global searching capacity, and the proposed CS-based hybrid model is effective and feasible in generating more reliable and skillful forecasts.

  3. Modeling cooperation and powered-two wheelers short-term strategic decisions during overtaking in urban arterials

    Directory of Open Access Journals (Sweden)

    Emmanouil N. Barmpounakis

    2016-12-01

    Full Text Available A difference between Powered Two Wheelers (PTW drivers’ behavior and other drivers’ behavior in urban arterials is the frequency of overtaking. The present paper focuses on PTW overtaking and models the specific behavior using concepts of Game Theory. Both the PTW driver and the lead vehicle’s driver are assumed rational decision-makers that develop strategies, trying to maximize their payoffs. These strategies may be cooperative or not with respect to the distances and safety gaps and other behavioral aspects. The payoff function is formulated based on a novel latent statistically determined driving indicator, which quantifies both the driving risk and comfort. The proposed model is evaluated using trajectory data from video recordings on an urban arterial. Results show that both drivers have maximized gains by following a cooperative strategy. Findings also reveal that the successful overtaking rate is higher, when the PTW driver is non-cooperative, whereas lower overtaking rates occur, when the driver of the lead vehicle is non-cooperative. Finally, the concepts of Dominant Strategies, bounded rationality and the construction of the optimum payoff function are further discussed.

  4. Estimating Long to Short-Term Erosion Rates of Fluvial vs Mass Movement Processes: An Example from the Axial Zone of the Southern Italian Apennines

    Directory of Open Access Journals (Sweden)

    Maurizio Lazzari

    2010-10-01

    Full Text Available Estimates of erosion rates related to fluvial and landslides processes have been performed for several temporal ranges on the basis of different viable methods. The study area includes three catchment sub-basins of the north-eastern flank of the Agri Valley, a tectonically active morpho-structural low located in the axial zone of the southern Apennines and affected by desertification processes in its middle part. Long and mid-term rates have been estimated evaluating the missing volume of eroded rocks, on the grounds of GIS-aided calculations linked to geomorphological markers corresponding to ancient base-levels of the erosion. Short-term rates have been calculated converting the parameter related to the turbid transport of streams (Tu, derived from the quantitative geomorphic analysis, adopting an average value for the specific weight of the outcropping rocks, Such an approximation has to be considered acceptable because of the presence of conditions of lithological homogeneity in two of the chosen basins. In the light of this, the third basin, characterized by a certain geological heterogeneity, has been assumed as test-site with regard to the other two sub-basins. The average value of the long-term erosion rate from the entire study area is 0.25 mm/y, mainly due to fluvial processes. The comparison of the erosion rates related to the fluvial network activity from early Pleistocene to Present with those related to the mass movements occurred in a more recent (and shorter time-span indicates that mass movements contribute just for the 1% of the whole erosion estimated in the two homogeneous basins, but they refer to a short time-span (10 ky to Present. A theoretical extrapolation to the past (0.5 to 1 My allows to consider the contribution of landslides to the total erosion inside the catchment areas as almost equal to the fluvial aliquot. The conversion of the Tu values shows that the short-term rates are generally greater than the rates

  5. Effects of curcumin on short-term spatial and recognition memory, adult neurogenesis and neuroinflammation in a streptozotocin-induced rat model of dementia of Alzheimer's type.

    Science.gov (United States)

    Bassani, Taysa B; Turnes, Joelle M; Moura, Eric L R; Bonato, Jéssica M; Cóppola-Segovia, Valentín; Zanata, Silvio M; Oliveira, Rúbia M M W; Vital, Maria A B F

    2017-09-29

    Curcumin is a natural polyphenol with evidence of antioxidant, anti-inflammatory and neuroprotective properties. Recent evidence also suggests that curcumin increases cognitive performance in animal models of dementia, and this effect would be related to its capacity to enhance adult neurogenesis. The aim of this study was to test the hypothesis that curcumin treatment would be able to preserve cognition by increasing neurogenesis and decreasing neuroinflammation in the model of dementia of Alzheimer's type induced by an intracerebroventricular injection of streptozotocin (ICV-STZ) in Wistar rats. The animals were injected with ICV-STZ or vehicle and curcumin treatments (25, 50 and 100mg/kg, gavage) were performed for 30days. Four weeks after surgery, STZ-lesioned animals exhibited impairments in short-term spatial memory (Object Location Test (OLT) and Y maze) and short-term recognition memory (Object Recognition Test - ORT), decreased cell proliferation and immature neurons (Ki-67- and doublecortin-positive cells, respectively) in the subventricular zone (SVZ) and dentate gyrus (DG) of hippocampus, and increased immunoreactivity for the glial markers GFAP and Iba-1 (neuroinflammation). Curcumin treatment in the doses of 50 and 100mg/kg prevented the deficits in recognition memory in the ORT, but not in spatial memory in the OLT and Y maze. Curcumin treatment exerted only slight improvements in neuroinflammation, resulting in no improvements in hippocampal and subventricular neurogenesis. These results suggest a positive effect of curcumin in object recognition memory which was not related to hippocampal neurogenesis. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Short-term emergency response planning and risk assessment via an integrated modeling system for nuclear power plants in complex terrain

    Institute of Scientific and Technical Information of China (English)

    Ni-Bin CHANG; Yu-Chi WENG

    2013-01-01

    Short-term predictions of potential impacts from accidental release of various radionuclides at nuclear power plants are acutely needed,especially after the Fukushima accident in Japan.An integrated modeling system that provides expert services to assess the consequences of accidental or intentional releases of radioactive materials to the atmosphere has received wide attention.These scenarios can be initiated either by accident due to human,software,or mechanical failures,or from intentional acts such as sabotage and radiological dispersal devices.Stringent action might be required just minutes after the occurrence of accidental or intentional release.To fulfill the basic functions of emergency preparedness and response systems,previous studies seldom consider the suitability of air pollutant dispersion models or the connectivity between source term,dispersion,and exposure assessment models in a holistic context for decision support.Therefore,the Gaussian plume and puff models,which are only suitable for illustrating neutral air pollutants in flat terrain conditional to limited meteorological situations,are frequently used to predict the impact from accidental release of industrial sources.In situations with complex terrain or special meteorological conditions,the proposing emergency response actions might be questionable and even intractable to decisionmakers responsible for maintaining public health and environmental quality.This study is a preliminary effort to integrate the source term,dispersion,and exposure assessment models into a Spatial Decision Support System (SDSS) to tackle the complex issues for short-term emergency response planning and risk assessment at nuclear power plants.Through a series model screening procedures,we found that the diagnostic (objective) wind field model with the aid of sufficient on-site meteorological monitoring data was the most applicable model to promptly address the trend of local wind field patterns.However,most of the

  7. Short-term emergency response planning and risk assessment via an integrated modeling system for nuclear power plants in complex terrain

    Science.gov (United States)

    Chang, Ni-Bin; Weng, Yu-Chi

    2013-03-01

    Short-term predictions of potential impacts from accidental release of various radionuclides at nuclear power plants are acutely needed, especially after the Fukushima accident in Japan. An integrated modeling system that provides expert services to assess the consequences of accidental or intentional releases of radioactive materials to the atmosphere has received wide attention. These scenarios can be initiated either by accident due to human, software, or mechanical failures, or from intentional acts such as sabotage and radiological dispersal devices. Stringent action might be required just minutes after the occurrence of accidental or intentional release. To fulfill the basic functions of emergency preparedness and response systems, previous studies seldom consider the suitability of air pollutant dispersion models or the connectivity between source term, dispersion, and exposure assessment models in a holistic context for decision support. Therefore, the Gaussian plume and puff models, which are only suitable for illustrating neutral air pollutants in flat terrain conditional to limited meteorological situations, are frequently used to predict the impact from accidental release of industrial sources. In situations with complex terrain or special meteorological conditions, the proposing emergency response actions might be questionable and even intractable to decisionmakers responsible for maintaining public health and environmental quality. This study is a preliminary effort to integrate the source term, dispersion, and exposure assessment models into a Spatial Decision Support System (SDSS) to tackle the complex issues for short-term emergency response planning and risk assessment at nuclear power plants. Through a series model screening procedures, we found that the diagnostic (objective) wind field model with the aid of sufficient on-site meteorological monitoring data was the most applicable model to promptly address the trend of local wind field patterns

  8. I-States-as-Objects-Analysis (ISOA): Extensions of an Approach to Studying Short-Term Developmental Processes by Analyzing Typical Patterns

    Science.gov (United States)

    Bergman, Lars R.; Nurmi, Jari-Erik; von Eye, Alexander A.

    2012-01-01

    I-states-as-objects-analysis (ISOA) is a person-oriented methodology for studying short-term developmental stability and change in patterns of variable values. ISOA is based on longitudinal data with the same set of variables measured at all measurement occasions. A key concept is the "i-state," defined as a person's pattern of variable…

  9. A Study of the Effects of Variation of Short-Term Memory Load, Reading Response Length, and Processing Hierarchy on TOEFL Listening Comprehension Item Performance. Report 33.

    Science.gov (United States)

    Henning, Grant

    Criticisms of the Test of English as a Foreign Language (TOEFL) have included speculation that the listening test places too much burden on short-term memory as compared with comprehension, that a knowledge of reading is required to respond successfully, and that many items appear to require mere recall and matching rather than higher-order…

  10. Short-term Traffic Flow Forecasting Model Based on Few Data Cloud Inference%基于少数据云推理的短时交通流预测模型

    Institute of Scientific and Technical Information of China (English)

    杨锦伟; 肖新平; 郭金海; 毛树华

    2015-01-01

    针对短时交通流所存在的不确定性即模糊性与随机性特点和准周期规律,提出基于灰色关联分析和少数据云推理的短时交通流预测模型.首先,针对短时交通流的准周期规律,运用灰色关联分析提取不同日期相同时段历史序列中最相似序列;其次,提出少数据逆向云算法,建立交通流序列一维云推理机制;最后综合利用历史云及当前云生成预测云,用于短时交通流实时预测.实例分析表明,预测精度良好,能够有效实现短时交通流的实时预测.该模型解决了少数据条件下正向云参数确定问题,降低了数据处理工作量,开拓了云模型在短时交通流中的应用.%Concerning the fuzziness and randomness characteristics and quasi-periodic regularity in short-term traffic flow, a short-term traffic flow forecasting model is developed using grey relational analysis and few data cloud inference. Firstly, according to quasi-periodic regularity in short-term traffic flow, the most similar sequence in the history is extracted by gray relational analysis. Then, the backward cloud algorithm of few data is developed, which establishes the mechanism of one-dimensional cloud reasoning of traffic flow sequence. Finally, the prediction cloud is generated by a one-dimensional cloud inference of historical and current information. The results show that this model is used in forecasting short-term traffic flows and the accuracy is considerably improved. This proposed model solves the confirmation of forward cloud parameters under few data conditions, reducing the data processing workload and extending the application scope of the traditional cloud model.

  11. Model for end-stage liver disease score versus Maddrey discriminant function score in assessing short-term outcome in alcoholic hepatitis.

    Science.gov (United States)

    Kadian, Monil; Kakkar, Rajesh; Dhar, Minakshi; Kaushik, Rajeev Mohan

    2014-03-01

    The Maddrey Discriminant Function (mDF) score and the Model for End-Stage Liver Disease (MELD) score are standard prognostic scores for predicting disease severity and mortality in alcoholic hepatitis (AH).This prospective study compared the MELD score and the mDF score as predictors of short-term outcome in AH. The admission MELD score and the mDF score were assessed in 47 patients with a diagnosis of AH in the Himalayan Institute Hospital, Dehradun, India and the concordance (C) statistics of the two scores for 28-day mortality were determined and compared. Both the MELD score and the mDF score on day 1 were significantly higher in non-survivors than in survivors (P = 0.0001 each). The C-statistic for 28-day mortality for the MELD score was 0.91 (P mDF score 0.90 (P  19 (sensitivity 91.6% and specificity 85.7%) corresponded to the mDF score of > 52.8 (sensitivity 91.6% and specificity 82.8%). Both the MELD score and the mDF score at admission were strong and equally good predictors of 28-day mortality in patients with AH, but the optimal mDF score corresponding to optimal MELD score was higher than the conventional one. Thus, MELD score may be used as an alternative to mDF score for predicting short-term mortality in AH with an advantage. © 2013 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

  12. Three-Dimensional Short-Term Prediction Model of Dissolved Oxygen Content Based on PSO-BPANN Algorithm Coupled with Kriging Interpolation

    Directory of Open Access Journals (Sweden)

    Yingyi Chen

    2016-01-01

    Full Text Available Dissolved oxygen (DO content is a significant aspect of water quality in aquaculture. Prediction of dissolved oxygen may timely avoid the financial loss caused by inappropriate dissolved oxygen content and three-dimensional prediction can achieve more accurate and overall guidance. Therefore, this study presents a three-dimensional short-term prediction model of dissolved oxygen in crab aquaculture ponds based on back propagation artificial neural network (BPANN optimized by particle swarm optimization (PSO, which coupled with Kriging method. In this model, wavelet analysis is adopted for denoising, BPANN optimized by PSO is utilized for data analysis and one-dimensional prediction, and Kriging method is used for three-dimensional prediction. Compared with traditional one-dimensional prediction model, three-dimensional model has more real reaction of dissolved oxygen content in crab growth environment. In particular, the merits of PSO are evaluated against genetic algorithm (GA. The root mean square error (RMSE, mean absolute error (MAE, and mean absolute percentage error (MAPE for PSO model are 0.136445, 0.90534, and 0.15384, respectively, while for the GA model the values are 2.04184, 1.18316, and 0.21014, respectively. Furthermore, results of cross validation experiment show that the average error of this model is 0.0705 (mg/L. Consequently, this study suggests that the prediction model operates in a satisfactory manner.

  13. Short-term traffic flow forecasting with A-SVARMA

    OpenAIRE

    2013-01-01

    PUBLISHED Short-term Traffic Flow Forecasting (STFF), the process of predicting future traffic conditions based on historical and real-time observations, is an essential aspect of Intelligent Transportation Systems (ITS). The existing well-known algorithms used for STFF include time-series analysis based techniques, among which the seasonal Autoregressive Moving Average (ARMA) model is one of the most precise methods used in this field. The effectiveness of STFF in an urban transport netwo...

  14. The attention-weighted sample-size model of visual short-term memory: Attention capture predicts resource allocation and memory load.

    Science.gov (United States)

    Smith, Philip L; Lilburn, Simon D; Corbett, Elaine A; Sewell, David K; Kyllingsbæk, Søren

    2016-09-01

    We investigated the capacity of visual short-term memory (VSTM) in a phase discrimination task that required judgments about the configural relations between pairs of black and white features. Sewell et al. (2014) previously showed that VSTM capacity in an orientation discrimination task was well described by a sample-size model, which views VSTM as a resource comprised of a finite number of noisy stimulus samples. The model predicts the invariance of [Formula: see text] , the sum of squared sensitivities across items, for displays of different sizes. For phase discrimination, the set-size effect significantly exceeded that predicted by the sample-size model for both simultaneously and sequentially presented stimuli. Instead, the set-size effect and the serial position curves with sequential presentation were predicted by an attention-weighted version of the sample-size model, which assumes that one of the items in the display captures attention and receives a disproportionate share of resources. The choice probabilities and response time distributions from the task were well described by a diffusion decision model in which the drift rates embodied the assumptions of the attention-weighted sample-size model.

  15. Short period forecasting of catchment-scale precipitation. Part II: a water-balance storm model for short-term rainfall and flood forecasting

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2000-01-01

    Full Text Available A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid within the higher resolution model (5 km grid. A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii advected, steady-state predictions from the dynamic model, using 'inferred updraughts', provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment

  16. 基于两重门限GARCH模型的短期负荷预测%Short term load forecasting based on double-threshold GARCH models

    Institute of Scientific and Technical Information of China (English)

    王玉荣; 万秋兰; 陈昊

    2011-01-01

    针对负荷时间序列的非线性和波动性特征,在研究负荷时间序列波动性门限特征的基础上,引入冲量门限的概念,提出了一种基于两重门限GARCH模型的短期负荷预测新方法.利用条件极大似然估计方法,估计了模型参数.同时,考虑到负荷时间序列波动的厚尾效应,将模型推广为服从非高斯分布假设下的情形,建立了2种基于厚尾假设的两重门限GARCH类负荷预测模型.利用所提出的混合信息冲击曲面,分析了不同性质的冲击和冲量对负荷时间序列波动性的影响.实际算例基于南京地区日用电量数据进行了短期负荷预测,验证了模型及方法的可行性和有效性.算例结果表明,服从广义误差分布的两重门限GARCH模型预测效果满意.%Considering the nonlinearity and volatility of load time series, threshold characteristics in load time series are analyzed. The concept of momentum threshold is employed and a novel double-threshold generalized auto-regressive conditional heteroskedasticity (DT-GARCH) model is proposed for short term load forecasting. By using the conditional maximum likelihood estimation (CMLE) , the parameters are estimated. In addition, with fat-tail effect in volatility, the proposed models with non-Gaussian distributions are highlighted and estimated. Furthermore, the hybrid news impact surface is proposed to help analyze the impact of different shocks and momentums to the load time series. In case study, short term load forecasting is carried out based on the historical daily power consumption data of Nanjing, which validates the feasibility and effectiveness of the proposed model. Numerical results indicate that the DT-GARCH model with generalized error distribution provides satisfying forecasting results.

  17. Comparison of four prognostic models and a new Logistic regression model to predict short-term prognosis of acute-on-chronic hepatitis B liver failure

    Institute of Scientific and Technical Information of China (English)

    HE Wei-ping; HU Jin-hua; ZHAO Jun; TONG Jing-jing; DING Jin-biao; LIN Fang; WANG Hui-fen

    2012-01-01

    Background Acute-on-chronic hepatitis B liver failure (ACLF-HBV) is a clinically severe disease associated with major life-threatening complications including hepatic encephalopathy and hepatorenal syndrome.The aim of this study was to evaluate the short-term prognostic predictability of the model for end-stage liver disease (MELD),MELD-based indices,and their dynamic changes in patients with ACLF-HBV,and to establish a new model for predicting the prognosis of ACLF-HBV.Methods A total of 172 patients with ACLF-HBV who stayed in the hospital for more than 2 weeks were retrospectively recruited.The predictive accuracy of MELD,MELD-based indices,and their dynamic change (△) were compared using the area under the receiver operating characteristic curve method.The associations between mortality and patient characteristics were studied by univariate and multivariate analyses.Results The 3-month mortality was 43.6%.The largest concordance (c) statistic predicting 3-month mortality was the MELD score at the end of 2 weeks of admission (0.8),followed by the MELD:sodium ratio (MESO) (0.796) and integrated MELD (iMELD) (0.758) scores,△MELD (0.752),△MESO (0.729),and MELD plus sodium (MELD-Na) (0.728) scores.In multivariate Logistic regression analysis,the independent factors predicting prognosis were hepatic encephalopathy (OR=-3.466),serum creatinine,international normalized ratio (INR),and total bilirubin at the end of 2 weeks of admission (OR=10.302,6.063,5.208,respectively),and cholinesterase on admission (OR=0.255).This regression model had a greater prognostic value (c=0.85,95% Cl 0.791-0.909) compared to the MELD score at the end of 2 weeks of admission (Z=4.9851,P=-0.0256).Conclusions MELD score at the end of 2 weeks of admission is a useful predictor for 3-month mortality in ACLF-HBV patients.Hepatic encephalopathy,serum creatinine,international normalized ratio,and total bilirubin at the end of 2 weeks of admission and cholinesterase on admission are

  18. Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors.

    Science.gov (United States)

    Minet, L; Gehr, R; Hatzopoulou, M

    2017-11-01

    The development of reliable measures of exposure to traffic-related air pollution is crucial for the evaluation of the health effects of transportation. Land-use regression (LUR) techniques have been widely used for the development of exposure surfaces, however these surfaces are often highly sensitive to the data collected. With the rise of inexpensive air pollution sensors paired with GPS devices, we witness the emergence of mobile data collection protocols. For the same urban area, can we achieve a 'universal' model irrespective of the number of locations and sampling visits? Can we trade the temporal representation of fixed-point sampling for a larger spatial extent afforded by mobile monitoring? This study highlights the challenges of short-term mobile sampling campaigns in terms of the resulting exposure surfaces. A mobile monitoring campaign was conducted in 2015 in Montreal; nitrogen dioxide (NO2) levels at 1395 road segments were measured under repeated visits. We developed LUR models based on sub-segments, categorized in terms of the number of visits per road segment. We observe that LUR models were highly sensitive to the number of road segments and to the number of visits per road segment. The associated exposure surfaces were also highly dissimilar. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Diverse Short-Term Dynamics of Inhibitory Synapses Converging on Striatal Projection Neurons: Differential Changes in a Rodent Model of Parkinson’s Disease

    Directory of Open Access Journals (Sweden)

    Janet Barroso-Flores

    2015-01-01

    Full Text Available Most neurons in the striatum are projection neurons (SPNs which make synapses with each other within distances of approximately 100 µm. About 5% of striatal neurons are GABAergic interneurons whose axons expand hundreds of microns. Short-term synaptic plasticity (STSP between fast-spiking (FS interneurons and SPNs and between SPNs has been described with electrophysiological and optogenetic techniques. It is difficult to obtain pair recordings from some classes of interneurons and due to limitations of actual techniques, no other types of STSP have been described on SPNs. Diverse STSPs may reflect differences in presynaptic release machineries. Therefore, we focused the present work on answering two questions: Are there different identifiable classes of STSP between GABAergic synapses on SPNs? And, if so, are synapses exhibiting different classes of STSP differentially affected by dopamine depletion? Whole-cell voltage-clamp recordings on SPNs revealed three classes of STSPs: depressing, facilitating, and biphasic (facilitating-depressing, in response to stimulation trains at 20 Hz, in a constant ionic environment. We then used the 6-hydroxydopamine (6-OHDA rodent model of Parkinson’s disease to show that synapses with different STSPs are differentially affected by dopamine depletion. We propose a general model of STSP that fits all the dynamics found in our recordings.

  20. Diverse Short-Term Dynamics of Inhibitory Synapses Converging on Striatal Projection Neurons: Differential Changes in a Rodent Model of Parkinson's Disease

    Science.gov (United States)

    Herrera-Valdez, Marco A.; Lopez-Huerta, Violeta Gisselle; Galarraga, Elvira

    2015-01-01

    Most neurons in the striatum are projection neurons (SPNs) which make synapses with each other within distances of approximately 100 µm. About 5% of striatal neurons are GABAergic interneurons whose axons expand hundreds of microns. Short-term synaptic plasticity (STSP) between fast-spiking (FS) interneurons and SPNs and between SPNs has been described with electrophysiological and optogenetic techniques. It is difficult to obtain pair recordings from some classes of interneurons and due to limitations of actual techniques, no other types of STSP have been described on SPNs. Diverse STSPs may reflect differences in presynaptic release machineries. Therefore, we focused the present work on answering two questions: Are there different identifiable classes of STSP between GABAergic synapses on SPNs? And, if so, are synapses exhibiting different classes of STSP differentially affected by dopamine depletion? Whole-cell voltage-clamp recordings on SPNs revealed three classes of STSPs: depressing, facilitating, and biphasic (facilitating-depressing), in response to stimulation trains at 20 Hz, in a constant ionic environment. We then used the 6-hydroxydopamine (6-OHDA) rodent model of Parkinson's disease to show that synapses with different STSPs are differentially affected by dopamine depletion. We propose a general model of STSP that fits all the dynamics found in our recordings. PMID:26167304

  1. Methodology to predict long-term cancer survival from short-term data using Tobacco Cancer Risk and Absolute Cancer Cure models

    Science.gov (United States)

    Mould, R. F.; Lederman, M.; Tai, P.; Wong, J. K. M.

    2002-11-01

    Three parametric statistical models have been fully validated for cancer of the larynx for the prediction of long-term 15, 20 and 25 year cancer-specific survival fractions when short-term follow-up data was available for just 1-2 years after the end of treatment of the last patient. In all groups of cases the treatment period was only 5 years. Three disease stage groups were studied, T1N0, T2N0 and T3N0. The models are the Standard Lognormal (SLN) first proposed by Boag (1949 J. R. Stat. Soc. Series B 11 15-53) but only ever fully validated for cancer of the cervix, Mould and Boag (1975 Br. J. Cancer 32 529-50), and two new models which have been termed Tobacco Cancer Risk (TCR) and Absolute Cancer Cure (ACC). In each, the frequency distribution of survival times of defined groups of cancer deaths is lognormally distributed: larynx only (SLN), larynx and lung (TCR) and all cancers (ACC). All models each have three unknown parameters but it was possible to estimate a value for the lognormal parameter S a priori. By reduction to two unknown parameters the model stability has been improved. The material used to validate the methodology consisted of case histories of 965 patients, all treated during the period 1944-1968 by Dr Manuel Lederman of the Royal Marsden Hospital, London, with follow-up to 1988. This provided a follow-up range of 20- 44 years and enabled predicted long-term survival fractions to be compared with the actual survival fractions, calculated by the Kaplan and Meier (1958 J. Am. Stat. Assoc. 53 457-82) method. The TCR and ACC models are better than the SLN model and for a maximum short-term follow-up of 6 years, the 20 and 25 year survival fractions could be predicted. Therefore the numbers of follow-up years saved are respectively 14 years and 19 years. Clinical trial results using the TCR and ACC models can thus be analysed much earlier than currently possible. Absolute cure from cancer was also studied, using not only the prediction models which

  2. Comparison of MELCOR modeling techniques and effects of vessel water injection on a low-pressure, short-term, station blackout at the Grand Gulf Nuclear Station

    Energy Technology Data Exchange (ETDEWEB)

    Carbajo, J.J.

    1995-06-01

    A fully qualified, best-estimate MELCOR deck has been prepared for the Grand Gulf Nuclear Station and has been run using MELCOR 1.8.3 (1.8 PN) for a low-pressure, short-term, station blackout severe accident. The same severe accident sequence has been run with the same MELCOR version for the same plant using the deck prepared during the NUREG-1150 study. A third run was also completed with the best-estimate deck but without the Lower Plenum Debris Bed (BH) Package to model the lower plenum. The results from the three runs have been compared, and substantial differences have been found. The timing of important events is shorter, and the calculated source terms are in most cases larger for the NUREG-1150 deck results. However, some of the source terms calculated by the NUREG-1150 deck are not conservative when compared to the best-estimate deck results. These results identified some deficiencies in the NUREG-1150 model of the Grand Gulf Nuclear Station. Injection recovery sequences have also been simulated by injecting water into the vessel after core relocation started. This marks the first use of the new BH Package of MELCOR to investigate the effects of water addition to a lower plenum debris bed. The calculated results indicate that vessel failure can be prevented by injecting water at a sufficiently early stage. No pressure spikes in the vessel were predicted during the water injection. The MELCOR code has proven to be a useful tool for severe accident management strategies.

  3. Evaluation of the Xpa-Deficient Transgenic Mouse Model for Short-Term Carcinogenicity Testing: 9-Month Studies with Haloperidol, Reserpine, Phenacetin, and D-Mannitol

    NARCIS (Netherlands)

    Lina, B.A.R.; Woutersen, R.A.; Bruijntjes, J.P.; Benthem, J. van; Berg, J.A.H. van den; Monbaliu, J.; Thoolen, B.J.J.M.; Beems, R.B.; Kreijl, C.F. van

    2004-01-01

    As part of the international evaluation program coordinated by ILSI/HESI, the potential of DNA repair deficient Xpa-/- mice and the double knockout Xpa-/-.p53+/- mice for short term carcinogenicity assays was evaluated. For comparison also wild-type C57BL/6 mice (WT) were included in these studies.

  4. Subacute administration of fluoxetine prevents short-term brain hypometabolism and reduces brain damage markers induced by the lithium-pilocarpine model of epilepsy in rats.

    Science.gov (United States)

    Shiha, Ahmed Anis; de Cristóbal, Javier; Delgado, Mercedes; Fernández de la Rosa, Rubén; Bascuñana, Pablo; Pozo, Miguel A; García-García, Luis

    2015-02-01

    The role of serotonin (5-hydroxytryptamine; 5-HT) in epileptogenesis still remains controversial. In this regard, it has been reported that serotonergic drugs can alter epileptogenesis in opposite ways. The main objective of this work was to investigate the effect of the selective 5-HT selective reuptake inhibitor (SSRI) fluoxetine administered subacutely (10mg/kg/day×7 days) on the eventual metabolic impairment induced by the lithium-pilocarpine model of epilepsy in rats. In vivo 2-deoxy-2-[(18)F]fluoro-d-glucose ([(18)F] FDG) positron emission tomography (PET) was performed to assess the brain glucose metabolic activity on days 3 and 30 after the insult. In addition, at the end of the experiment (day 33), several histochemical and neurochemical assessments were performed for checking the neuronal functioning and integrity. Three days after the insult, a marked reduction of [(18)F] FDG uptake (about 30% according to the brain region) was found in all brain areas studied. When evaluated on day 30, although a hypometabolism tendency was observed, no statistically significant reduction was present in any region analyzed. In addition, lithium-pilocarpine administration was associated with medium-term hippocampal and cortical damage, since it induced neurodegeneration, glial activation and augmented caspase-9 expression. Regarding the effect of fluoxetine, subacute treatment with this SSRI did not significantly reduce the mortality rate observed after pilocarpine-induced seizures. However, fluoxetine did prevent not only the short-term metabolic impairment, but also the aforementioned signs of neuronal damage in surviving animals to lithium-pilocarpine protocol. Finally, fluoxetine increased the density of GABAA receptor both at the level of the dentate gyrus and CA1-CA2 regions in pilocarpine-treated animals. Overall, our data suggest a protective role for fluoxetine against pilocarpine-induced brain damage. Moreover, this action may be associated with an increase of

  5. Corneal-protective effects of an artificial tear containing sodium hyaluronate and castor oil on a porcine short-term dry eye model.

    Science.gov (United States)

    Hasegawa, Takashi; Amako, Hideki; Yamamoto, Takeshi; Tazawa, Mariko; Sakamoto, Yuji

    2014-09-01

    The corneal-protective effects of an artificial tear containing sodium hyaluronate (SH) and castor oil (CO) were evaluated on a porcine short-term dry eye model. Fresh porcine eyes with an intact cornea were treated with an artificial tear of saline, SH solution (0.1%, 0.5% or 1%), CO solution (0.5%, 1% or 5%) or a mixture solution containing 0.5% SH and 1% CO and then desiccated for 60, 90 or 180 min. To assess corneal damage, the eyes were stained with methylene blue (MB) or lissamine green (LG). The staining score of MB, absorbance of MB extracted from the cornea and staining density of LG increased significantly with increasing desiccation time in untreated and all artificial tear-treated eyes, although there were no significant differences in staining scores and absorbance of MB between eyes treated continuously with saline and 1% SH-treated ones at 60 and 90 min of desiccation or the mixture-treated eyes at 60 min of desiccation. No significant differences in the staining density of LG were also found between continuous saline-treated eyes and ones desiccated for 60 min and treated with 1% SH and the mixture. Mild cytoplasmic vacuolations were histopathologically observed in the basal and wing cells in eyes desiccated for 60 min and treated with 1% SH and the mixture. The mixture solution containing 0.5% SH and 1% CO has protective effects against corneal desiccation similar to those of 1% SH and would be helpful as an artificial tear.

  6. Health economic modelling to assess short-term costs of maternal overweight, gestational diabetes and related macrosomia – a pilot evaluation

    Directory of Open Access Journals (Sweden)

    Irene eLenoir-Wijnkoop

    2015-05-01

    Full Text Available Background: Despite the interest in the impact of overweight and obesity on public health, little is known about the social and economic impact of being born large for gestational age or macrosomic. Both conditions are related to maternal obesity and/or gestational diabetes (GDM and associated with increased morbidity for mother and child in the perinatal period. Poorly controlled diabetes during pregnancy, pre- pregnancy maternal obesity and/or excessive maternal weight gain during pregnancy are associated with intermittent periods of fetal exposure to hyperglycemia and subsequent hyperinsulinemia, leading to increased birth weight (e.g. macrosomia, body adiposity and glycogen storage in the liver. Macrosomia is associated with an increased risk of developing obesity and type 2 diabetes mellitus later in life.Objective: Provide insight in the short-term health-economic impact of maternal overweight, gestational diabetes (GDM and related macrosomia. To this end, a health economic framework was designed. This pilot study also aims to encourage further health technology assessments, based on country- and population-specific data. Results: The estimation of the direct health-economic burden of maternal overweight, GDM and related macrosomia indicates that associated healthcare expenditures are substantial. The calculation of a budget impact of GDM, based on a conservative approach of our model, using USA costing data, indicates an annual cost of more than $1,8 billion without taking into account long-term consequences.Conclusion: Although overweight and obesity are a recognized concern worldwide, less attention has been given to the health economic consequences of these conditions in women of child-bearing age and their offspring. The presented outcomes underline the need for preventive management strategies and public health interventions on life style, diet and physical activity. Also, the predisposition in people of Asian ethnicity to develop

  7. Prognostic indicators influencing short term outcomes among ...

    African Journals Online (AJOL)

    Prognostic indicators influencing short term outcomes among operated head injury patients ... AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search ... indicators for short term outcomes in operated head injury patients at KCMC.

  8. Analog VLSI Circuits for Short-Term Dynamic Synapses

    Directory of Open Access Journals (Sweden)

    Shih-Chii Liu

    2003-06-01

    Full Text Available Short-term dynamical synapses increase the computational power of neuronal networks. These synapses act as additional filters to the inputs of a neuron before the subsequent integration of these signals at its cell body. In this work, we describe a model of depressing and facilitating synapses derived from a hardware circuit implementation. This model is equivalent to theoretical models of short-term synaptic dynamics in network simulations. These circuits have been added to a network of leaky integrate-and-fire neurons. A cortical model of direction-selectivity that uses short-term dynamic synapses has been implemented with this network.

  9. Implementation of short-term prediction

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L.; Joensen, A.; Giebel, G. [and others

    1999-03-01

    This paper will giver a general overview of the results from a EU JOULE funded project (`Implementing short-term prediction at utilities`, JOR3-CT95-0008). Reference will be given to specialised papers where applicable. The goal of the project was to implement wind farm power output prediction systems in operational environments at a number of utilities in Europe. Two models were developed, one by Risoe and one by the Technical University of Denmark (DTU). Both prediction models used HIRLAM predictions from the Danish Meteorological Institute (DMI). (au) EFP-94; EU-JOULE. 11 refs.

  10. 基于PSO优化LSSVM的短期风速预测%The short-term wind speed forecast analysis based on the PSO-LSSVM predict model

    Institute of Scientific and Technical Information of China (English)

    孙斌; 姚海涛

    2012-01-01

    为了提高风电场风速短期预测的精确性,提出了基于粒子群算法优化最小二乘支持向量机的预测方法.首先求出风速时间序列的嵌入维数和延迟时间,进而对混沌风速时间序列进行相空间重构.利用粒子群算法对最小二乘支持向量机进行参数优化,然后利用优化后的最小二乘支持向量机模型对相空间重构后的风速时间序列进行预测,预测结果表明基于粒子群优化的最小二乘支持向量机的预测效果满足了精度要求.同时运用了支持向量机和BP神经网络模型进行预测,仿真结果表明,基于粒子群优化的最小二乘支持向量机预测方法具有预测精度高,预测速度快的优点,因此具有很高的工程实际应用意义.%In order to improve the accuracy of short-term wind speed forecast, this paper proposes a least squares support vector machine (LSSVM) model optimized by the particle swarm optimization (PSO). The phase space of the chaotic wind speed time series is reconstructed by calculating the embedding dimension and the delay time of the wind speed time series. The PSO is used to optimize the parameters of the LSSVM. Then the improved LSSVM model can be used to forecast the wind speed. The results show the improved LSSVM can meet the accuracy requirements. At the same time, this paper uses the SVM prediction model and the BP neural network prediction model to forecast the wind speed time series. The simulation results show that the PSO-LSSVM prediction model is more efficient and accurate. So it can be widely used in engineering practice.

  11. Short-term sleep deprivation stimulates hippocampal neurogenesis in rats following global cerebral ischemia/reperfusion.

    Directory of Open Access Journals (Sweden)

    Oumei Cheng

    Full Text Available Sleep deprivation (SD plays a complex role in central nervous system (CNS diseases. Recent studies indicate that short-term SD can affect the extent of ischemic damage. The aim of this study was to investigate whether short-term SD could stimulate hippocampal neurogenesis in a rat model of global cerebral ischemia/reperfusion (GCIR.One hundred Sprague-Dawley rats were randomly divided into Sham, GCIR and short-term SD groups based on different durations of SD; the short-term SD group was randomly divided into three subgroups: the GCIR+6hSD*3d-treated, GCIR+12hSD-treated and GCIR+12hSD*3d-treated groups. The GCIR rat model was induced via the bilateral occlusion of the common carotid arteries and hemorrhagic hypotension. The rats were sleep-deprived starting at 48 h following GCIR. A Morris water maze test was used to assess learning and memory ability; cell proliferation and differentiation were analyzed via 5-bromodeoxyuridine (BrdU and neuron-specific enolase (NSE, respectively, at 14 and 28 d; the expression of hippocampal BDNF was measured after 7 d.The different durations of short-term SD designed in our experiment exhibited improvement in cognitive function as well as increased hippocampal BDNF expression. Additionally, the short-term SD groups also showed an increased number of BrdU- and BrdU/NSE-positive cells compared with the GCIR group. Of the three short-term SD groups, the GCIR+12hSD*3d-treated group experienced the most substantial beneficial effects.Short-term SD, especially the GCIR+12hSD*3d-treated method, stimulates neurogenesis in the hippocampal dentate gyrus (DG of rats that undergo GCIR, and BDNF may be an underlying mechanism in this process.

  12. Research on Grey Verhulst Models for Short-term Wind Speed Prediction%基于灰色Verhulst模型的短期风速预测研究

    Institute of Scientific and Technical Information of China (English)

    王子赟; 纪志成

    2013-01-01

    风速预测在保持风力发电系统稳定、风力发电功率预报、风电并网接入等方面都具有重要的应用.为了提高风速预测的精确性,提出了一种基于新陈代谢思想的灰色Verhulst模型的风速预测方法.该方法首先对灰色GM (1,1)模型和灰色Verhulst模型进行改进,其次引入了“新陈代谢”的概念,即在每一次风速预测的迭代过程中用风速真实序列的最新数据替代原有序列的最老数据,在不增加迭代维数的条件下,不断更新灰色Verhulst模型,将更新后的Verhulst模型进行优化,实现精确的风速预测.通过对实际风场风速数据的采集,运用该灰色Verhulst模型预测风速.实践仿真结果表明,与传统预测方法相比,此方法能有效的降低短期风速预测的误差,应用前景十分广阔.%Wind speed forecast technology is playing an increasingly important role in maintaining the wind power system's stability,forecasting wind power generation and connecting electric grid.In the paper,the forecast problems of wind speed are considered.In order to enhance the rediction accuracy of the wind speed,a grey Verhulst model based prediction method is introduced to forecast wind speed in a short period.Moreover,a metabolic Verhulst model is presented based on the iterative thought to update the prediction model at each calculation,through replacing the oldest data by the current new data,without increasing the dimension of iterative wind speed vector.Collect the real data from wind farm and use the proposed method to forecast wind speed.The simulation results show that the proposed metabolic Verhulst method can effectively predict the wind speed in short term.

  13. Operations Management in Short Term Power Markets

    DEFF Research Database (Denmark)

    Heide-Jørgensen, Ditte Mølgård

    minutes. The stochastic input is the electricity price modelled as a time inhomogenous Markov chain that the power producer uses to maximise profits. To maintain computational tractability with such high time resolution and stochastics the model is solved with dynamic programming. The two models differ......Electricity market models have often been modelled as deterministic or at most two-stage stochastic models with an hourly time resolution. This thesis looks into possible ways of extending such models and formulating new models to handle both higher time resolution than hourly and stochastics....... The first is an introduction to the background for the work with stochastic electricity market models with a high time resolution. It is followed by three self-contained chapters. The second chapter Short-term balancing of supply and demand in an electricity system: forecasting and scheduling...

  14. Onboard Short Term Plan Viewer

    Science.gov (United States)

    Hall, Tim; LeBlanc, Troy; Ulman, Brian; McDonald, Aaron; Gramm, Paul; Chang, Li-Min; Keerthi, Suman; Kivlovitz, Dov; Hadlock, Jason

    2011-01-01

    Onboard Short Term Plan Viewer (OSTPV) is a computer program for electronic display of mission plans and timelines, both aboard the International Space Station (ISS) and in ISS ground control stations located in several countries. OSTPV was specifically designed both (1) for use within the limited ISS computing environment and (2) to be compatible with computers used in ground control stations. OSTPV supplants a prior system in which, aboard the ISS, timelines were printed on paper and incorporated into files that also contained other paper documents. Hence, the introduction of OSTPV has both reduced the consumption of resources and saved time in updating plans and timelines. OSTPV accepts, as input, the mission timeline output of a legacy, print-oriented, UNIX-based program called "Consolidated Planning System" and converts the timeline information for display in an interactive, dynamic, Windows Web-based graphical user interface that is used by both the ISS crew and ground control teams in real time. OSTPV enables the ISS crew to electronically indicate execution of timeline steps, launch electronic procedures, and efficiently report to ground control teams on the statuses of ISS activities, all by use of laptop computers aboard the ISS.

  15. Long short-term memory.

    Science.gov (United States)

    Hochreiter, S; Schmidhuber, J

    1997-11-15

    Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

  16. Modeling the runoff regime of the glacierised upper Aconcagua River Basin using a physically-based distributed hydrological model: the value of short term glaciological observations

    Science.gov (United States)

    Ragettli, S.; Pellicciotti, F.; Molnar, D.; Rimkus, S.; Helbing, J.; Escobar, F.; Burlando, P.

    2010-12-01

    In the Central Andes of Chile the interactions between snow, glaciers and water resources are governed by a distinct climatological forcing. Summers are dry and stable, with precipitation close to zero, low relative humidity and intense solar radiation. During the summer months, water originates almost exclusively from snow and ice melt. Evidence of glaciers retreat and changes in the seasonal snow cover suggests that climate change might have an impact on the water resources in the area. We use the physically-based, spatially-distributed hydrological model TOPKAPI to study the processes governing the exchange between the climate, snow and ice in the upper Aconcagua River Basin. The model incorporates the melting of snow and ice based on a simplified energy-balance approach (ETI model) and the routing of melt water through the glacial system. The model has numerous empirical parameters used in the computation of the single components of the hydrological cycle, the determination of which might lead to problems of equifinality. To address this issue we set up a rigorous calibration procedure that allows calibration of the main model parameters in three different steps by separating parameters governing distinct processes. We evaluate the parameters’ transferability in time and investigate the differences in model parameters and performance that result from applying the model at different spatial scales. The model ability to simulate the relevant processes is tested against a data set of meteorological data, measurements of surface ablation and glacier runoff at the snout of the Juncal Norte Glacier during two ablation seasons. Modeled snow height is compared to snow maps derived from terrestrial photos and MODIS images. Results show that the magnitude of snow and icemelt rates on the glacier tongue is correctly reproduced, but simulations at higher elevation have a larger uncertainty. Crucial factors affecting model performance are the model ability to simulate the

  17. Short-term geomorphological evolution of proglacial systems

    Science.gov (United States)

    Carrivick, Jonathan L.; Heckmann, Tobias

    2017-06-01

    Proglacial systems are amongst the most rapidly changing landscapes on Earth, as glacier mass loss, permafrost degradation and more episodes of intense rainfall progress with climate change. This review addresses the urgent need to quantitatively define proglacial systems not only in terms of spatial extent but also in terms of functional processes. It firstly provides a critical appraisal of prevailing conceptual models of proglacial systems, and uses this to justify compiling data on rates of landform change in terms of planform, horizontal motion, elevation changes and sediment budgets. These data permit us to produce novel summary conceptual diagrams that consider proglacial landscape evolution in terms of a balance of longitudinal and lateral water and sediment fluxes. Throughout, we give examples of newly emerging datasets and data processing methods because these have the potential to assist with the issues of: (i) a lack of knowledge of proglacial systems within high-mountain, arctic and polar regions, (ii) considerable inter- and intra-catchment variability in the geomorphology and functioning of proglacial systems, (iii) problems with the magnitude of short-term geomorphological changes being at the threshold of detection, (iv) separating short-term variability from longer-term trends, and (v) of the representativeness of plot-scale field measurements for regionalisation and for upscaling. We consider that understanding of future climate change effects on proglacial systems requires holistic process-based modelling to explicitly consider feedbacks and linkages, especially between hillslope and valley-floor components. Such modelling must be informed by a new generation of repeated distributed topographic surveys to detect and quantify short-term geomorphological changes.

  18. Health economic modeling to assess short-term costs of maternal overweight, gestational diabetes, and related macrosomia – a pilot evaluation

    Science.gov (United States)

    Lenoir-Wijnkoop, Irene; van der Beek, Eline M.; Garssen, Johan; Nuijten, Mark J. C.; Uauy, Ricardo D.

    2015-01-01

    Background: Despite the interest in the impact of overweight and obesity on public health, little is known about the social and economic impact of being born large for gestational age or macrosomic. Both conditions are related to maternal obesity and/or gestational diabetes mellitus (GDM) and associated with increased morbidity for mother and child in the perinatal period. Poorly controlled diabetes during pregnancy, pre- pregnancy maternal obesity and/or excessive maternal weight gain during pregnancy are associated with intermittent periods of fetal exposure to hyperglycemia and subsequent hyperinsulinemia, leading to increased birth weight (e.g., macrosomia), body adiposity, and glycogen storage in the liver. Macrosomia is associated with an increased risk of developing obesity and type 2 diabetes mellitus later in life. Objective: Provide insight in the short-term health-economic impact of maternal overweight, GDM, and related macrosomia. To this end, a health economic framework was designed. This pilot study also aims to encourage further health technology assessments, based on country- and population-specific data. Results: The estimation of the direct health-economic burden of maternal overweight, GDM and related macrosomia indicates that associated healthcare expenditures are substantial. The calculation of a budget impact of GDM, based on a conservative approach of our model, using USA costing data, indicates an annual cost of more than $1,8 billion without taking into account long-term consequences. Conclusion: Although overweight and obesity are a recognized concern worldwide, less attention has been given to the health economic consequences of these conditions in women of child-bearing age and their offspring. The presented outcomes underline the need for preventive management strategies and public health interventions on life style, diet and physical activity. Also, the predisposition in people of Asian ethnicity to develop diabetes emphasizes the

  19. Short-term energy outlook, January 1999

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1999-01-01

    The Energy Information Administration (EIA) prepares the Short-Term Energy Outlook (energy supply, demand, and price projections) monthly. The forecast period for this issue of the Outlook extends from January 1999 through December 2000. Data values for the fourth quarter 1998, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the January 1999 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 28 figs., 19 tabs.

  20. Short term depression unmasks the ghost frequency.

    Directory of Open Access Journals (Sweden)

    Tjeerd V Olde Scheper

    Full Text Available Short Term Plasticity (STP has been shown to exist extensively in synapses throughout the brain. Its function is more or less clear in the sense that it alters the probability of synaptic transmission at short time scales. However, it is still unclear what effect STP has on the dynamics of neural networks. We show, using a novel dynamic STP model, that Short Term Depression (STD can affect the phase of frequency coded input such that small networks can perform temporal signal summation and determination with high accuracy. We show that this property of STD can readily solve the problem of the ghost frequency, the perceived pitch of a harmonic complex in absence of the base frequency. Additionally, we demonstrate that this property can explain dynamics in larger networks. By means of two models, one of chopper neurons in the Ventral Cochlear Nucleus and one of a cortical microcircuit with inhibitory Martinotti neurons, it is shown that the dynamics in these microcircuits can reliably be reproduced using STP. Our model of STP gives important insights into the potential roles of STP in self-regulation of cortical activity and long-range afferent input in neuronal microcircuits.

  1. The Nature of Short-Term Consolidation in Visual Working Memory.

    Science.gov (United States)

    Ricker, Timothy J; Hardman, Kyle O

    2017-07-13

    Short-term consolidation is the process by which stable working memory representations are created. This process is fundamental to cognition yet poorly understood. The present work examines short-term consolidation using a Bayesian hierarchical model of visual working memory recall to determine the underlying processes at work. Our results show that consolidation functions largely through changing the proportion of memory items successfully maintained until test. Although there was some evidence that consolidation affects representational precision, this change was modest and could not account for the bulk of the consolidation effect on memory performance. The time course of the consolidation function and selective influence of consolidation on specific serial positions strongly indicates that short-term consolidation induces an attentional blink. The blink leads to deficits in memory for the immediately following item when time pressure is introduced. Temporal distinctiveness accounts of the consolidation process are tested and ruled out. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  2. Short-term energy outlook, July 1998

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-07-01

    The Energy Information Administration (EIA) prepares The Short-Term Energy Outlook (energy supply, demand, and price projections) monthly for distribution on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. In addition, printed versions of the report are available to subscribers in January, April, July and October. The forecast period for this issue of the Outlook extends from July 1998 through December 1999. Values for second quarter of 1998 data, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the July 1998 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. 28 figs., 19 tabs.

  3. Short-term energy outlook, July 1998

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-07-01

    The Energy Information Administration (EIA) prepares The Short-Term Energy Outlook (energy supply, demand, and price projections) monthly for distribution on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. In addition, printed versions of the report are available to subscribers in January, April, July and October. The forecast period for this issue of the Outlook extends from July 1998 through December 1999. Values for second quarter of 1998 data, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the July 1998 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. 28 figs., 19 tabs.

  4. The model of short term traffic flow prediction based on the random forest%基于随机森林模型的短时交通流预测方法

    Institute of Scientific and Technical Information of China (English)

    程政; 陈贤富

    2016-01-01

    The short term traffic flow prediction is very important to the application of intelligent traffic system ( ITS) , but it needs more flexible model for the strong nonlinear and noisy and processes lots of data in short time. This article discusses the random forest model for the predic-tion of short term traffic flow. The model has characters such as stronger generalization, easy to adjust the parameter, effective computation and quality stability. It extracts the principal features as the variables to form input space after observing the variation of traffic flow in the longer term. The prediction accuracy of the model on the test set is 94% after the model trained on the training set. Compared with the popular sup-port vector machine( SVM) , the random forest has better accuracy prediction. And the random forest is better than SVM on the efficiency, usa-bility and the extension of future usage.%短时交通流的准确高效预测对于智能交通系统的应用十分关键,但较强的非线性和噪声干扰使其对模型的灵活性要求较高,并且还需在尽可能短的时间内处理大量的数据。因此,讨论了用随机森林模型对短时交通流进行预测,该模型具有比单棵树更强的泛化能力,参数调节方便,计算高效,且稳定性好。观察交通流数据在较长时间跨度上的变化后,提取出主要特征变量构造输入空间,对模型进行训练后,在测试集上的预测准确率约为94%。与目前广泛使用的支持向量机模型进行对比分析,结果显示随机森林预测不仅准确率稍好于支持向量机,而且在效率、易用性及未来应用的扩展上都要优于支持向量机。

  5. Short-term intercultural psychotherapy: ethnographic inquiry.

    Science.gov (United States)

    Seeley, Karen M

    2004-01-01

    This article examines the challenges specific to short-term intercultural treatments and recently developed approaches to intercultural treatments based on notions of cultural knowledge and cultural competence. The article introduces alternative approaches to short-term intercultural treatments based on ethnographic inquiry adapted for clinical practice. Such approaches allow clinicians conducting short-term intercultural treatments to foreground clients' indigenous conceptions of selfhood, mind, relationship, and emotional disturbance, and thus to more fully grasp their internal, interpersonal, and external worlds. This article demonstrates the uses of clinically adapted ethnographic inquiry in three short-term intercultural cases.

  6. Prediction Model for Short-term Wind Speed Based on Improved EMD and RBFNN%基于改进EMD 和RBFNN的短期风速预测模型

    Institute of Scientific and Technical Information of China (English)

    尹子中; 陈众; 黄健; 俞晓鹏; 邱强杰; 文亮

    2016-01-01

    In order to improve precision of prediction on short-term wind speed,a prediction model for short-term wind speed combining improved empirical model decomposition (EMD)and radial basis function neural network (RBFNN)is pro-posed.Firstly,extreme point symmetric extension is used for processing on preprocessed wind speed sequences so as to re-strain fringe effect in decomposition caused by traditional EMD,and piecewise cubic Hermite interpolation method is used to solve overshoot or undershoot of traditional EMD envelope lines.Then,improved EMD is used to decompose wind speed se-quences into different intrinsic mode function (IMF)components and respective RBFNN model is constructed for predic-tion.Finally,prediction results of various components are reconstructed and overlayed to get final predicted value of original wind speed.Experimental results indicate that the improved EMD-RBFNN prediction model is able to effectively improve precision of prediction on wind speed and has certain application value.%为提高短期风速预测精度,提出改进经验模态分解法(empirical mode decomposition,EMD)与径向基函数神经网络(radial basis function neural network,RBFNN)相结合的短期风速预测模型。首先,利用极值点对称延拓法对预处理过的风速序列进行处理,以抑制传统EMD在分解过程中所引起的边缘效应,并引用分段三次埃米特插值法解决传统EMD包络线的过冲或欠冲问题;然后,利用改进 EMD 将风速序列分解成各本征模态(in-trinsic mode function,IMF)分量,再针对各分量分别构建各自的 RBFNN 模型进行预测;最后,将各分量的预测结果进行重构、叠加,得到最终的原始风速预测值。实验结果表明,改进的 EMD-RBFNN 预测模型能有效地提高风速预测精度,并具有一定的应用价值。

  7. Short-term memory and dual task performance

    Science.gov (United States)

    Regan, J. E.

    1982-01-01

    Two hypotheses concerning the way in which short-term memory interacts with another task in a dual task situation are considered. It is noted that when two tasks are combined, the activity of controlling and organizing performance on both tasks simultaneously may compete with either task for a resource; this resource may be space in a central mechanism or general processing capacity or it may be some task-specific resource. If a special relationship exists between short-term memory and control, especially if there is an identity relationship between short-term and a central controlling mechanism, then short-term memory performance should show a decrement in a dual task situation. Even if short-term memory does not have any particular identity with a controlling mechanism, but both tasks draw on some common resource or resources, then a tradeoff between the two tasks in allocating resources is possible and could be reflected in performance. The persistent concurrence cost in memory performance in these experiments suggests that short-term memory may have a unique status in the information processing system.

  8. Short-term Power Load Forecasting Based on Gray Theory

    Directory of Open Access Journals (Sweden)

    Cui Herui

    2013-11-01

    Full Text Available Power load forecasting provides the basis for the preparation of power planning, especially the accurate short-term power load forecasting. It can formulate power rationing program of area load reliably and timely, to maintain the normal production and life. This article describes the gray prediction method, and improves GM (1,1 model via processing the original data sequence smoothly, using the correction model of parameteramending parameter values​​, adding the residual model, and also applying the idea of the metabolism. It conducts an empirical analysis of the 10KV large cable of Guigang Power Supply Bureau in Nan Ping, and verifies the limitations of ordinary gray theory. The improved gray model has a higher prediction accuracy than the conventional GM (1,1 model.  

  9. Short Term Load Forecast Using Wavelet Neural Network

    Institute of Scientific and Technical Information of China (English)

    Gui Min; Rong Fei; Luo An

    2005-01-01

    This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impacts on load level were used in the proposed forecasting model. The model used the three-layer feed forward network trained by the error back-propagation algorithm. To enhance the forecasting accuracy by neural networks, wavelet multi-resolution analysis method was introduced to pre-process these data and reconstruct the predicted output. The proposed model has been evaluated with actual data of electricity load and temperature of Hunan Province. The simulation results show that the model is capable of providing a reasonable forecasting accuracy in STLF.

  10. Short-term managerial contracts facilitate cartels

    NARCIS (Netherlands)

    Han, M.A.

    2010-01-01

    This paper shows how a series of commonly observed short-term CEO employment contracts improves cartel stability compared to a long-term contract. When a manager’s short-term appointment is renewed if and only if the firm hits a certain profit target, then (a) defection from collusion results in sup

  11. Statistical properties of short term price trends in high frequency stock market data

    CERN Document Server

    Sieczka, P; Sieczka, Pawe{\\l}; Ho{\\l}yst, Janusz A.

    2007-01-01

    We investigated distributions of short term price trends for high frequency stock market data. A number of trends as a function of their lengths was measured. We found that such a distribution does not fit to results following from an uncorrelated stochastic process. We proposed a simple model with a memory that gives a qualitative agreement with real data.

  12. Short-Term Planning of Hybrid Power System

    Science.gov (United States)

    Knežević, Goran; Baus, Zoran; Nikolovski, Srete

    2016-07-01

    In this paper short-term planning algorithm for hybrid power system consist of different types of cascade hydropower plants (run-of-the river, pumped storage, conventional), thermal power plants (coal-fired power plants, combined cycle gas-fired power plants) and wind farms is presented. The optimization process provides a joint bid of the hybrid system, and thus making the operation schedule of hydro and thermal power plants, the operation condition of pumped-storage hydropower plants with the aim of maximizing profits on day ahead market, according to expected hourly electricity prices, the expected local water inflow in certain hydropower plants, and the expected production of electrical energy from the wind farm, taking into account previously contracted bilateral agreement for electricity generation. Optimization process is formulated as hourly-discretized mixed integer linear optimization problem. Optimization model is applied on the case study in order to show general features of the developed model.

  13. Gum arabic-coated radioactive gold nanoparticles cause no short-term local or systemic toxicity in the clinically relevant canine model of prostate cancer

    Directory of Open Access Journals (Sweden)

    Axiak-Bechtel SM

    2014-10-01

    Full Text Available Sandra M Axiak-Bechtel,1 Anandhi Upendran,2,3 Jimmy C Lattimer,1 James Kelsey,3,4 Cathy S Cutler,4 Kim A Selting,1 Jeffrey N Bryan,1 Carolyn J Henry,1,5 Evan Boote,6 Deborah J Tate,1 Margaret E Bryan,7 Kattesh V Katti,3,8 Raghuraman Kannan3,8 1Department of Veterinary Medicine and Surgery, 2Department of Physics, University of Missouri, Columbia, MO, USA; 3Nanoparticle Biochem, Inc., and Shasun-NBI LLC, Columbia, MO, USA; 4Missouri University Research Reactor, 5Department of Internal Medicine, University of Missouri, Columbia, MO, USA; 6Spectrum Health, Grand Rapids, MI, USA; 7Department of Statistics, 8Department of Radiology, University of Missouri, Columbia, MO, USA Introduction: Gum arabic-coated radioactive gold nanoparticles (GA-198AuNPs offer ­several advantages over traditional brachytherapy in the treatment of prostate cancer, including homogenous dose distribution and higher dose-rate irradiation. Our objective was to determine the short-term safety profile of GA-198AuNPs injected intralesionally. We proposed that a single treatment of GA-198AuNPs would be safe with minimal-to-no evidence of systemic or local toxicity.Methods: Nine dogs with spontaneously occurring prostatic cancer were treated. Injections were performed with ultrasound or computerized tomography guidance. Complete blood counts, chemistry panels, and urinalyses were performed at weekly intervals for 1 month and imaging was repeated 4 weeks postinjection. Planar scintigraphic images were obtained within 30 minutes of injection.Results: No statistically significant difference was found in any hematologic or biochemical parameter studied, nor was any evidence of tumor swelling or abscessation found in eight dogs with repeat imaging; one dog died secondary to urethral obstruction 12 days following injection. At 30 minutes postinjection, an average of 53% of injected dose in seven dogs was retained in the prostate, with loss of remaining activity in the bladder and

  14. Involvement of striatal lipid peroxidation and inhibition of calcium influx into brain slices in neurobehavioral alterations in a rat model of short-term oral exposure to manganese.

    Science.gov (United States)

    Avila, Daiana Silva; Gubert, Priscila; Fachinetto, Roselei; Wagner, Caroline; Aschner, Michael; Rocha, João Batista Teixeira; Soares, Félix Alexandre Antunes

    2008-11-01

    Manganese is an essential element for biological systems, nevertheless occupational exposure to high levels of Mn can lead to neurodegenerative disorder, characterized by excessive Mn accumulation, especially in astrocytes of basal ganglia and symptoms closely resembling idiopathic Parkinson's disease (PD). The purpose of this study was to evaluate behavioral and biochemical alterations in adult rats exposed for 30 days to 10 and 25mg/mL of MnCl(2) in their drinking water. MnCl(2) intoxicated rats showed impaired locomotor activity in comparison to control animals. Furthermore, lipid peroxidation were increased, delta-aminolevulinate dehydratase (delta-ALA-D, an enzyme sensitive to pro-oxidant situations) activity was inhibited and (45)Ca(2+) influx into striatal slices was decreased in rats exposed to 25mg/mL of Mn, indicating that this brain region was markedly affected by short-term Mn exposure. In contrast, Mn exposure was not associated with characteristic extrapyramidal effects and did not modify protein oxidation, suggesting that the striatal damage represents early stages of Mn-induced damage. In addition, treatment with Mn was associated with reduced body weight gain, but there were no discernible alterations in liver and kidney function. In conclusion, Mn caused increased oxidative stress and decreased (45)Ca(2+) influx into the striatum, which are likely linked to impaired locomotor activity, but not with the occurrence of orofacial dyskinesia.

  15. A Conceptual Model for the Interaction between Carbon Content and Manganese Sulphide Inclusions in the Short-Term Seawater Corrosion of Low Carbon Steel

    Directory of Open Access Journals (Sweden)

    Robert E. Melchers

    2016-05-01

    Full Text Available The critical role of manganese sulphide (MnS inclusions for the initiation of the short-term growth of pitting or localized corrosion of low carbon steels has long been recognized. Classical results show that pitting probability and pitting severity increases with increased sulphide concentration for low carbon steels as a result of magnesium sulphides acting as local cathodes for initiating pitting corrosion. However, the iron carbides (cementite in steels can also act as local cathodes for initiation of pitting corrosion. Herein it is proposed that there is competition between pits for cathodic area and that this will determine the severity of pitting and general corrosion observed in extended exposures. Preliminary experimental data for immersion exposures of up to 56 days in natural seawater of three low carbon steels show, contrary to conventional wisdom, greater pit depths for the steels with lower S content. However, the pit depth results are consistent with lower C/S ratios. This is considered to support the concept of cathodic competition between C and S. It is proposed that this offers explanations for a number of other phenomena, including the thus far unexplained apparently higher reactivity of some MnS inclusions.

  16. Mechanisms of short-term plasticity at neuromuscular active zones of Drosophila

    Science.gov (United States)

    Hallermann, Stefan; Heckmann, Manfred; Kittel, Robert J.

    2010-01-01

    During short bursts of neuronal activity, changes in the efficacy of neurotransmitter release are governed primarily by two counteracting processes: (1) Ca2+-dependent elevations of vesicle release probability and (2) depletion of synaptic vesicles. The dynamic interplay of both processes contributes to the expression of activity-dependent synaptic plasticity. Here, we exploited various facets of short-term plasticity at the Drosophila neuromuscular junction to dissect these two processes. This enabled us to rigorously analyze different models of synaptic vesicle pools in terms of their size and mobilization properties. Independent of the specific model, we estimate ∼300 readily releasable vesicles with an average release probability of ∼50% in 1 mM extracellular calcium (∼5% in 0.4 mM extracellular calcium) under resting conditions. The models also helped interpreting the altered short-term plasticity of the previously reported mutant of the active zone component Bruchpilot (BRP). Finally, our results were independently confirmed through fluctuation analysis. Our data reveal that the altered short-term plasticity observed in BRP mutants cannot be accounted for by delocalized Ca2+ channels alone and thus suggest an additional role of BRP in short-term plasticity. PMID:20811513

  17. The role of short-term memory in semantic priming.

    Science.gov (United States)

    Beer, A L; Diehl, V A

    2001-07-01

    Two theories of priming were compared: spreading activation theories, in particular ACT, and compound-cue theories. Whereas ACT assumes that priming is a result of diffusing activation in long-term memory, compound-cue models suggest that priming results from a formation process of prime and target in short-term memory. Thirty-eight participants took part in a study that combined a digit span task with a double lexical decision task consisting of a prime and a target item. Digit span length (low, medium, and high) and prime type (related or unrelated word or nonword) were both within-subject variables. As expected, results showed significant priming effects. In favor of ACT, no interaction between digit span length and prime type was found. Additionally, a nonword inhibition effect (unrelated versus nonword prime) was found, which was predicted by compound-cue theories. This finding is discussed in terms of the process interference and response competition hypotheses.

  18. Analyzing Short-Term Disability Benefits.

    Science.gov (United States)

    Houff, James N.; Wiatrowski, William J.

    1989-01-01

    The Bureau of Labour Statistics has combined data on sick leave and sickness and accident insurance. Results show that short-term disability benefits vary by length of service and between the private and public sectors. (Author)

  19. Impaired short-term memory for pitch in congenital amusia.

    Science.gov (United States)

    Tillmann, Barbara; Lévêque, Yohana; Fornoni, Lesly; Albouy, Philippe; Caclin, Anne

    2016-06-01

    Congenital amusia is a neuro-developmental disorder of music perception and production. The hypothesis is that the musical deficits arise from altered pitch processing, with impairments in pitch discrimination (i.e., pitch change detection, pitch direction discrimination and identification) and short-term memory. The present review article focuses on the deficit of short-term memory for pitch. Overall, the data discussed here suggest impairments at each level of processing in short-term memory tasks; starting with the encoding of the pitch information and the creation of the adequate memory trace, the retention of the pitch traces over time as well as the recollection and comparison of the stored information with newly incoming information. These impairments have been related to altered brain responses in a distributed fronto-temporal network, associated with decreased connectivity between these structures, as well as in abnormalities in the connectivity between the two auditory cortices. In contrast, amusic participants׳ short-term memory abilities for verbal material are preserved. These findings show that short-term memory deficits in congenital amusia are specific to pitch, suggesting a pitch-memory system that is, at least partly, separated from verbal memory. This article is part of a Special Issue entitled SI: Auditory working memory. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Neural mechanisms of semantic interference and false recognition in short-term memory.

    Science.gov (United States)

    Atkins, Alexandra S; Reuter-Lorenz, Patricia A

    2011-06-01

    Decades of research using the Deese-Roediger-McDermott (DRM) paradigm have demonstrated that episodic memory is vulnerable to semantic distortion, and neuroimaging investigations of this phenomenon have shown dissociations between the neural mechanisms subserving true and false retrieval from long-term memory. Recently, false short-term memories have also been demonstrated, with false recognition of items related in meaning to memoranda encoded less than 5s earlier. Semantic interference is also evident in short-term memory, such that correct rejection of related lures is slowed relative to correct rejection of unrelated lures. The present research constitutes the first fMRI investigation of false recognition and semantic interference in short-term memory using a short-term DRM paradigm in which participants retained 4 semantic associates over a short 4-s filled retention interval. Results showed increased activation in the left mid-ventrolateral prefrontal cortex (BA45) associated with semantic interference, and significant correlations between these increases and behavioral measures of interference across subjects. Furthermore, increases in dorsolateral PFC occurred when related lures were correctly rejected versus falsely remembered. Compared with false recognition, true recognition was associated with increases in left fusiform gyrus, a finding consistent with the notion that increased perceptual processing may distinguish true from false recognition over both short and long retention intervals. Findings are discussed in relation to current models of interference resolution in short-term memory, and suggest that false short-term recognition occurs as a consequence of the failure of frontally mediated cognitive control processes which adjudicate semantic familiarity in support of accurate mnemonic retrieval.

  1. Influence of short-term experimental warming on heat-water processes of the active layer in a swamp meadow ecosystem of the Qinghai-Tibet Plateau

    Institute of Scientific and Technical Information of China (English)

    GuangSheng Liu; GenXu Wang

    2016-01-01

    Climate change is now evident in the Qinghai-Tibet Plateau (QTP), with impacts on the alpine ecosystem, particularly on water and heat balance between the active layer and the atmosphere. Thus, we document the basic characteristics of changes in the water and heat dynamics in response to experimental warming in a typical alpine swamp meadow ecosystem. Data sets under open top chambers (OTC) and the control manipulations were collected over a complete year. The results show that annual (2008) air temperatures of OTC-1 and OTC-2 were 6.7 °C and 3.5 °C warmer than the control. Rising temperature promotes plant growth and development. The freeze-thaw and isothermal days of OTCs appeared more frequently than the control, owing to comparably higher water and better vegetation conditions. OTCs soil moisture decreased with the decrease of soil depth; however, there was an obviously middle dry aquifer of the control, which is familiar in QTP. Moreover, experimental warming led to an increase in topsoil water content due to poorly drained swamp meadow ecosystem with higher organic matter content and thicker root horizons. The results of this study will have some contributions to alpine cold ecosystem water-heat process and water cycle under climate change.

  2. Nuisance forecasting. Univariate modelling and very-short-term forecasting of winter smog episodes; Immissionsprognose. Univariate Modellierung und Kuerzestfristvorhersage von Wintersmogsituationen

    Energy Technology Data Exchange (ETDEWEB)

    Schlink, U.

    1996-12-31

    The work evaluates specifically the nuisance data provided by the measuring station in the centre of Leipig during the period from 1980 to 1993, with the aim to develop an algorithm for making very short-term forecasts of excessive nuisances. Forecasting was to be univariate, i.e., based exclusively on the half-hourly readings of SO{sub 2} concentrations taken in the past. As shown by Fourier analysis, there exist three main and mutually independent spectral regions: the high-frequency sector (period < 12 hours) of unstable irregularities, the seasonal sector with the periods of 24 and 12 hours, and the low-frequency sector (period > 24 hours). After breaking the measuring series up into components, the low-frequency sector is termed trend component, or trend for short. For obtaining the components, a Kalman filter is used. It was found that smog episodes are most adequately described by the trend component. This is therefore more closely investigated. The phase representation then shows characteristic trajectories of the trends. (orig./KW) [Deutsch] In der vorliegende Arbeit wurden speziell die Immissionsdaten der Messstation Leipzig-Mitte des Zeitraumes 1980-1993 mit dem Ziel der Erstellung eines Algorithmus fuer die Kuerzestfristprognose von Ueberschreitungssituationen untersucht. Die Prognosestellung sollte allein anhand der in der Vergangenheit registrierten Halbstundenwerte der SO{sub 2}-Konzentration, also univariat erfolgen. Wie die Fourieranalyse zeigt, gibt es drei wesentliche und voneinander unabhaengige Spektralbereiche: Den hochfrequenten Bereich (Periode <12 Stunden) der instabilen Irregularitaeten, den saisonalen Anteil mit den Perioden von 24 und 12 Stunden und den niedrigfrequenten Bereich (Periode >24 Stunden). Letzterer wird nach einer Zerlegung der Messreihe in Komponenten als Trendkomponente (oder kurz Trend) bezeichnet. Fuer die Komponentenzerlegung wird ein Kalman-Filter verwendet. Es stellt sich heraus, dass Smogepisoden am deutlichsten

  3. Short-term dynamics of intertidal microphytobenthic biomass. Mathematical modelling [La dynamique a court terme de la biomasse du microphytobenthos intertidal. Formalisation mathematique

    Science.gov (United States)

    Guarini, J.-M.; Gros, P.; Blanchard, G.F.; Bacher, C.

    1999-01-01

    We formulate a deterministic mathematical model to describe the dynamics of the microphytobenthos of intertidal mudflats. It is 'minimal' because it only takes into account the essential processes governing the functioning of the system: the autotrophic production, the active upward and downward migrations of epipelic microalgae, the saturation of the mud surface by a biofilm of diatoms and the global net loss rates of biomass. According to the photic environment of the benthic diatoms inhabiting intertidal mudflats, and to their migration rhythm, the model is composed of two sub-systems of ordinary differential equations; they describe the simultaneous evolution of the biomass 'S' concentrated in the mud surface biofilm - the photic layer - and of the biomass 'F' diluted in the topmost centimetre of the mud - the aphotic layer. Qualitatively, the model solutions agree fairly well with the in situ observed dynamics of the S + F biomass. The study of the mathematical properties of the model, under some simplifying assumptions, shows the convergence of solutions to a stable cyclic equilibrium, whatever the frequencies of the physical synchronizers of the production. The sensitivity analysis reveals the necessity of a better knowledge of the processes of biomass losses, which so far are uncertain, and may further vary in space and time.

  4. Short-term predictions of solar flares.

    Science.gov (United States)

    Burov, V. A.

    1990-02-01

    A review of present-day theoretical investigations of the problem of the accumulation and release of energy in solar flares permits advancing the opinion that only individual flare events are described by a concrete model and that a single model alone does not describe the entire diversity of flares. Consideration of the observational data does not permit claiming the existence of a single universal mechanism known today of flare events. It appears possible to treat the problem of prediction in terms of the algebra of logic (Boolean logic) and to compare the truth table with the often-used contingency table. The introduction of a number of very general assumptions permits forming a general approach to the development of predictive schemes and selection of the individual elements of the models and informative criteria. Experimental results are given on the testing of some prediction procedures. The author's procedure of routine short-term prediction of flares on the basis of the methods of instruction on pattern recognition implemented in the form of a set of programs is outlined. The results of the application of this procedure in 1986 - 1988 are presented.

  5. Short-term energy outlook, April 1999

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1999-04-01

    The forecast period for this issue of the Outlook extends from April 1999 through December 2000. Data values for the first quarter 1999, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the April 1999 version of the Short-Term Integrated forecasting system (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 25 figs., 19 tabs.

  6. MEDSLIK-II, a Lagrangian marine oil spill model for short-term forecasting – Part 2: Numerical simulations and validations

    Directory of Open Access Journals (Sweden)

    M. De Dominicis

    2013-03-01

    Full Text Available In this paper we use MEDSLIK-II, a Lagrangian marine oil spill model described in Part 1 of this paper (De Dominicis et al., 2013, to simulate oil slick transport and transformation processes for realistic oceanic cases where satellite or drifting buoys data are available for verification. The model is coupled with operational oceanographic currents, atmospheric analyses winds and remote-sensing data for initialization. The sensitivity of the oil spill simulations to several model parameterizations is analyzed and the results are validated using surface drifters and SAR (Synthetic Aperture Radar images in different regions of the Mediterranean Sea. It is found that the forecast skill of Lagrangian trajectories largely depends on the accuracy of the Eulerian ocean currents: the operational models give useful estimates of currents, but high-frequency (hourly and high spatial resolution is required, and the Stokes drift velocity has to be often added, especially in coastal areas. From a numerical point of view, it is found that a realistic oil concentration reconstruction is obtained using an oil tracer grid resolution of about 100 m, with at least 100 000 Lagrangian particles. Moreover, sensitivity experiments to uncertain model parameters show that the knowledge of oil type and slick thickness are, among all the others, key model parameters affecting the simulation results. Considering acceptable for the simulated trajectories a maximum spatial error of the order of three times the horizontal resolution of the Eulerian ocean currents, the predictability skill for particle trajectories is from 1 to 2.5 days depending on the specific current regime. This suggests that re-initialization of the simulations is required every day.

  7. Holding Multiple Items in Short Term Memory: A Neural Mechanism

    Science.gov (United States)

    Rolls, Edmund T.; Dempere-Marco, Laura; Deco, Gustavo

    2013-01-01

    Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging. PMID:23613789

  8. Why do short term workers have high mortality?

    DEFF Research Database (Denmark)

    Kolstad, Henrik; Olsen, Jørn

    1999-01-01

    Increased mortality is often reported among workers in short term employment. This may indicate either a health-related selection process or the presence of different lifestyle or social conditions among short term workers. The authors studied these two aspects of short term employment among 16......,404 Danish workers in the reinforced plastics industry who were hired between 1978 and 1985 and were followed to the end of 1988. Preemployment hospitalization histories for 1977-1984 were ascertained and were related to length of employment between 1978 and 1988. Workers who had been hospitalized prior...... to employment showed a 20% higher risk of early termination of employment than those never hospitalized (rate ratio (RR) = 1.20, 95% confidence interval (Cl) 1.16-1.29), and the risk increased with number of hospitalizations. For workers with two or more preemployment hospitalizations related to alcohol abuse...

  9. Cascades in model steels: The effect of cementite (Fe3C) and Cr23C6 particles on short-term crystal damage

    Science.gov (United States)

    Henriksson, K. O. E.

    2015-06-01

    Ferritic stainless steel can be modeled as an iron matrix containing precipitates of cementite (Fe3C) and Cr23C6. When used in nuclear power production the steels in the vicinity of the core start to accumulate damage due to neutrons. The role of the afore-mentioned carbides in this process is not well understood. In order to clarify the situation bulk cascades created by primary recoils in model steels have been carried out in the present work. Investigated configurations consisted of bulk ferrite containing spherical particles (diameter of 4 nm) of either (1) Fe3C or (2) Cr23C6. Primary recoils were initiated at different distances from the inclusions, with recoil energies varying between 100 eV and 1 keV. Results for the number of point defects such as vacancies and antisites are presented. These findings indicate that defects are also remaining when cascades are started outside the carbide inclusions. The work uses a recently developed Abell-Brenner-Tersoff potential for the Fe-Cr-C system.

  10. Short-term sea ice forecasts with the RASM-ESRL coupled model: A testbed for improving simulations of ocean-ice-atmosphere interactions in the marginal ice zone

    Science.gov (United States)

    Solomon, A.; Cox, C. J.; Hughes, M.; Intrieri, J. M.; Persson, O. P. G.

    2015-12-01

    The dramatic decrease of Arctic sea-ice has led to a new Arctic sea-ice paradigm and to increased commercial activity in the Arctic Ocean. NOAA's mission to provide accurate and timely sea-ice forecasts, as explicitly outlined in the National Ocean Policy and the U.S. National Strategy for the Arctic Region, needs significant improvement across a range of time scales to improve safety for human activity. Unfortunately, the sea-ice evolution in the new Arctic involves the interaction of numerous physical processes in the atmosphere, ice, and ocean, some of which are not yet understood. These include atmospheric forcing of sea-ice movement through stress and stress deformation; atmospheric forcing of sea-ice melt and formation through energy fluxes; and ocean forcing of the atmosphere through new regions of seasonal heat release. Many of these interactions involve emerging complex processes that first need to be understood and then incorporated into forecast models in order to realize the goal of useful sea-ice forecasting. The underlying hypothesis for this study is that errors in simulations of "fast" atmospheric processes significantly impact the forecast of seasonal sea-ice retreat in summer and its advance in autumn in the marginal ice zone (MIZ). We therefore focus on short-term (0-20 day) ice-floe movement, the freeze-up and melt-back processes in the MIZ, and the role of storms in modulating stress and heat fluxes. This study uses a coupled ocean-atmosphere-seaice forecast model as a testbed to investigate; whether ocean-sea ice-atmosphere coupling improves forecasts on subseasonal time scales, where systematic biases develop due to inadequate parameterizations (focusing on mixed-phase clouds and surface fluxes), how increased atmospheric resolution of synoptic features improves the forecasts, and how initialization of sea ice area and thickness and snow depth impacts the skill of the forecasts. Simulations are validated with measurements at pan-Arctic land

  11. Short-term robustness of production management systems

    NARCIS (Netherlands)

    Kleijnen, J.P.C.; Gaury, E.G.A.

    1998-01-01

    Short-term performance of a production management system for make-to-stock factories may be quantified through the service rate per shift; long-term performance through the average monthly work in process (WIP). This may yield, for example, that WIP is minimized, while the probability of the service

  12. Mercury simulations within GMOS: Analysis of short-term observational episodes

    Directory of Open Access Journals (Sweden)

    Travnikov O.

    2013-04-01

    Full Text Available A number of contemporary chemical transport models for mercury are applied within the framework of the EU GMOS project to study principal processes of mercury transport and transformations in the atmosphere. Each model is involved in simulation of short-term episodes corresponding to particular Hg measurement campaigns in Europe and other regions. In order to evaluate different physical and chemical mechanisms the models perform sensitivity runs with various parameterizations and/or combinations of considered processes. The modeling results are compared to detailed measurements of Hg species (Hg0/TGM, RGM, HgP with high temporal resolution (hours aiming at reproduction of short-term temporal variability of Hg air concentration.

  13. Short-term memory deficits correlate with hippocampal-thalamic functional connectivity alterations following acute sleep restriction.

    Science.gov (United States)

    Chengyang, Li; Daqing, Huang; Jianlin, Qi; Haisheng, Chang; Qingqing, Meng; Jin, Wang; Jiajia, Liu; Enmao, Ye; Yongcong, Shao; Xi, Zhang

    2016-07-21

    Acute sleep restriction heavily influences cognitive function, affecting executive processes such as attention, response inhibition, and memory. Previous neuroimaging studies have suggested a link between hippocampal activity and short-term memory function. However, the specific contribution of the hippocampus to the decline of short-term memory following sleep restriction has yet to be established. In the current study, we utilized resting-state functional magnetic resonance imaging (fMRI) to examine the association between hippocampal functional connectivity (FC) and the decline of short-term memory following total sleep deprivation (TSD). Twenty healthy adult males aged 20.9 ± 2.3 years (age range, 18-24 years) were enrolled in a within-subject crossover study. Short-term memory and FC were assessed using a Delay-matching short-term memory test and a resting-state fMRI scan before and after TSD. Seed-based correlation analysis was performed using fMRI data for the left and right hippocampus to identify differences in hippocampal FC following TSD. Subjects demonstrated reduced alertness and a decline in short-term memory performance following TSD. Moreover, fMRI analysis identified reduced hippocampal FC with the superior frontal gyrus (SFG), temporal regions, and supplementary motor area. In addition, an increase in FC between the hippocampus and bilateral thalamus was observed, the extent of which correlated with short-term memory performance following TSD. Our findings indicate that the disruption of hippocampal-cortical connectivity is linked to the decline in short-term memory observed after acute sleep restriction. Such results provide further evidence that support the cognitive impairment model of sleep deprivation.

  14. Short-Term Memory, Executive Control, and Children's Route Learning

    Science.gov (United States)

    Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

  15. Short-Term Memory, Executive Control, and Children's Route Learning

    Science.gov (United States)

    Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

  16. Short-term forecasting tools for agricultural nutrient management

    Science.gov (United States)

    The advent of real time/short term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high performance computing and hydrologic/climate modeling have enabled rapid dissemination of ...

  17. Transfer process and the short-term effect of radiotherapy in classic Kaposi's sarcoma%经典型Kaposi肉瘤的放疗转归过程及近期疗效

    Institute of Scientific and Technical Information of China (English)

    赵辉; 古丽娜尔; 陈惠; 朱成斌; 张风华; 蔺波

    2013-01-01

    目的 探讨经典型Kaposi肉瘤(KS)的放疗转归过程及近期疗效,以期提高对该病的认识及治疗水平.方法 回顾性分析经病理证实的51例经典型KS患者资料,总结其分布特点、放疗转归及近期疗效.结果 经典型KS的发病部位多位于肢体远端,上下肢均受累22例(43.1%),下肢受累16例(31.4%),上肢受累13例(25.5%),在放疗过程中KS的转归分为5个阶段:色素沉着期、局部肿胀期、溃烂渗出期、结痂期和愈合期.放疗的近期有效率为96.1%.结论 放疗是治疗KS的一种有效局部手段,但其转归过程不同于皮肤癌,有较独特的演变过程.%Objective To explore the transfer process and the short-term of radiotherapy in classic Kaposi's sarcoma.Methods In a retrospective analysis of 51 cases with classic Kaposi's sarcoma confirmed by pathology,the distribution characteristics of tumor,radiation treatment outcomes and the short-term curative effect were investigated.Results Classic Kaposi's sarcoma of Xinjiang province was located in the distal limb.There were 22 (43.1%) patients with both upper-and lower-extremity lesions,16 (31.4%)patients with lower-extremity lesions and 13 (25.5%) patients with both upper-extremity lesions.The development of outcome changes during the radiation treatment could be divided into 5 stages:the period of pigmentation,local swelling,decay,scabby and healing.The effective rate of radiation treatment was 96.1%.Conclusion Radiation therapy is an effective local treatment for classic Kaposi's sarcoma,but the development process is different from skin cancer and possesses relatively unique evolutionary process.

  18. Visual Short-Term Memory Complexity

    DEFF Research Database (Denmark)

    Sørensen, Thomas Alrik

    Several recent studies have explored the nature and limits of visual short-term memory (VSTM) (e.g. Luck & Vogel, 1997). A general VSTM capacity limit of about 3 to 4 letters has been found, thus confirming results from earlier studies (e.g. Cattell, 1885; Sperling, 1960). However, Alvarez...

  19. Abdominal Pain (Stomach Pain), Short-Term

    Science.gov (United States)

    ... in Children and TeensRead MoreBMI Calculator Abdominal Pain (Stomach Pain), Short-termJust about everyone has had a " ... time or another. But sudden severe abdominal pain (stomach pain), also called acute pain, shouldn't be ...

  20. LIFE with Short-Term Memory

    Science.gov (United States)

    Alonso-Sanz, Ramón

    This chapter considers an extension to the standard framework of cellular automata in which, cells are endowed with memory of their previous state values. The effect of short-term memory, i.e., memory of only the latest states, in the (formally unaltered) Life rule is described in this work.

  1. The model for end-stage liver disease score-based system predicts short term mortality better than the current Child-Turcotte-Pugh score-based allocation system during waiting for deceased liver transplantation.

    Science.gov (United States)

    Hong, Geun; Lee, Kwang-Woong; Suh, Sukwon; Yoo, Tae; Kim, Hyeyoung; Park, Min-Su; Choi, Youngrok; Yi, Nam-Joon; Suh, Kyung-Suk

    2013-08-01

    To adopt the model for end-stage liver disease (MELD) score-based system in Korea, the feasibility should be evaluated by analysis of Korean database. The aim of this study was to investigate the feasibility of the MELD score-based system compared with the current Child-Turcotte-Pugh (CTP) based-system and to suggest adequate cut-off to stratify waiting list mortality among Korean population. We included 788 adult patients listed in waiting list in Seoul National University Hospital from January 2008 to May 2011. The short-term survival until 6 months after registration was evaluated. Two hundred forty six (31.2%) patients underwent live donor liver transplantation and 353 (44.8%) patients were still waiting and 121 (15.4%) patients were dropped out due to death. Significant difference was observed when MELD score 24 and 31 were used as cut-off. Three-months survival of Status 2A was 70.2%. However, in Status 2A patients whose MELD score less than 24 (n=82), 86.6% of patients survived until 6 month. Furthermore, patients with high MELD score (≥31) among Status 2B group showed poorer survival rate (45.8%, 3-month) than Status 2A group. In conclusion, MELD score-based system can predict short term mortality better and select more number of high risk patients in Korean population.

  2. Short-Term, Low-Dose Use of Tolvaptan as a Bridge Therapy to Expedite Liver Transplant for Severe Hyponatremic, Cirrhotic Patients With High Model for End-Stage Liver Disease Scores.

    Science.gov (United States)

    Lenci, Ilaria; Milana, Martina; Angelico, Mario; Baiocchi, Leonardo

    2015-11-17

    For patients on liver transplant waiting lists, hyponatremia is associated with increased mortality before transplant and complications during the early posttransplant period. Conventional therapies, such as fluid restriction or hypertonic saline infusion, are of limited value. We describe 2 patients with high Model for End-Stage Liver Disease scores (> 30) who were referred to our unit for expedited liver transplant. While on waiting lists, these patients developed severe hyponatremia (therapies. Low-dose, short-term tolvaptan therapy (15 mg/d for 5 d) was then administered, as a bridge therapy to transplant, resulting in prompt restoration of serum sodium levels without any major clinical event. One patient died a few days later as no suitable grafts were available. The other received a liver transplant, and the outcome was uneventful. In conclusion, our report demonstrates that a short-term, low-dose tolvaptan-based strategy promptly resolves hyponatremia in patients who are on expedited waiting lists for liver transplant, allowing surgery with improved sodium levels and possibly limiting peritransplant complications.

  3. Pigeon visual short-term memory directly compared to primates.

    Science.gov (United States)

    Wright, Anthony A; Elmore, L Caitlin

    2016-02-01

    Three pigeons were trained to remember arrays of 2-6 colored squares and detect which of two squares had changed color to test their visual short-term memory. Procedures (e.g., stimuli, displays, viewing times, delays) were similar to those used to test monkeys and humans. Following extensive training, pigeons performed slightly better than similarly trained monkeys, but both animal species were considerably less accurate than humans with the same array sizes (2, 4 and 6 items). Pigeons and monkeys showed calculated memory capacities of one item or less, whereas humans showed a memory capacity of 2.5 items. Despite the differences in calculated memory capacities, the pigeons' memory results, like those from monkeys and humans, were all well characterized by an inverse power-law function fit to d' values for the five display sizes. This characterization provides a simple, straightforward summary of the fundamental processing of visual short-term memory (how visual short-term memory declines with memory load) that emphasizes species similarities based upon similar functional relationships. By closely matching pigeon testing parameters to those of monkeys and humans, these similar functional relationships suggest similar underlying processes of visual short-term memory in pigeons, monkeys and humans. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. A short-term output model of wind farm considering rain-snow-ice weather%计及冰雪天气影响的风电场短期出力模型

    Institute of Scientific and Technical Information of China (English)

    王浩; 王洪涛; 王春义

    2016-01-01

    Considering the impacts of rain-snow-ice weather on wind farm practical operation, this paper proposes a new short-term output model of wind farm. This model considers the ice accretion on wind turbines and transmission lines. The power output models of wind turbines with ice on blades are modified based on existing studies. The outage of facilities in wind farm is also considered in this model. The outage probability of wind turbines and transmission lines under bad conditions is estimated and a time varying model is established. Considering the wake effect, the states of wind turbines are sampled sequentially. Combined with the output level of wind turbines and the states of transmission lines, the power output of wind farm is calculated. Simulation result shows that the proposed model can reflect the short-term characteristics of wind farm output and can be used for short-term reliability assessment of power system.%针对冰雪天气对风电场实际运行的影响,提出了一种新的风电场短期出力模型。该模型考虑了风电机组和集电线路的积冰过程,基于现有的研究成果对叶片积冰情况下的风电机组有功出力模型进行了修正。同时考虑冰雪天气下风电场内设备的随机停运,对风电机组和集电线路在恶劣运行条件下的故障停运概率进行了估计,建立了与环境因素相依的时变停运概率模型。考虑尾流效应影响,对风电场内机组的状态进行分批次抽样,并结合机组出力水平和集电线路的抽样状态,计算风电场的出力。仿真结果表明所提的出力模型能够反映风电场在冰雪天气下短时间内的出力特性,适用于风电接入系统的短期可靠性评估。

  5. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

    Science.gov (United States)

    Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

    2016-06-01

    To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

  6. Reformulating and Testing the Perfectionism Model of Binge Eating among Undergraduate Women: A Short-Term, Three-Wave Longitudinal Study

    Science.gov (United States)

    Mackinnon, Sean P.; Sherry, Simon B.; Graham, Aislin R.; Stewart, Sherry H.; Sherry, Dayna L.; Allen, Stephanie L.; Fitzpatrick, Skye; McGrath, Daniel S.

    2011-01-01

    The perfectionism model of binge eating (PMOBE) is an integrative model explaining why perfectionism is related to binge eating. This study reformulates and tests the PMOBE, with a focus on addressing limitations observed in the perfectionism and binge-eating literature. In the reformulated PMOBE, concern over mistakes is seen as a destructive…

  7. Long-term manure carbon sequestration in soil simulated with the Daisy model on the basis of short-term incubation study

    DEFF Research Database (Denmark)

    Karki, Yubaraj Kumar; Børgesen, Christen Duus; Thomsen, Ingrid Kaag

    2013-01-01

    This study focused on simulating the long-term soil carbon sequestration after application of anaerobically digested and non-digested cattle manure using the Daisy model. The model was parameterized and calibrated for soil carbon (C) release during a 247 days incubation study including a coarse s...

  8. Long-term manure carbon sequestration in soil simulated with the Daisy model on the basis of short-term incubation study

    DEFF Research Database (Denmark)

    Karki, Yubaraj Kumar; Børgesen, Christen Duus; Thomsen, Ingrid Kaag

    2013-01-01

    This study focused on simulating the long-term soil carbon sequestration after application of anaerobically digested and non-digested cattle manure using the Daisy model. The model was parameterized and calibrated for soil carbon (C) release during a 247 days incubation study including a coarse s...

  9. Reformulating and Testing the Perfectionism Model of Binge Eating among Undergraduate Women: A Short-Term, Three-Wave Longitudinal Study

    Science.gov (United States)

    Mackinnon, Sean P.; Sherry, Simon B.; Graham, Aislin R.; Stewart, Sherry H.; Sherry, Dayna L.; Allen, Stephanie L.; Fitzpatrick, Skye; McGrath, Daniel S.

    2011-01-01

    The perfectionism model of binge eating (PMOBE) is an integrative model explaining why perfectionism is related to binge eating. This study reformulates and tests the PMOBE, with a focus on addressing limitations observed in the perfectionism and binge-eating literature. In the reformulated PMOBE, concern over mistakes is seen as a destructive…

  10. 基于ELMAN神经网络的短期风速预测%The Wind Speed Short-term Forecast Analysis Based on the Elman Neural Network Predict Model

    Institute of Scientific and Technical Information of China (English)

    孙斌; 姚海涛; 齐城龙

    2012-01-01

    In order to improve the accuracy of short-term wind speed forecast,this paper proposes a Elman neural network model.Reconstruction the phase space of the chaotic wind speed time series by calculating the embedding dimension and the delay time of the wind speed time series.Then the Elman neural network model can be used to forecast the wind speed.The results show the Elman neural network model can meet the accuracy requirements.At the same time,this thesis will use the BP neural network prediction model to forecast the wind speed time series.The simulation results show that the Elman neural network prediction model can be a good short-term wind speed prediction model.So it can be widely used in engineering practice.%为了提高风电场风速短期预测的精确性,本文提出了基于Elman神经网络的预测。首先求出风速时间序列的嵌入维数和延迟时间,进而对混沌风速时间序列进行相空间重构。然后利用Elman神经网络对相空间重构后的风速时间序列进行预测,预测结果表明基于Elman神经网络的预测效果满足了精度要求。本文同时运用BP神经网络进行预测。仿真结果表明,基于ELMAN神经网络的预测模型能够较为准确的进行短期风速的预测,具有很高的工程实际应用意义。

  11. Report on the use of stability parameters and mesoscale modelling in short-term prediction[Wind speed at wind farm sites

    Energy Technology Data Exchange (ETDEWEB)

    Badger, J.; Giebel, G.; Guo Larsen, X.; Skov Nielsen, T.; Aalborg Nielsen, H.; Madsen, Henrik; Toefting, J.

    2007-06-15

    In this report investigations using atmospheric stability measures to improve wind speed predictions at wind farm sites are described. Various stability measures have been calculated based on numerical weather prediction model output. Their ability to improve the wind speed predictions is assessed at three locations. One of the locations is in complex terrain. Mesoscale modelling has been carried out using KAMM at this location. The characteristics of the measured winds are captured well by the mesoscale modelling. It can be seen that the atmospheric stability plays an important role in determining how the flow is influence by the terrain. A prediction system employing a look-up table approach based on wind class simulations is presented. The mesoscale modelling results produced by KAMM were validated using an alternative mesoscale model called WRF. A good agreement in the results of KAMM and WRF was found. It is shown that including a stability parameter in physical and/or statistical modelling can improve the wind speed predictions at a wind farm site. A concept for the inclusion of a stability measure in the WPPT prediction system is presented, and the testing of the concept is outlined. (au)

  12. Mental rotation impairs attention shifting and short-term memory encoding: neurophysiological evidence against the response-selection bottleneck model of dual-task performance

    NARCIS (Netherlands)

    M.M. Pannebakker; W.O. van Dam; G.P.H. Band; K.R. Ridderinkhof; B. Hommel

    2011-01-01

    Dual tasks and their associated delays have often been used to examine the boundaries of processing in the brain. We used the dual-task procedure and recorded event-related potentials (ERPs) to investigate how mental rotation of a first stimulus (S1) influences the shifting of visual-spatial attenti

  13. Short-Term Energy Outlook: Quarterly projections. Fourth quarter 1993

    Energy Technology Data Exchange (ETDEWEB)

    1993-11-05

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the fourth quarter of 1993 through the fourth quarter of 1994. Values for the third quarter of 1993, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications.

  14. Retrieval-Induced Inhibition in Short-Term Memory.

    Science.gov (United States)

    Kang, Min-Suk; Choi, Joongrul

    2015-07-01

    We used a visual illusion called motion repulsion as a model system for investigating competition between two mental representations. Subjects were asked to remember two random-dot-motion displays presented in sequence and then to report the motion directions for each. Remembered motion directions were shifted away from the actual motion directions, an effect similar to the motion repulsion observed during perception. More important, the item retrieved second showed greater repulsion than the item retrieved first. This suggests that earlier retrieval exerted greater inhibition on the other item being held in short-term memory. This retrieval-induced motion repulsion could be explained neither by reduced cognitive resources for maintaining short-term memory nor by continued inhibition between short-term memory representations. These results indicate that retrieval of memory representations inhibits other representations in short-term memory. We discuss mechanisms of retrieval-induced inhibition and their implications for the structure of memory. © The Author(s) 2015.

  15. Using Short-term Hindcast Skill to Add Confidence to the Choice of Uncertain Model Parameter Values in CESM Climate Change Simulations

    Science.gov (United States)

    Hannay, C.; Neale, R. B.; Rothstein, M.

    2016-12-01

    Projections of future climate change are inherently uncertain and regional details are heavily dependent on coupled climate model formulations. Bernstein and Neelin (2016) show that projections of future climate using the Community Earth System Model (CESM) can vary significantly depending on the (reasonable) value used for important but uncertain model parameters. This includes a wide variation in the tropical precipitation response due to perturbations of parameters inherent to the formulation of deep convection parameterization. The question is therefore which model formulation should be trusted most? Since true validation is, of course, not possible at present day, guidance has to be provided by other proxies. Using a simple metric that climate models that performing best in a standard present-day (AMIP-type) configuration should be trusted most for future climate projections is unsatisfactory here, as only a small tuning effort is required to produce simulations equally skillful to the unperturbed model configurations. Here we employ an alternative approach for "trusting" the future climate projections. It is based on using CESM for a series of CAPT-type hindcast simulations, mirroring the limited perturbed parameter ensemble approach of Bernstein and Neelin (2016). Simulation sets are run for the YOTC period of 2009-2010 using CAM5 at 1 degree resolution. In this talk we will show the regional variations of climate change signals in the hydrological cycle in response to deep convection dependent parameter sets (e.g., entrainment, timescale) and contrast them with the equivalent hindcast experiments using the same parameter set. With this analysis we are able to provide guidance as to which parameter value selections result in the highest skill in the hindcasts and how that corresponds with the equivalent CESM future climate change signals.

  16. Short-Term Wind Speed Prediction Based on Combination Model%基于组合模型的短期风速预测研究

    Institute of Scientific and Technical Information of China (English)

    章伟; 邓院昌

    2013-01-01

    Wind speed is characteristic of large stochastic volatility,which affects the wind power and the stable operation of the power grid connected with it,thus prediction of wind speeds is crucial in the integration of wind power with the grid.This paper uses Grey-Markov chain model and least squares support vector machine model to predict wind speeds,and then compares the accuracies obtained with each prediction model.Based on this study,the dynamic weight combination model and the 0-1 combination model are studied.Furthermore,analysis is made with the actually measured wind speed in a certain wind farm in China as an example.The result shows that the combination prediction model is better than the single prediction method,and has large practical values.%风速具有较大的随机波动性,影响了电网的稳定性,风速预测对于风电并网问题至关重要.本研究采用灰色-马尔可夫链(GM-Markov)与最小二乘支持向量机(LSSVM)预测模型分别对风速进行预测,比较了各单一预测模型的精度;在此基础上研究了动态权重组合模型与0-1法组合预测模型.然后以国内某风电场的实测风速数据为例进行分析,结果表明,单一预测方法时好时坏,稳定性较差,组合预测模型总体效果较好,具有较大的实用价值.

  17. Preservation of Long-Term Memory and Synaptic Plasticity Despite Short-Term Impairments in the Tc1 Mouse Model of Down Syndrome

    Science.gov (United States)

    Morice, Elise; Andreae, Laura C.; Cooke, Sam F.; Vanes, Lesley; Fisher, Elizabeth M. C.; Tybulewicz, Victor L. J.; Bliss, Timothy V. P.

    2008-01-01

    Down syndrome (DS) is a genetic disorder arising from the presence of a third copy of the human chromosome 21 (Hsa21). Recently, O'Doherty and colleagues in an earlier study generated a new genetic mouse model of DS (Tc1) that carries an almost complete Hsa21. Since DS is the most common genetic cause of mental retardation, we have undertaken a…

  18. Preservation of Long-Term Memory and Synaptic Plasticity Despite Short-Term Impairments in the Tc1 Mouse Model of Down Syndrome

    Science.gov (United States)

    Morice, Elise; Andreae, Laura C.; Cooke, Sam F.; Vanes, Lesley; Fisher, Elizabeth M. C.; Tybulewicz, Victor L. J.; Bliss, Timothy V. P.

    2008-01-01

    Down syndrome (DS) is a genetic disorder arising from the presence of a third copy of the human chromosome 21 (Hsa21). Recently, O'Doherty and colleagues in an earlier study generated a new genetic mouse model of DS (Tc1) that carries an almost complete Hsa21. Since DS is the most common genetic cause of mental retardation, we have undertaken a…

  19. Short-term genome stability of serial Clostridium difficile ribotype 027 isolates in an experimental gut model and recurrent human disease.

    Directory of Open Access Journals (Sweden)

    David W Eyre

    Full Text Available BACKGROUND: Clostridium difficile whole genome sequencing has the potential to identify related isolates, even among otherwise indistinguishable strains, but interpretation depends on understanding genomic variation within isolates and individuals. METHODS: Serial isolates from two scenarios were whole genome sequenced. Firstly, 62 isolates from 29 timepoints from three in vitro gut models, inoculated with a NAP1/027 strain. Secondly, 122 isolates from 44 patients (2-8 samples/patient with mostly recurrent/on-going symptomatic NAP-1/027 C. difficile infection. Reference-based mapping was used to identify single nucleotide variants (SNVs. RESULTS: Across three gut model inductions, two with antibiotic treatment, total 137 days, only two new SNVs became established. Pre-existing minority SNVs became dominant in two models. Several SNVs were detected, only present in the minority of colonies at one/two timepoints. The median (inter-quartile range [range] time between patients' first and last samples was 60 (29.5-118.5 [0-561] days. Within-patient C. difficile evolution was 0.45 SNVs/called genome/year (95%CI 0.00-1.28 and within-host diversity was 0.28 SNVs/called genome (0.05-0.53. 26/28 gut model and patient SNVs were non-synonymous, affecting a range of gene targets. CONCLUSIONS: The consistency of whole genome sequencing data from gut model C. difficile isolates, and the high stability of genomic sequences in isolates from patients, supports the use of whole genome sequencing in detailed transmission investigations.

  20. Short-Term Genome Stability of Serial Clostridium difficile Ribotype 027 Isolates in an Experimental Gut Model and Recurrent Human Disease

    Science.gov (United States)

    Eyre, David W.; Walker, A. Sarah; Freeman, Jane; Baines, Simon D.; Fawley, Warren N.; Chilton, Caroline H.; Griffiths, David; Vaughan, Alison; Crook, Derrick W.; Peto, Tim E. A.; Wilcox, Mark H.

    2013-01-01

    Background Clostridium difficile whole genome sequencing has the potential to identify related isolates, even among otherwise indistinguishable strains, but interpretation depends on understanding genomic variation within isolates and individuals. Methods Serial isolates from two scenarios were whole genome sequenced. Firstly, 62 isolates from 29 timepoints from three in vitro gut models, inoculated with a NAP1/027 strain. Secondly, 122 isolates from 44 patients (2–8 samples/patient) with mostly recurrent/on-going symptomatic NAP-1/027 C. difficile infection. Reference-based mapping was used to identify single nucleotide variants (SNVs). Results Across three gut model inductions, two with antibiotic treatment, total 137 days, only two new SNVs became established. Pre-existing minority SNVs became dominant in two models. Several SNVs were detected, only present in the minority of colonies at one/two timepoints. The median (inter-quartile range) [range] time between patients’ first and last samples was 60 (29.5–118.5) [0–561] days. Within-patient C. difficile evolution was 0.45 SNVs/called genome/year (95%CI 0.00–1.28) and within-host diversity was 0.28 SNVs/called genome (0.05–0.53). 26/28 gut model and patient SNVs were non-synonymous, affecting a range of gene targets. Conclusions The consistency of whole genome sequencing data from gut model C. difficile isolates, and the high stability of genomic sequences in isolates from patients, supports the use of whole genome sequencing in detailed transmission investigations. PMID:23691061

  1. A New Strategy for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Yi Yang

    2013-01-01

    Full Text Available Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.

  2. Analysis of the short-term overproduction capability of variable speed wind turbines

    DEFF Research Database (Denmark)

    Hansen, Anca Daniela; Altin, Müfit; Margaris, Ioannis D.

    2014-01-01

    Emphasis in this article is on variable speed wind turbines (VSWTs) capability to provide short-term overproduction and better understanding of VSWTs’ mechanical and electrical limits to deliver such support. VSWTs’ short-term overproduction capability is of primary concern for the transmission...... system operators (TSOs) in the process of restoring critical situations during large frequency excursions in power systems with high wind power penetration. This study is conducted on a simplified generic model for VSWTs with full scale power converter (Type IV), which includes several adjustments...... and extensions of the Type IV standard wind turbine model proposed by the IEC Committee in IEC 61400-27-1. This modified standard model is able to account for dynamic features relevant for integrating active power ancillary services in wind power plants, such as frequency support capabilities. The performance...

  3. Comparison of short-term effects of midazolam and lorazepam in the intra-amygdala kainic acid model of status epilepticus in mice.

    Science.gov (United States)

    Diviney, Mairead; Reynolds, James P; Henshall, David C

    2015-10-01

    Benzodiazepines remain as the first-line treatment for status epilepticus (SE), but debate continues as to the choice and delivery route of pharmacotherapy. Lorazepam is currently the preferred anticonvulsant for clinical use, but midazolam has become a popular alternative, particularly as it can be given by nonintravenous routes. Anticonvulsants are also commonly used to terminate SE in animal models. Here, we aimed to compare the efficacy of midazolam with that of lorazepam in an experimental model of focal-onset SE. Status epilepticus was induced by intra-amygdala microinjection of kainic acid in 8week old C57Bl/6 mice. Forty minutes later, mice were treated with an intraperitoneal injection of either lorazepam or midazolam (8mg/kg). Electroencephalogram (EEG) activity, histology, and behavioral tests assessing recovery of function were evaluated and compared between groups. Intraperitoneal injection of either lorazepam or midazolam resulted in similar patterns of reduced EEG epileptiform activity during 1-hour recordings. Damage to the hippocampus and presentation of postinsult anxiety-related behavior did not significantly differ between treatment groups at 72h. However, return of normal behaviors such as grooming, levels of activity, and the evaluation of overall recovery of SE mice were all superior at 24h in animals given midazolam compared with lorazepam. Our results indicate that midazolam is as effective as lorazepam as an anticonvulsant in this model while also providing improved animal recovery after SE. These data suggest that midazolam might be considered by researchers as an anticonvulsant in animal models of SE, particularly as it appears to satisfy the requirements of refining procedures involving experimental animals at early time-points after SE.

  4. 新马尔科夫模型的黄金价格短期预测%Short-term prediction of new markov forecasting model on gold price

    Institute of Scientific and Technical Information of China (English)

    张延利

    2012-01-01

    An ARMA-markov forecasting model is established for gold price which associates data statistical features closely with grey theory. The ARMA is used to reveal the linear variation trend of prediction sequence while , markov state transition probability matrix is used to find out the transition regularities. Practices indicate that the model manifests better prediction accuracy than ARMA and grey markov model.%对黄金价格建立了ARMA-马尔科夫预测模型,该模型将数据统计特征与灰色理论密切结合.ARMA部分用来揭示预测序列的线性变化趋势,而马尔科夫状态转移概率矩阵用来确定状态转移的规律.实证研究表明,该模型预测精度优于ARMA模型以及灰色马尔科夫模型的预测精度.

  5. Achievement of a short term three dimensional electron density mapping of the ionosphere in the European sector: Comparisons with the IRI model for quiet-moderate geomagnetic-ionospheric conditions

    Science.gov (United States)

    Pietrella, M.

    2016-10-01

    In this paper will be described the procedure followed for the achievement of a short term three dimensional (3-D) electron density mapping of the ionosphere in the European area. It consists of three main steps: (1) foF2 and M(3000)F2 short-term forecasts, (foF2STF) and (M3000F2STF), are calculated at 12 ionospheric observatories scattered in the European area; (2) the values of foF2STF and M3000F2STF on a grid of equi-spaced points, (foF2STF,GP) and (M3000F2STF,GP), are calculated by means of an appropriate interpolation algorithm by using the foF2STF and M3000F2STF data; (3) foF2STF,GP and M3000F2STF,GP data ingestion into the IRI model is employed to produce a short term 3-D electron density mapping (ST-3D-M) of the ionosphere. The electron density profiles provided by the ST-3D-M and IRI models, were compared with the electron density profiles autoscaled by the Automatic Real-Time Ionogram Scaler with True-height (ARTIST) system, which are here considered as the truth profiles. The results of these comparisons, shown for a certain number of epochs during quiet-moderate geomagnetic-ionospheric conditions, in the truth-sites of Athens (38○.0‧N, 23○.5‧E), Chilton (51○.5‧N, -0○.6‧W), Dourbes (50○.1‧ N, 4○.6‧E), Pruhonice (50○.0‧N, 14○.6‧E), Rome (41○.9‧N, 12○.5‧E), and Tortosa (40○.8‧N, 0○.5‧E), indicate that the ST-3D-M as forecasting tool can be considered generally reliable.

  6. 大气水汽同位素组成的短期变异特征%Short-term variations of vapor isotope ratios reveal the influence of atmospheric processes

    Institute of Scientific and Technical Information of China (English)

    张世春; 孙晓敏; 王建林; 于贵瑞; 温学发

    2011-01-01

    Stable isotopes of atmospheric water vapor reveal rich information on water movement and phase changes in the atmosphere. Here we presented two nearly continuous time-series of δD and δ18O of atmospheric water vapor (δv) measured at hourly intervals in surface air in Beijing and above a winter wheat canopy in Shijiazhuang using in-situ measurement technique. During the precipitation events, the δv values in both Beijing and Shijiazhuang were in the state of equilibrium with precipitation water, revealing the influence of precipitation processes. However, the δv departures from the equilibrium state were positively correlated with local relative humidity. Note that the δv tended to enrich in Beijing, but deplete in Shijiazhuang during the precipitation events, which mainly resulted from the influence of transpiration processes that enriched the δv in Shijiazhuang. On seasonal time-scale, the δvvalues were log-linear functions of water vapor mixing ratios in both Beijing and Shijiazhuang. The water vapor mixing ratio was an excellent predictor of the δv by the Rayleigh distillation mechanisms, indicating that air mass advection could also play an important role in determining the δv. On a diurnal time-scale, the δv reached the minimum in the early afternoon hours in Beijing which was closely related to the atmospheric processes of boundary layer entrainment. During the peak of growing season of winter wheat, however, the δv reached the minimum in the early morning, and increased gradually through the daytime, and reached the maximum in the late afternoon, which was responsible by the interaction between boundary layer entrainment and the local atmospheric processes, such as transpiration and dew formation. This study has the implications for the important role of vegetation in determining the surface δv and highlights the need to conduct δv measurement on short-term (e.g. diurnal) time scales.

  7. Short-term forecasting of the chloride content in the mineral waters of the Ustroń Health Resort using SARIMA and Holt-Winters models

    Directory of Open Access Journals (Sweden)

    Dąbrowska Dominika

    2015-12-01

    Full Text Available The Ustroń S.A. Health Resort (southern Poland uses iodide-bromide mineral waters taken from Middle and Upper Devonian limestones and dolomites with a mineralisation range of 110-130 g/dm3 for curative purposes. Two boreholes - U-3 and U3-A drilled in the early 1970s were exploited. The aim of this paper is to estimate changes in mineral water quality of the Ustroń Health Resort by taking into consideration chloride content in the water from the U-3 borehole. The data has included the results of monthly analyses of chlorides from 2005 to 2015 during the tests carried out by the Mining Department of the Health Resort. The triple exponential smoothing (ETS function and the Seasonal Autoregressive Integrated Moving Average (SARIMA method of modelling time series were used for the calculations. The ability to properly forecast mineral water quality can result in a good status of the exploitation borehole and a limited number of failures in the exploitation system. Because of the good management of health resorts, it is possible to acquire more satisfied customers. The main goal of the article involves the real-time forecast accuracy, obtained results show that the proposed methods are effective for such situations. Presented methods made it possible to obtain a 24-month point and interval forecast. The results of these analyses indicate that the chloride content is forecast to be in the range of 72 to 83 g/l from 2015 to 2017. While comparing the two methods of analysis, a narrower range of forecast values and, therefore, greater accuracy were obtained for the ETS function. The good performance of the ETS model highlights its utility compared with complicated physically based numerical models.

  8. The assessment of general well-being using spontaneous burrowing behaviour in a short-term model of chemotherapy-induced mucositis in the rat.

    Science.gov (United States)

    Whittaker, A L; Lymn, K A; Nicholson, A; Howarth, G S

    2015-01-01

    Mucositis is a common and serious side-effect experienced by cancer patients during treatment with chemotherapeutic agents. Consequently, programmes of research focus on the elucidation of novel therapeutics for alleviation of mucositis symptoms, and these frequently use animal models. However, although these models are assumed to be painful and distressing to the animal, endpoints are difficult to determine. The aim of this study was to evaluate whether a change in burrowing behaviour could provide an indication of disease onset and potentially be applied as a humane endpoint. Baseline burrowing behaviour was measured in healthy animals on three occasions by determining the weight of gravel displaced from a hollow tube. Mucositis was then induced in the same animals by intraperitoneal injection of 5-fluorouracil (150 mg/kg) and burrowing behaviour recorded over three consecutive days. Standard measures of disease progression, including body weight loss and clinical score, were also made. The presence of mucositis was confirmed at necropsy by findings of decreased duodenal and colon lengths, and reduced liver, spleen and thymus weights in comparison with non-treated control animals. Histological score of the jejunum and ileum was also significantly increased. Mucositis onset coincided with a decrease in mean burrowing behaviour which was progressive, however this result did not achieve statistical significance (P = 0.66).We conclude that burrowing may be a useful indicator of inflammation in the mucositis model, although this requires further characterization. Pre-selection of animals into treatment groups based on their prior burrowing performance should be pursued in further studies.

  9. A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality.

    Science.gov (United States)

    Loghmanpour, Natasha A; Kanwar, Manreet K; Druzdzel, Marek J; Benza, Raymond L; Murali, Srinivas; Antaki, James F

    2015-01-01

    Existing risk assessment tools for patient selection for left ventricular assist devices (LVADs) such as the Destination Therapy Risk Score and HeartMate II Risk Score (HMRS) have limited predictive ability. This study aims to overcome the limitations of traditional statistical methods by performing the first application of Bayesian analysis to the comprehensive Interagency Registry for Mechanically Assisted Circulatory Support dataset and comparing it to HMRS. We retrospectively analyzed 8,050 continuous flow LVAD patients and 226 preimplant variables. We then derived Bayesian models for mortality at each of five time end-points postimplant (30 days, 90 days, 6 month, 1 year, and 2 years), achieving accuracies of 95%, 90%, 90%, 83%, and 78%, Kappa values of 0.43, 0.37, 0.37, 0.45, and 0.43, and area under the receiver operator characteristic (ROC) of 91%, 82%, 82%, 80%, and 81%, respectively. This was in comparison to the HMRS with an ROC of 57% and 60% at 90 days and 1 year, respectively. Preimplant interventions, such as dialysis, ECMO, and ventilators were major contributing risk markers. Bayesian models have the ability to reliably represent the complex causal relations of multiple variables on clinical outcomes. Their potential to develop a reliable risk stratification tool for use in clinical decision making on LVAD patients encourages further investigation.

  10. Enhancement of Short-Term Memory by Methyl-6-(Phenylethynyl-Pyridine in the BTBR T+tf/J Mouse Model of Autism Spectrum Disorder

    Directory of Open Access Journals (Sweden)

    Haijie Yang

    2015-03-01

    Full Text Available BackgroundAutism spectrum disorder (ASD encompasses a range of disorders that are characterized by social and communication deficits and repetitive behaviors. This study evaluated the effect of methyl-6-(phenylethynyl-pyridine (MPEP, an antagonist of the mGluR5 metabotropic glutamate receptor, on memory enhancement in the BTBR T+tf/J (BTBR mouse strain, which has been recognized as a model of ASD.MethodsThe pharmacological effects of MPEP on memory and motor coordination were assessed using the Morris water maze and rotarod tests in BTBR and C57BL/6J (B6 mice. Furthermore, we performed morphological analyses of cerebellar foliation in BTBR and B6 mice using hematoxylin and eosin staining.ResultsMPEP-treated BTBR mice exhibited improved learning and memory in the Morris water maze test. MPEP administration also improved motor coordination in the rotarod test. However, no significant difference was observed regarding the numbers of Purkinje cells in the cerebella of BTBR versus normal B6 mice.ConclusionThis study suggests that the mGluR5 antagonist MPEP has the potential to ameliorate learning and memory dysfunction and impaired motor coordination in BTBR mice. These results further suggest that the BTBR mouse model may be useful in pharmacological studies investigating drugs that could potentially alleviate cognitive dysfunction in ASD.

  11. Mechanisms controlling primary and new production in a global ecosystem model – Part II: the role of the upper ocean short-term periodic and episodic mixing events

    Directory of Open Access Journals (Sweden)

    E. E. Popova

    2006-07-01

    Full Text Available The use of 6 h, daily, weekly and monthly atmospheric forcing resulted in dramatically different predictions of plankton productivity in a global 3-D coupled physical-biogeochemical model.

    Resolving the diurnal cycle of atmospheric variability by use of 6 h forcing, and hence also diurnal variability in UML depth, produced the largest difference, reducing predicted global primary and new production by 25% and 10% respectively relative to that predicted with daily and weekly forcing. This decrease varied regionally, being a 30% reduction in equatorial areas and 25% at moderate and high latitudes. A 10% increase in the primary production was seen in the peripheries of the oligotrophic gyres.

    By resolving the diurnal cycle, model performance was significantly improved with respect to several common problems: underestimated primary production in the oligotrophic gyres; overestimated primary production in the Southern Ocean; overestimated magnitude of the spring bloom in the subarctic Pacific Ocean, and overestimated primary production in equatorial areas. The result of using 6 h forcing on predicted ecosystem dynamics was profound, the effects persisting far beyond the hourly timescale, and having major consequences for predicted global and new production on an annual basis.

  12. Short-Term Administration of Serelaxin Produces Predominantly Vascular Benefits in the Angiotensin II/L-NAME Chronic Heart Failure Model

    Directory of Open Access Journals (Sweden)

    Joseph C. McCarthy, MS

    2017-06-01

    Full Text Available In patients hospitalized with acute heart failure, temporary serelaxin infusion reduced 6-month mortality through unknown mechanisms. This study therefore explored the cardiovascular effects of temporary serelaxin administration in mice subjected to the angiotensin II (AngII/L-NG-nitroarginine methyl ester (L-NAME heart failure model, both during serelaxin infusion and 19 days post–serelaxin infusion. Serelaxin administration did not alter AngII/L-NAME-induced cardiac hypertrophy, geometry, or dysfunction. However, serelaxin-treated mice had reduced perivascular left ventricular fibrosis and preserved left ventricular capillary density at both time points. Furthermore, resistance vessels from serelaxin-treated mice displayed decreased potassium chloride–induced constriction and reduced aortic fibrosis. These findings suggest that serelaxin improves outcomes in patients through vascular-protective effects.

  13. Short term synaptic depression improves information transfer in perceptual multistability

    Directory of Open Access Journals (Sweden)

    Zachary P Kilpatrick

    2013-07-01

    Full Text Available Competitive neural networks are often used to model the dynamics of perceptual bistability. Switching between percepts can occur through fluctuations and/or a slow adaptive process. Here, we analyze switching statistics in competitive networks with short term synaptic depression and noise. We start by analyzing a ring model that yields spatially structured solutions and complement this with a study of a space-free network whose populations are coupled with mutual inhibition. Dominance times arising from depression driven switching can be approximated using a separation of timescales in the ring and space-free model. For purely noise-driven switching, we derive approximate energy functions to justify how dominance times are exponentially related to input strength. We also show that a combination of depression and noise generates realistic distributions of dominance times. Unimodal functions of dominance times are more easily told apart by sampling, so switches induced by synaptic depression induced provide more information about stimuli than noise-driven switching. Finally, we analyze a competitive network model of perceptual tristability, showing depression generates a history-dependence in dominance switching.

  14. A Short-term Wind Speed Prediction Model Using Phase-space Reconstructed Extreme Learning Machine%相空间重构的极端学习机短期风速预测模型

    Institute of Scientific and Technical Information of China (English)

    武峰雨; 乐秀瑶; 南东亮

    2013-01-01

    A quick and accurate prediction of wind speed can effectively reduce or avoid the adverse effects of wind farms on power system, and can as well improve the competitiveness of the wind farm in the electricity market. In this paper, according to the chaotic characteristics of the wind speed, a short-term wind speed prediction model using phase-space reconstructed extreme learning machine (ELM) is put forward. The decision of the delay time and embedding dimension is used to reconstruct the sample space, which makes the new sample better reflect the change characteristics of wind speed. On this basis, the ELM is applied for short-term wind speed prediction. Compared with the traditional prediction model, this method has the advantages of fast learning speed and good generalization performance. Therefore, a new method is provided for wind speed prediction.%对风速进行快速、准确的预测,可以有效地减小或避免风电场对电力系统的不利影响,同时提高风电场在电力市场中的竞争能力.根据风速具有混沌特性,提出一种相空间重构的极端学习机(extreme learningmachine,ELM)的短期风速预测模型,通过确定延迟时间和嵌入维数,对样本空间进行重构,使新的样本更能反映风速变化特性,在此基础上运用ELM进行短期风速预测.与传统的预测模型相比,该方法具有学习速度快、泛化性能好等优点,为风速预测提供了新方法.

  15. Statistical mechanics of neocortical interactions EEG eigenfunctions of short-term memory

    CERN Document Server

    Ingber, L

    2000-01-01

    This paper focuses on how bottom-up neocortical models can be developed into eigenfunction expansions of probability distributions appropriate to describe short-term memory in the context of scalp EEG. The mathematics of eigenfunctions are similar to the top-down eigenfunctions developed by Nunez, albeit they have different physical manifestations. The bottom-up eigenfunctions are at the local mesocolumnar scale, whereas the top-down eigenfunctions are at the global regional scale. However, as described in several joint papers, our approaches have regions of substantial overlap, and future studies may expand top-down eigenfunctions into the bottom-up eigenfunctions, yielding a model of scalp EEG that is ultimately expressed in terms of columnar states of neocortical processing of attention and short-term memory.

  16. Short-Term Wind Speed Forecasting for Power System Operations

    KAUST Repository

    Zhu, Xinxin

    2012-04-01

    The emphasis on renewable energy and concerns about the environment have led to large-scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High-quality short-term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented. © 2012 The Authors. International Statistical Review © 2012 International Statistical Institute.

  17. Short-term memory in networks of dissociated cortical neurons.

    Science.gov (United States)

    Dranias, Mark R; Ju, Han; Rajaram, Ezhilarasan; VanDongen, Antonius M J

    2013-01-30

    Short-term memory refers to the ability to store small amounts of stimulus-specific information for a short period of time. It is supported by both fading and hidden memory processes. Fading memory relies on recurrent activity patterns in a neuronal network, whereas hidden memory is encoded using synaptic mechanisms, such as facilitation, which persist even when neurons fall silent. We have used a novel computational and optogenetic approach to investigate whether these same memory processes hypothesized to support pattern recognition and short-term memory in vivo, exist in vitro. Electrophysiological activity was recorded from primary cultures of dissociated rat cortical neurons plated on multielectrode arrays. Cultures were transfected with ChannelRhodopsin-2 and optically stimulated using random dot stimuli. The pattern of neuronal activity resulting from this stimulation was analyzed using classification algorithms that enabled the identification of stimulus-specific memories. Fading memories for different stimuli, encoded in ongoing neural activity, persisted and could be distinguished from each other for as long as 1 s after stimulation was terminated. Hidden memories were detected by altered responses of neurons to additional stimulation, and this effect persisted longer than 1 s. Interestingly, network bursts seem to eliminate hidden memories. These results are similar to those that have been reported from similar experiments in vivo and demonstrate that mechanisms of information processing and short-term memory can be studied using cultured neuronal networks, thereby setting the stage for therapeutic applications using this platform.

  18. Short term teleconnections associated with an individual tropical cyclone

    OpenAIRE

    Woll, Stephen C.

    1993-01-01

    Approved for public release; distribution is unlimited. The short term teleconnections associated with an individual western Pacific tropical cyclone have been investigated using an atmospheric general circulation model. The general strategy was to use the GCM, in combination with several tropical cyclone bogusing procedures, to isolate the effects on the global circulation of the tropical cyclone. The bogusing procedures were used to alter the tropical cyclone in the initial conditions fo...

  19. Short Term Airing by Natural Ventilation

    DEFF Research Database (Denmark)

    Heiselberg, Per; Perino, M.

    2010-01-01

    principles is necessary. The present study analyses and presents the results of an experimental evaluation of airing performance in terms of ventilation characteristics, IAQ and thermal comfort. It includes investigations of the consequences of opening time, opening frequency, opening area and expected...... airflow rate, ventilation efficiency, thermal comfort and dynamic temperature conditions. A suitable laboratory test rig was developed to perform extensive experimental analyses of the phenomenon under controlled and repeatable conditions. The results showed that short-term window airing is very effective...... and can provide both acceptable IAQ and thermal comfort conditions in buildings....

  20. 基于云支持向量机模型的短期风电功率预测%Short-term wind power forecasting based on cloud SVM model

    Institute of Scientific and Technical Information of China (English)

    凌武能; 杭乃善; 李如琦

    2013-01-01

    A CSVM(Cloud Support Vector Machine) model combining the cloud model and the SVM(Support Vector Machine) is proposed for the short-term wind power forecasting,which applies the cloud transformation to extract the qualitative attribute of wind speed data and uses SVM to build the relationship between wind speed and wind power.The forecasts for the next 24 hours' wind power show that,the forecasts at a particular point of the presented model is a set of discrete values with stabilized bias.The backward cloud algorithm is applied to calculate the expectation of the forecast set as the deterministic prediction,which is more accurate than that forecasted by SVM model or ARIMA(Auto-Regressive Integrated Moving Average) model.The presented model is effective for short-term wind power forecasting.%将云模型和支持向量机(SVM)相结合,提出一种适合短期风电功率预测的云支持向量机模型.该模型采用云变换方法提取风速序列的定性特征,并通过SVM建立风速特征与风电功率间的关系.对未来24h的风电功率预测结果显示,该模型在某个点上的预测值是一个有稳定倾向的离散值集合.采用逆向云算法求取集合的期望值作为确定性预测结果,并与SVM和自回归求和移动平均(ARIMA)模型的预测结果相比较,结果表明云支持向量机具有更高的预测精度,预测效果显著,因此,该模型可有效应用于短期风电功率预测.

  1. Neuroevolution Results in Emergence of Short-Term Memory for Goal-Directed Behavior

    CERN Document Server

    Lakhman, Konstantin

    2012-01-01

    Animals behave adaptively in the environment with multiply competing goals. Understanding of the mechanisms underlying such goal-directed behavior remains a challenge for neuroscience as well for adaptive system research. To address this problem we developed an evolutionary model of adaptive behavior in the multigoal stochastic environment. Proposed neuroevolutionary algorithm is based on neuron's duplication as a basic mechanism of agent's recurrent neural network development. Results of simulation demonstrate that in the course of evolution agents acquire the ability to store the short-term memory and, therefore, use it in behavioral strategies with alternative actions. We found that evolution discovered two mechanisms for short-term memory. The first mechanism is integration of sensory signals and ongoing internal neural activity, resulting in emergence of cell groups specialized on alternative actions. And the second mechanism is slow neurodynamical processes that makes possible to code the previous behav...

  2. How Emotional Pictures Influence Visuospatial Binding in Short-Term Memory in Ageing and Alzheimer's Disease?

    Science.gov (United States)

    Borg, Celine; Leroy, Nicolas; Favre, Emilie; Laurent, Bernard; Thomas-Anterion, Catherine

    2011-01-01

    The present study examines the prediction that emotion can facilitate short-term memory. Nevertheless, emotion also recruits attention to process information, thereby disrupting short-term memory when tasks involve high attentional resources. In this way, we aimed to determine whether there is a differential influence of emotional information on…

  3. The Role of Short Term Synaptic Plasticity in Temporal Coding of Neuronal Networks

    Science.gov (United States)

    Chandrasekaran, Lakshmi

    2008-01-01

    Short term synaptic plasticity is a phenomenon which is commonly found in the central nervous system. It could contribute to functions of signal processing namely, temporal integration and coincidence detection by modulating the input synaptic strength. This dissertation has two parts. First, we study the effects of short term synaptic plasticity…

  4. How Emotional Pictures Influence Visuospatial Binding in Short-Term Memory in Ageing and Alzheimer's Disease?

    Science.gov (United States)

    Borg, Celine; Leroy, Nicolas; Favre, Emilie; Laurent, Bernard; Thomas-Anterion, Catherine

    2011-01-01

    The present study examines the prediction that emotion can facilitate short-term memory. Nevertheless, emotion also recruits attention to process information, thereby disrupting short-term memory when tasks involve high attentional resources. In this way, we aimed to determine whether there is a differential influence of emotional information on…

  5. The Role of Short Term Synaptic Plasticity in Temporal Coding of Neuronal Networks

    Science.gov (United States)

    Chandrasekaran, Lakshmi

    2008-01-01

    Short term synaptic plasticity is a phenomenon which is commonly found in the central nervous system. It could contribute to functions of signal processing namely, temporal integration and coincidence detection by modulating the input synaptic strength. This dissertation has two parts. First, we study the effects of short term synaptic plasticity…

  6. Short-term energy outlook: Quarterly projections, fourth quarter 1997

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-10-14

    The Energy Information Administration (EIA) prepares quarterly short-term energy supply, demand, and price projections for printed publication in January, April, July, and October in the Short-Term Energy Outlook. The details of these projections, as well as monthly updates on or about the 6th of each interim month, are available on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. The forecast period for this issue of the Outlook extends from the fourth quarter of 1997 through the fourth quarter of 1998. Values for the fourth quarter of 1997, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the fourth quarter 1997 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. 19 tabs.

  7. Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

    Directory of Open Access Journals (Sweden)

    Chuan Ding

    2016-10-01

    Full Text Available Understanding the relationship between short-term subway ridership and its influential factors is crucial to improving the accuracy of short-term subway ridership prediction. Although there has been a growing body of studies on short-term ridership prediction approaches, limited effort is made to investigate the short-term subway ridership prediction considering bus transfer activities and temporal features. To fill this gap, a relatively recent data mining approach called gradient boosting decision trees (GBDT is applied to short-term subway ridership prediction and used to capture the associations with the independent variables. Taking three subway stations in Beijing as the cases, the short-term subway ridership and alighting passengers from its adjacent bus stops are obtained based on transit smart card data. To optimize the model performance with different combinations of regularization parameters, a series of GBDT models are built with various learning rates and tree complexities by fitting a maximum of trees. The optimal model performance confirms that the gradient boosting approach can incorporate different types of predictors, fit complex nonlinear relationships, and automatically handle the multicollinearity effect with high accuracy. In contrast to other machine learning methods—or “black-box” procedures—the GBDT model can identify and rank the relative influences of bus transfer activities and temporal features on short-term subway ridership. These findings suggest that the GBDT model has considerable advantages in improving short-term subway ridership prediction in a multimodal public transportation system.

  8. Audiovisual integration facilitates monkeys' short-term memory.

    Science.gov (United States)

    Bigelow, James; Poremba, Amy

    2016-07-01

    Many human behaviors are known to benefit from audiovisual integration, including language and communication, recognizing individuals, social decision making, and memory. Exceptionally little is known about the contributions of audiovisual integration to behavior in other primates. The current experiment investigated whether short-term memory in nonhuman primates is facilitated by the audiovisual presentation format. Three macaque monkeys that had previously learned an auditory delayed matching-to-sample (DMS) task were trained to perform a similar visual task, after which they were tested with a concurrent audiovisual DMS task with equal proportions of auditory, visual, and audiovisual trials. Parallel to outcomes in human studies, accuracy was higher and response times were faster on audiovisual trials than either unisensory trial type. Unexpectedly, two subjects exhibited superior unimodal performance on auditory trials, a finding that contrasts with previous studies, but likely reflects their training history. Our results provide the first demonstration of a bimodal memory advantage in nonhuman primates, lending further validation to their use as a model for understanding audiovisual integration and memory processing in humans.

  9. Short-Term Faculty-Led Study Abroad Programs Enhance Cultural Exchange and Self-Awareness

    Science.gov (United States)

    Gaia, A. Celeste

    2015-01-01

    Though many experts argue that semester or year abroad study is the optimal path, short-term programs meet the needs of students who would not otherwise study abroad and can be effective at increasing intercultural competency. The present study describes one type of short-term program--the embedded faculty-led model--and provides evidence that…

  10. Does tonality boost short-term memory in congenital amusia?

    Science.gov (United States)

    Albouy, Philippe; Schulze, Katrin; Caclin, Anne; Tillmann, Barbara

    2013-11-06

    Congenital amusia is a neuro-developmental disorder of music perception and production. Recent findings have demonstrated that this deficit is linked to an impaired short-term memory for tone sequences. As it has been shown before that non-musicians' implicit knowledge of musical regularities can improve short-term memory for tone information, the present study investigated if this type of implicit knowledge could also influence amusics' short-term memory performance. Congenital amusics and their matched controls, who were non-musicians, had to indicate whether sequences of five tones, presented in pairs, were the same or different; half of the pairs respected musical regularities (tonal sequences) and the other half did not (atonal sequences). As previously reported for non-musician participants, the control participants showed better performance (as measured with d') for tonal sequences than for atonal ones. While this improvement was not observed in amusics, both control and amusic participants showed faster response times for tonal sequences than for atonal sequences. These findings suggest that some implicit processing of tonal structures is potentially preserved in congenital amusia. This observation is encouraging as it strengthens the perspective to exploit implicit knowledge to help reducing pitch perception and memory deficits in amusia. © 2013 Elsevier B.V. All rights reserved.

  11. Frequency-specific insight into short-term memory capacity.

    Science.gov (United States)

    Feurra, Matteo; Galli, Giulia; Pavone, Enea Francesco; Rossi, Alessandro; Rossi, Simone

    2016-07-01

    The digit span is one of the most widely used memory tests in clinical and experimental neuropsychology for reliably measuring short-term memory capacity. In the forward version, sequences of digits of increasing length have to be reproduced in the order in which they are presented, whereas in the backward version items must be reproduced in the reversed order. Here, we assessed whether transcranial alternating current stimulation (tACS) increases the memory span for digits of young and midlife adults. Imperceptibly weak electrical currents in the alpha (10 Hz), beta (20 Hz), theta (5 Hz), and gamma (40 Hz) range, as well as a sham stimulation, were delivered over the left posterior parietal cortex, a cortical region thought to sustain maintenance processes in short-term memory through oscillatory brain activity in the beta range. We showed a frequency-specific effect of beta-tACS that robustly increased the forward memory span of young, but not middle-aged, healthy individuals. The effect correlated with age: the younger the subjects, the greater the benefit arising from parietal beta stimulation. Our results provide evidence of a short-term memory capacity improvement in young adults by online frequency-specific tACS application. Copyright © 2016 the American Physiological Society.

  12. Cardioprotective Signature of Short-Term Caloric Restriction.

    Directory of Open Access Journals (Sweden)

    Hossein Noyan

    Full Text Available To understand the molecular pathways underlying the cardiac preconditioning effect of short-term caloric restriction (CR.Lifelong CR has been suggested to reduce the incidence of cardiovascular disease through a variety of mechanisms. However, prolonged adherence to a CR life-style is difficult. Here we reveal the pathways that are modulated by short-term CR, which are associated with protection of the mouse heart from ischemia.Male 10-12 wk old C57bl/6 mice were randomly assigned to an ad libitum (AL diet with free access to regular chow, or CR, receiving 30% less food for 7 days (d, prior to myocardial infarction (MI via permanent coronary ligation. At d8, the left ventricles (LV of AL and CR mice were collected for Western blot, mRNA and microRNA (miR analyses to identify cardioprotective gene expression signatures. In separate groups, infarct size, cardiac hemodynamics and protein abundance of caspase 3 was measured at d2 post-MI.This short-term model of CR was associated with cardio-protection, as evidenced by decreased infarct size (18.5±2.4% vs. 26.6±1.7%, N=10/group; P=0.01. mRNA and miR profiles pre-MI (N=5/group identified genes modulated by short-term CR to be associated with circadian clock, oxidative stress, immune function, apoptosis, metabolism, angiogenesis, cytoskeleton and extracellular matrix (ECM. Western blots pre-MI revealed CR-associated increases in phosphorylated Akt and GSK3ß, reduced levels of phosphorylated AMPK and mitochondrial related proteins PGC-1α, cytochrome C and cyclooxygenase (COX IV, with no differences in the levels of phosphorylated eNOS or MAPK (ERK1/2; p38. CR regimen was also associated with reduced protein abundance of cleaved caspase 3 in the infarcted heart and improved cardiac function.

  13. 支持短时交通流量预测的概率图模型构建与推理%Construction and Inference of Probabilistic Graphical Model for Short-term Traffic Flow Prediction

    Institute of Scientific and Technical Information of China (English)

    吴杰; 岳昆; 刘惟一; 赵小明

    2011-01-01

    短时交通流量预测,是交通系统信息化和智能化交通运输管理技术领域研究的关键问题.目前的方法对历史数据具有较高的依赖程度,或者具有较高的计算成本,或者不能有效反映实际中较复杂的交通网络及各结点之间的相互关系、以及依赖的不确定性,或者多种模型的组合使得预测方法较复杂.贝叶斯网是一种重要的概率图模型,本文以交通网络结构为基础,利用概率图模型在不确定性知识表示和推理方面的良好性质,考虑路口交通流量及其预测的时序依赖特征,构建了带有时序条件依赖关系的交通贝叶斯网.进而针对短时交通流量预测的实时性和高效性要求,提出了基于Gibbs采样的交通贝叶斯网近似概率推理算法,并进行交通流量的短时预测.实验结果表明,本文提出的交通贝叶斯网构建、近似推理以及相应的短时交通流量的预测方法,具有高效性、准确性和可用性.%Short-term traffic flow prediction is the critical problem of informative and intelligent management technology in traffic transportation and systems. Current research depends much on historical data, has high computational cost, cannot reflect the dependencies among roads in complex traffic networks nor the uncertainty of these dependencies, or derives complex methods by integrating various underlying models. Bayesian network (BN) is an important probabilistic graphical model (PGM). In this paper, we construct the traffic BN (TBN) with time-series dependencies based on the traffic network and well-behaved properties of PGM on representing and inferring uncertain knowledge. Further, we propose an approximate algorithm for TBN inferences and the corresponding short-term traffic flow prediction based on the basic idea of Gibbs sampling and the inherence of instantaneous and efficient traffic prediction. Experimental results show that the methods for TBN construction, inference and the

  14. 考虑尾流效应的风电场短期功率空间预测模型%Spatial model for short term wind power prediction considering wake effects

    Institute of Scientific and Technical Information of China (English)

    曾程; 叶林; 赵永宁

    2012-01-01

    System integration of wind farms into power grids has influence on power systems operation due to its fluctuation and stochasticity. A short term prediction model based on spatial correlation approach is introduced to deal with the uncertainty of wind power. Firstly, continuous partial differential equation of each wind turbine has been developed by calculating wake effects in accordance with specific spatial location and distribution of correlated wind generators. Then, discretization of differential equation at each grid point derives spatial correlation matrix of wind speed through a finite volume method (FVM), and wind velocity of each turbine corresponding to the solution of differential equation above is solved under given boundary conditions. Finally, the short term wind power prediction is made through a practical wind power curve. Forecasting results show that the spatial correlation model of wind farm can be practically used for calculating output power in wind farm quantitatively and eliminating the uncertainty in short term wind power prediction.%由于风电的波动性和随机性,并网运行的风电场会对电力系统稳定性产生一定的影响.针对风电场输出功率的不确定性,构建了一种基于物理方法的空间相关模型来对短期风电功率进行预测.首先,该模型根据风电场风力机之间的空间位置及排布关系,计算风力机之间的尾流效应,导出了各风力机处的连续微分方程,然后使用有限体积法将网格点处的微分方程进行离散化,推导出风速空间相关矩阵,通过给定的边界条件求解上述微分方程,得出各个风力机的输入风速.最后将风速计算结果输入功率转化曲线得到各风力机的输出功率,从而预测风电场的输出功率.研究结果表明,空间相关模型可以较好地量化每台风力机的输出功率,将风电场输出功率的不确定性转化为相对量化的确定性关系,具有一定的实用价值.

  15. Visual Short-Term Memory Complexity

    DEFF Research Database (Denmark)

    Sørensen, Thomas Alrik

    Several recent studies have explored the nature and limits of visual short-term memory (VSTM) (e.g. Luck & Vogel, 1997). A general VSTM capacity limit of about 3 to 4 letters has been found, thus confirming results from earlier studies (e.g. Cattell, 1885; Sperling, 1960). However, Alvarez...... and Cavanagh (2004) have raised the question that the capacity of VSTM is dependent on visual complexity rather than the number of objects. We hypothesise that VSTM capacity is dependent on both the objective and subjective complexity of visual stimuli. Contrary to Alvarez and Cavanagh, who argue for the role...... of objective complexity, it seems that subjective complexity - which is dependent on the familiarity of the stimulus - plays a more important role than the objective visual complexity of the objects stored. In two studies, we explored how familiarity influences the capacity of VSTM. 1) In children learning...

  16. The Ultra short-term prediction of wind power based on ARMA-GARCH model%基于 ARMA-GARCH 模型的超短期风功率预测研究

    Institute of Scientific and Technical Information of China (English)

    田波; 朴在林; 郭丹; 王慧

    2016-01-01

    Wind power prediction is very important to improve the power quality and the safe operation of power sys -tem.The ultra short-term prediction of wind power data is carried out in a wind farm in Chifeng of Inner Mongola based on time series analysis , and the ARMA ( Autoregressive Moving Average ) model of time series is built through the stationary test of data.Through ARCH effect of the residual of ARMA model by Lagrange Multiplier (LM), the corresponding ARMA-GARCH model is set up .Through the comparison of the wind power prediction by using ARMA model , ARMA-ARCH model and ARMA-GARCH model respectively , ARMA-GARCH model possesses higher accura-cy on the residual sequence of the data with the long term correlation .%风功率预测对提高电能质量和电力系统的安全运行具有重要意义。基于时间序列的方法,对内蒙古赤峰地区某风场的风功率数据进行了超短期预测,通过对数据平稳性检验的结果,建立了时间序列的ARMA模型,利用拉格朗日乘子检验的方法,检验ARMA模型具有ARCH效应,并建立适合的ARMA-GARCH模型。结论通过对比ARMA模型,ARMA-ARCH模型和ARMA-GARCH模型的风功率预测精度可知,在解决数据的残差序列异方差函数具有长期相关性时,ARMA-GARCH模型能够有效的提高预测精度。

  17. SHORT-TERM FORECASTING OF MORTGAGE LENDING

    Directory of Open Access Journals (Sweden)

    Irina V. Orlova

    2013-01-01

    Full Text Available The article considers the methodological and algorithmic problems arising in modeling and forecasting of time series of mortgage loans. Focuses on the processes of formation of the levels of time series of mortgage loans and the problem of choice and identification of models in the conditions of small samples. For forecasting options are selected and implemented a model of autoregressive and moving average, which allowed to obtain reliable forecasts.

  18. Representation of Instantaneous and Short-term Loudness in the Human Cortex

    Directory of Open Access Journals (Sweden)

    Andrew eThwaites

    2016-04-01

    Full Text Available Acoustic signals pass through numerous transforms in the auditory system before perceptual attributes such as loudness and pitch are derived. However, relatively little is known as to exactly when these transformations happen, and where, cortically or sub-cortically, they occur. In an effort to examine this, we investigated the latencies and locations of cortical entrainment to two transforms predicted by a model of loudness perception for time-varying sounds: the transforms were instantaneous loudness and short-term loudness, where the latter is hypothesized to be derived from the former and therefore should occur later in time. Entrainment of cortical activity was estimated by electro- and magneto-encephalographic (EMEG activity, recorded while healthy subjects listened to continuous speech. There was entrainment to instantaneous loudness bilaterally at 45 ms, 100 ms and 165 ms, in Heschl’s gyrus, dorsal lateral sulcus and Heschl’s gyrus respectively. Entrainment to short-term loudness was found in both the dorsal lateral sulcus and superior temporal sulcus at 275 ms. These results suggest that short-term loudness is derived from instantaneous loudness, and that this derivation occurs after processing in sub-cortical structures.

  19. A Short Term Seismic Hazard Assessment in Christchurch, New Zealand, After the M 7.1, 4 September 2010 Darfield Earthquake: An Application of a Smoothing Kernel and Rate-and-State Friction Model

    Directory of Open Access Journals (Sweden)

    Chung-Han Chan

    2012-01-01

    Full Text Available The Mw 6.3, 21 February 2011 Christchurch, New Zealand, earthquake is regarded as an aftershock of the M 7.1, 4 September 2010 Darfield earthquake. However, it caused severe damage in the downtown Christchurch. Such a circumstance points out the importance of an aftershock sequence in seismic hazard evaluation and suggests the re-evaluation of a seismic hazard immediately after a large earthquake occurrence. For this purpose, we propose a probabilistic seismic hazard assessment (PSHA, which takes the disturbance of a short-term seismicity rate into account and can be easily applied in comparison with the classical PSHA. In our approach, the treatment of the background seismicity rate is the same as in the zoneless approach, which considers a bandwidth function as a smoothing Kernel in neighboring region of earthquakes. The rate-and-state friction model imparted by the Coulomb stress change of large earthquakes is used to calculate the fault-interaction-based disturbance in seismicity rate for PSHA. We apply this approach to evaluate the seismic hazard in Christchurch after the occurrence of the M 7.1, 4 September 2010 Darfield earthquake. Results show an increase of seismic hazards due to the stress increase in the region around the rupture plane, which extended to Christchurch. This provides a suitable basis for the application of a time-dependent PSHA using updating earthquake information.

  20. Study on Short-Term Prediction for Satellite Clock Bias Based on ARIMA Model%基于 ARIMA 模型的卫星钟差短期预报研究

    Institute of Scientific and Technical Information of China (English)

    范旭亮; 王晓红; 张显云; 王阳; 潘绍林; 冯富寿

    2015-01-01

    The precision of satellite clock bias has a significant impact on the capacity of navigation and positioning system .Based on the space atomic clocks from different types , this paper used the ARIMA time series model to predict the short -term (6h and one day) satellite clock error by adopting IGS 5min and 30s -interval precise satellite clock products on one day .Results show that, the prediction accuracy of rubidium clock can reach sub nanosecond level ;caesium clock is at the nanosecond level .Moreover, the sam-ple interval of original satellite clock products has effect on the precision of satellite clock error prediction .%卫星钟差的精度直接影响导航定位的性能。本文针对不同类型的星载原子钟,采用ARIMA时间序列模型分别对1 d的IGS 5 min、30 s采样间隔的精密卫星钟差产品进行建模,并作6 h、一天的短期预报。结果表明,铷钟的预报精度达到了亚纳秒级,铯钟的预报精度处于纳秒级,并且原始钟差产品的采样间隔对卫星钟差预报精度有一定的影响。

  1. Short-term wind speed forecasting model based on D-S evidence theory%基于D-S证据理论的短期风速预测模型

    Institute of Scientific and Technical Information of China (English)

    刘亚南; 卫志农; 朱艳; 孙国强; 孙永辉; 杨友情; 钱瑛; 周军

    2013-01-01

    提出一种基于D-S证据理论的短期风速组合预测模型.分别采用时间序列、BP神经网络和支持向量机预测模型对风速进行预测,通过对预测误差的分析,借助D-S证据理论对3种模型进行融合.选取待测日前凡日的风速数据作为融合样本,计算出相应的基本信任分配函数,同时将函数进行融合,并将融合结果作为风速预测模型的权重,得到待预测日的风速预测结果.仿真结果表明,所提组合预测模型的预测误差更小,效果更好.%A combined short-term wind speed forecasting model based on D-S evidence theory is proposed.The forecasting models of time series,BP neural network and support vector machine are adopted to respectively forecast the wind speed.Based on the analysis of forecast errors,D-S evidence theory is applied to fuse these three models.The wind speed data for several days before are taken as the fusion samples to calculate the corresponding basic trust distribution functions,which are then fused.The results of fusion are taken as the weights of the wind speed forecasting model and the wind speed of the day to be forecasted is calculated.Simulative results show that,the proposed combined forecasting model has smaller forecasting error and better effect.

  2. Short Term Effect of Exercise on Intraocular Pressure of Ocular ...

    African Journals Online (AJOL)

    Short Term Effect of Exercise on Intraocular Pressure of Ocular Hypertensive Subjects. ... AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search ... Keywords: Intraocular pressure; short term exercise; ocular hypertension.

  3. Analysis of growth characteristics in short-term divergently selected ...

    African Journals Online (AJOL)

    Analysis of growth characteristics in short-term divergently selected Japanese quail. ... AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search · USING ... This study was carried out to examine the effect of short-term selection for ...

  4. Effect Of Admission Hyperglycaemia On Short-Term Outcome In ...

    African Journals Online (AJOL)

    Effect Of Admission Hyperglycaemia On Short-Term Outcome In Adult ... AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search · USING AJOL ... Admission hyperglycaemia is a significant predictor of short-term case fatality but ...

  5. Short term climate trend and variability around Woliso, Oromia ...

    African Journals Online (AJOL)

    Short term climate trend and variability around Woliso, Oromia Region, Central Ethiopia. ... AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search ... for the last decade (2004-2013), short-term climate variability was assessed.

  6. Short-term prediction towards the 21st century

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Joensen, Alfred K.; Madsen, Henrik

    2000-01-01

    A new chapter in the continued and exiting story of short-term prediction has begun! The paper will describe a new project funded by the Dnaisn Ministry of Energy where all the Danish utilities (Elkraft, ELsam, Eltra, and SEAS) will participate. The goal of the project is to develop and implement...... on-line a model combining the RISO and IMM models. This will ensure that the best forecasts are giveen on all prediction horizons form the very short range (o-9 hours) to the very long range (36-48 hours)....

  7. Attentional priorities and access to short-term memory

    DEFF Research Database (Denmark)

    Gillebert, Celine; Dyrholm, Mads; Vangkilde, Signe Allerup

    2012-01-01

    The intraparietal sulcus (IPS) has been implicated in selective attention as well as visual short-term memory (VSTM). To contrast mechanisms of target selection, distracter filtering, and access to VSTM, we combined behavioral testing, computational modeling and functional magnetic resonance......, thereby displaying a significant interaction between the two factors. The interaction between target and distracter set size in IPS could not be accounted for by a simple explanation in terms of number of items accessing VSTM. Instead, it led us to a model where items accessing VSTM receive differential...

  8. In Search of Decay in Verbal Short-Term Memory

    Science.gov (United States)

    Berman, Marc G.; Jonides, John; Lewis, Richard L.

    2009-01-01

    Is forgetting in the short term due to decay with the mere passage of time, interference from other memoranda, or both? Past research on short-term memory has revealed some evidence for decay and a plethora of evidence showing that short-term memory is worsened by interference. However, none of these studies has directly contrasted decay and…

  9. 基于粗糙集和RBF神经网络的风电场短期风速预测模型%Short-term wind speed prediction for wind farms based on rough sets and RBF neural network model

    Institute of Scientific and Technical Information of China (English)

    王莉; 王德明; 张广明; 周献中

    2011-01-01

    A radical basis function (RBF) neural network model combined with rough sets was used to predict short-term wind speed. Rough sets were used to reduce input feature space so that the significant factors for wind speed prediction could be found as the input variables of RBF neural network prediction model. Online rolling optimization was adopted in training RBF neural network. The latest sample was added into the training sets, thus the prediction model could catch recent changes of wind speed. The proposed method was used to predict wind speed in 1 h. Simulation results showed that the method had advantages of simplicity and high precision.%结合粗糙集提出了一种RBF神经网络短期风速预测模型.采用粗糙集对预测模型的输入特征空间进行约简,找出对未来预测的风速具有主要影响的因素,以此作为RBF神经网络预测模型的输入变量;在RBF神经网络训练的过程中,采用在线滚动优化策略,将最新的样本加入训练集,从而使预测模型能够跟踪风速的最新变化.将提出的方法用于某风电场的lh短期风速预测,仿真实验结果表明该方法具有结构简单、预测精度高的优点.

  10. Establishment of Short-Term Wind Speed Prediction Model for New Wind Farm%新投产风电场的短期风速预测模型建立

    Institute of Scientific and Technical Information of China (English)

    陈欣; 孙翰墨; 申烛; 孟凯锋; 岳捷

    2014-01-01

    常规的风电场功率预测建模主要方法是将数值天气预报产生的气象要素输入基于历史scada数据建立统计模型,得到全场预报总功率。但是新投产的风电场没有历史scada数据,而风电场功率预测的准确性主要依赖于短期风速预报的精度。因此,为提高新投产风电场功率预测的准确性,短期风速预报的建立是基于数值气象预报的物理模型和统计模型相结合的方式。首先,通过数值气象模式输出风电场测风塔处轮毂高度层的气象要素;其次,通过建立神经网络模型和多元线性回归两种统计方法对模式输出数据进行修正;最后,对误差的来源进行分类分析。在江苏某风场的测试结果表明,较传统的方式,预测精度有了明显的提高,该方法能够消除数值气象预报的振幅偏差,但相位偏差仍是误差的主要来源。%Conventional wind power prediction method primarily uses numerical weather prediction to generate histori-cal scada data and build statistical model, by which the total power prediction is made. As the new wind farm could not collect historical scada data, the accuracy of the wind farm power prediction relies on the accuracy of short-term wind speed prediction. Therefore, to improve the accuracy of wind power prediction for new wind farm, the short-term wind speed prediction is established based on the combination of physical and statistical models of numerical weather prediction. First, the numerical weather model is used to output the meteorological element at turbine hub height lay-ers. Second, the output data are corrected by the establishment of neural network model and multiple linear regression model. Finally, sources of errors are classified and analyzed. Results of wind farm test in Jiangsu province indicate that, compared with traditional methods, this method can significantly improve the accuracy of wind speed prediction and eliminate the

  11. Statistical approaches to short-term electricity forecasting

    Science.gov (United States)

    Kellova, Andrea

    The study of the short-term forecasting of electricity demand has played a key role in the economic optimization of the electric energy industry and is essential for power systems planning and operation. In electric energy markets, accurate short-term forecasting of electricity demand is necessary mainly for economic operations. Our focus is directed to the question of electricity demand forecasting in the Czech Republic. Firstly, we describe the current structure and organization of the Czech, as well as the European, electricity market. Secondly, we provide a complex description of the most powerful external factors influencing electricity consumption. The choice of the most appropriate model is conditioned by these electricity demand determining factors. Thirdly, we build up several types of multivariate forecasting models, both linear and nonlinear. These models are, respectively, linear regression models and artificial neural networks. Finally, we compare the forecasting power of both kinds of models using several statistical accuracy measures. Our results suggest that although the electricity demand forecasting in the Czech Republic is for the considered years rather a nonlinear than a linear problem, for practical purposes simple linear models with nonlinear inputs can be adequate. This is confirmed by the values of the empirical loss function applied to the forecasting results.

  12. Short Term Electrical Load Forecasting by Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hong Li

    2016-07-01

    Full Text Available This paper presents an application of artificial neural networks for short-term times series electrical load forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of training process. Historical data of hourly power load as well as hourly wind power generation are sourced from European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training with the adaptive learning factor starting at different initial value and errors behave volatile with constant learning factors with different values

  13. Short-Term Saved Leave Scheme

    CERN Multimedia

    HR Department

    2007-01-01

    As announced at the meeting of the Standing Concertation Committee (SCC) on 26 June 2007 and in http://Bulletin No. 28/2007, the existing Saved Leave Scheme will be discontinued as of 31 December 2007. Staff participating in the Scheme will shortly receive a contract amendment stipulating the end of financial contributions compensated by save leave. Leave already accumulated on saved leave accounts can continue to be taken in accordance with the rules applicable to the current scheme. A new system of saved leave will enter into force on 1 January 2008 and will be the subject of a new im-plementation procedure entitled "Short-term saved leave scheme" dated 1 January 2008. At its meeting on 4 December 2007, the SCC agreed to recommend the Director-General to approve this procedure, which can be consulted on the HR Department’s website at the following address: https://cern.ch/hr-services/services-Ben/sls_shortterm.asp All staff wishing to participate in the new scheme ...

  14. Short-Term Saved Leave Scheme

    CERN Multimedia

    2007-01-01

    As announced at the meeting of the Standing Concertation Committee (SCC) on 26 June 2007 and in http://Bulletin No. 28/2007, the existing Saved Leave Scheme will be discontinued as of 31 December 2007. Staff participating in the Scheme will shortly receive a contract amendment stipulating the end of financial contributions compensated by save leave. Leave already accumulated on saved leave accounts can continue to be taken in accordance with the rules applicable to the current scheme. A new system of saved leave will enter into force on 1 January 2008 and will be the subject of a new implementation procedure entitled "Short-term saved leave scheme" dated 1 January 2008. At its meeting on 4 December 2007, the SCC agreed to recommend the Director-General to approve this procedure, which can be consulted on the HR Department’s website at the following address: https://cern.ch/hr-services/services-Ben/sls_shortterm.asp All staff wishing to participate in the new scheme a...

  15. Continuity of Landsat observations: Short term considerations

    Science.gov (United States)

    Wulder, M.A.; White, Joanne C.; Masek, J.G.; Dwyer, J.; Roy, D.P.

    2011-01-01

    As of writing in mid-2010, both Landsat-5 and -7 continue to function, with sufficient fuel to enable data collection until the launch of the Landsat Data Continuity Mission (LDCM) scheduled for December of 2012. Failure of one or both of Landsat-5 or -7 may result in a lack of Landsat data for a period of time until the 2012 launch. Although the potential risk of a component failure increases the longer the sensor's design life is exceeded, the possible gap in Landsat data acquisition is reduced with each passing day and the risk of Landsat imagery being unavailable diminishes for all except a handful of applications that are particularly data demanding. Advances in Landsat data compositing and fusion are providing opportunities to address issues associated with Landsat-7 SLC-off imagery and to mitigate a potential acquisition gap through the integration of imagery from different sensors. The latter will likely also provide short-term, regional solutions to application-specific needs for the continuity of Landsat-like observations. Our goal in this communication is not to minimize the community's concerns regarding a gap in Landsat observations, but rather to clarify how the current situation has evolved and provide an up-to-date understanding of the circumstances, implications, and mitigation options related to a potential gap in the Landsat data record. ?? 2010.

  16. Short-term GNSS satellite clock stability

    Science.gov (United States)

    Griggs, E.; Kursinski, E. R.; Akos, D.

    2015-08-01

    Global Navigation Satellite System (GNSS) clock stability is characterized via the modified Allan deviation using active hydrogen masers as the receiver frequency reference. The high stability of the maser reference allows the GNSS clock contribution to the GNSS carrier phase variance to be determined quite accurately. Satellite clock stability for four different GNSS constellations are presented, highlighting the similarities and differences between the constellations as well as satellite blocks and clock types. Impact on high-rate applications, such as GNSS radio occultation (RO), is assessed through the calculation of the maximum carrier phase error due to clock instability. White phase noise appears to dominate at subsecond time scales. However, while we derived the theoretical contribution of white phase modulation to the modified Allan deviation, our analysis of the GNSS satellite clocks was limited to 1-200 s time scales because of inconsistencies between the subsecond results from the commercial and software-defined receivers. The rubidium frequency standards on board the Global Positioning System (GPS) Block IIF, BeiDou, and Galileo satellites show improved stability results in comparison to previous GPS blocks for time scales relevant to RO. The Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) satellites are the least stable of the GNSS constellations in the short term and will need high-rate corrections to produce RO results comparable to those from the other GNSS constellations.

  17. 基于遗传BP神经网络的短期风速预测模型%Short-term wind speed forecast model for wind farms based on genetic BP neural network

    Institute of Scientific and Technical Information of China (English)

    王德明; 王莉; 张广明

    2012-01-01

    To improve the short-term wind speed forecasting accuracy for wind farm, a prediction model based on back propagation(BP) neural network combining genetic algorithm was proposed. Autocorrelation analysis was used to discover historical wind speeds which have significant influence on predicted wind speed. The input variables of BP neural network predictive model were historical wind speeds, temperature, humidity and air pressure. Genetic algorithm was used to optimize the weights and bias of BP neural network. Optimized BP neural network was applied to predict wind speed an hour before, two hours before and three hours before individually. The simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.%为了提高风电场短期风速预测精度,提出将遗传算法和反向传播(BP)神经网络相结合的预测模型.采用自相关性分析找出对预测值影响最大的几个历史时刻风速,以历史时刻的风速、强度、湿度和气压作为BP神经网络预测模型的输入变量;利用遗传算法的全局搜索能力获得BP神经网络优化的初始权值和阈值;采用优化后的BP神经网络分别建立1、2、3h的短期风速预测模型.实验结果表明,该方法较BP神经网络具有预测精度高、收敛速度快的优点.

  18. 用于短期风速预测的优化核心向量回归模型%An optimized CVR model for short-term wind speed forecasting

    Institute of Scientific and Technical Information of China (English)

    李元诚; 杨瑞仙

    2012-01-01

    风能的不确定性和难以准确预测给风电并入电网带来了困难.风速是影响风能的重要因素,风速的预测精度对风电功率预测的准确性有重要影响.提出一种优化的核心向量回归(CVR)模型,进行短期风速预测.其风速数据从某风电场每隔1h采集1次,并采用粒子群优化(PSO)算法对CVR模型的参数进行优化,利用优化后的CVR模型进行风速预测.试验结果表明,在时空复杂度相当的情况下,该方法具有比CVR和SVR(support vector regresson)型高的预测精度.%It is difficult to merge wind power into a grid, owing to wind power's uncertainty and prediction inaccuracy. Wind speed is an important factor affecting wind power, so the accuracy of wind speed prediction has a major impact on the wind power prediction. An optimized prediction model based on nore vector regression (CVR) is proposed in short-term wind speed forecasting. The wind speed data from a wind farm are collected hourly as the inputs. The particle swarm optimization (PSO) method is used to optimize the CVR model parameters. Experimental results show that the method has higher prediction accuracy than the CVR and support vector regression (SVR) method.

  19. MHz Gravitational Waves from Short-term Anisotropic Inflation

    CERN Document Server

    Ito, Asuka

    2016-01-01

    We reveal the universality of short-term anisotropic inflation. As a demonstration, we study inflation with an exponential type gauge kinetic function which is ubiquitous in models obtained by dimensional reduction from higher dimensional fundamental theory. It turns out that an anisotropic inflation universally takes place in the later stage of conventional inflation. Remarkably, we find that primordial gravitational waves with a peak amplitude around $10^{-26}$ ~ $10^{-27}$ are copiously produced in high-frequency bands 10MHz~100MHz. If we could detect such gravitational waves in future, we would be able to probe higher dimensional fundamental theory.

  20. MHz gravitational waves from short-term anisotropic inflation

    Energy Technology Data Exchange (ETDEWEB)

    Ito, Asuka; Soda, Jiro [Department of Physics, Kobe University,Kobe 657-8501 (Japan)

    2016-04-18

    We reveal the universality of short-term anisotropic inflation. As a demonstration, we study inflation with an exponential type gauge kinetic function which is ubiquitous in models obtained by dimensional reduction from higher dimensional fundamental theory. It turns out that an anisotropic inflation universally takes place in the later stage of conventional inflation. Remarkably, we find that primordial gravitational waves with a peak amplitude around 10{sup −26}∼10{sup −27} are copiously produced in high-frequency bands 10 MHz∼100 MHz. If we could detect such gravitational waves in future, we would be able to probe higher dimensional fundamental theory.

  1. Forecasting model of short-term wind speed based on sample entropy and support vector machine%基于样本熵和支持向量机的短期风速预测模型

    Institute of Scientific and Technical Information of China (English)

    林常青; 上官安琪; 徐箭; 许梁

    2014-01-01

    提出一种经验模态分解、样本熵和支持向量机相结合的短期风速组合预测方法。首先利用经验模态分解将原始风速序列逐级分解成若干个规律性更强的子序列,以减小不同特征尺度序列间的相互影响,提高预测精度。接着计算各风速子序列的样本熵,将复杂度相近的序列归类形成一个新序列,以减少所需建立的预测模型的数量。然后对经 EMD-SE 处理后得到的新的风速子序列分别建立支持向量机预测模型,并采用遗传算法实现各模型参数的自动选取和寻优,最后将各序列的预测结果叠加得到风速预测结果。算例研究表明,该方法充分挖掘了风速序列的特性,能快速地对风速变化作出响应,预测的均方根误差和百分比误差分别比单纯采用支持向量机法降低了5.1%和5.4%,有效地提高了短期风速预测的准确度。%This paper put forward a short-term wind speed forecasting method combined with em-pirical mode decomposition (EMD),sample entropy (SE)and support vector machine (SVM). Firstly,EMD was used to change the original wind speed sequence into several more regular sub-sequences step by step to minimize the mutual influence between different sequences and improve the prediction precision.Then calculating the SE of each wind speed sequence,cluster sequences of similar complexity was formed a new sequence,which can reduce the number of forecast model required.SVM prediction models was set up respectively for the new wind speed sequences opera-ted by EMD-SE,and then automatic selection and optimization of model parameters can be real-ized by using genetic algorithm(GA).Case study results showed that the method fully exploited the characteristics of the wind speed sequence,and can respond to the change of wind speed quickly.The RMSE and MAPE of prediction were reduced by 5.1 percent and 5.4 percent com-pared with using SVM separately,and the precision of

  2. Effects of model aromatizable (17α-methyltestosterone) and non-aromatizable (5α-dihydrotestosterone) androgens on the adult mummichog (Fundulus heteroclitus) in a short-term reproductive endocrine bioassay.

    Science.gov (United States)

    Rutherford, Robert; Lister, Andrea; Hewitt, L Mark; MacLatchy, Deborah

    2015-04-01

    Androgens originating from pulp mill processing, sewage treatment facilities and agricultural activities have the potential for discharge into aquatic receiving environments. To assess androgen effects on reproductive endocrine status in fish in estuarine environments, male and female adult northern mummichog (Fundulus heteroclitus macrolepidotus) were exposed to an aromatizable androgen (17α-methyltestosterone; MT) and a non-aromatizable androgen (5α-dihydrotestosterone; DHT) in a short-term reproductive endocrine bioassay. Fish were nominally exposed to 10 μg/L or 100 μg/L DHT, or 0.1 μg/L or 1 μg/L MT for 14 days during gonadal recrudescence. Actual concentrations of androgens, as measured by enzyme immunoassay (EIA), were 10-49% of nominal MT 0.1, 3-6% of nominal MT 1, 5-10% of nominal DHT 10 and 3-25% of nominal DHT 100. Female mummichog were impacted to a greater degree by androgen exposure, with increased plasma testosterone (T) at 100 μg/L DHT, depressed plasma 17β-estradiol (E2) at both DHT concentrations and at 1 μg/L MT, as well as depressed in vitro E2 at both MT concentrations and 100 μg/L DHT. Males had depressed plasma T in the 10 μg/L DHT treatment and depressed in vitro 11-ketotestosterone production for both MT concentrations and 10 μg/L DHT. Ovarian aromatase gene expression was induced in females exposed to 1 μg/L MT. DHT at 100 μg/L increased hepatic vitellogenin-1 (VTG1) expression in males and depressed VTG1 expression in females. The range of responses between sexes and among species provides evidence for modes of actions and potential impacts of androgens in aquatic receiving environments. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. The Short-Term Load Forecasting Model Based on Bayesian Neural Network%基于贝叶斯神经网络短期负荷预测模型

    Institute of Scientific and Technical Information of China (English)

    史会峰; 牛东晓; 卢艳霞

    2012-01-01

    本文提出了基于贝叶斯神经网络(BNN)短期负荷预测模型。根据气象影响因素和电力负荷的样本数据,针对权向量参数的先验分布分别为正态分布和柯西分布两种情况,应用混合蒙特卡洛(HMC)算法学习了BNN的权向量参数。由HMC算法和Laplace算法学习的贝叶斯神经网络以及BP算法学习的传统神经网络分别对4月(春)、8月(夏)、10月(秋)和1月(冬)每月25天的每个整点时刻的负荷进行了预测。这些神经网络的输入层有11个节点,它们分别与每个整点时刻和的气象因素、上一个整点时刻的气象因素和时间变量相对应,输出层只有一个节点,它与负荷变量对应。试验结果表明HMC算法学习的BNN的预测结果的百分比平均绝对误差(MAPE)和平方根平均误差(RSME)取值远远小于由Laplace算法学习的BNN和BP算法学习的人工神经网络的MAPE和RMSE。而且,HMC算法学习的BNN在测试集和训练集上的预测误差MAPE和RMSE的相差很小。实验结果充分说明HMC算法学习的BNN具有较高的预测精度和较强的泛化能力。%A short term load forecasting model based on Bayesian neural network learned by the Hybrid Monte Carlo(HMC) algorithm is presented in this paper.The weight vector parameter of the Bayesian neural network is considered as multi-dimensional random variables.Using the weather factors and load recorders in training set,HMC algorithm is used to learn the weight vector parameter with respect to normal prior distribution and Cauchy prior distribution respectively.Two Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network learned by the BP algorithm are used to forecast the hourly load of 25 days of April(spring),August(summer),October(autumn) and January(winter) respectively.There are eleven nodes in input layer,ten nodes representing the ten weather factor

  4. Neural circuit mechanisms of short-term memory

    Science.gov (United States)

    Goldman, Mark

    Memory over time scales of seconds to tens of seconds is thought to be maintained by neural activity that is triggered by a memorized stimulus and persists long after the stimulus is turned off. This presents a challenge to current models of memory-storing mechanisms, because the typical time scales associated with cellular and synaptic dynamics are two orders of magnitude smaller than this. While such long time scales can easily be achieved by bistable processes that toggle like a flip-flop between a baseline and elevated-activity state, many neuronal systems have been observed experimentally to be capable of maintaining a continuum of stable states. For example, in neural integrator networks involved in the accumulation of evidence for decision making and in motor control, individual neurons have been recorded whose activity reflects the mathematical integral of their inputs; in the absence of input, these neurons sustain activity at a level proportional to the running total of their inputs. This represents an analog form of memory whose dynamics can be conceptualized through an energy landscape with a continuum of lowest-energy states. Such continuous attractor landscapes are structurally non-robust, in seeming violation of the relative robustness of biological memory systems. In this talk, I will present and compare different biologically motivated circuit motifs for the accumulation and storage of signals in short-term memory. Challenges to generating robust memory maintenance will be highlighted and potential mechanisms for ameliorating the sensitivity of memory networks to perturbations will be discussed. Funding for this work was provided by NIH R01 MH065034, NSF IIS-1208218, Simons Foundation 324260, and a UC Davis Ophthalmology Research to Prevent Blindness Grant.

  5. Confined placental mosaicism in short term culture

    Directory of Open Access Journals (Sweden)

    Petrović Bojana

    2016-01-01

    Full Text Available Finding of fetal chromosomal mosaicism complicates genetic counseling, as well as pregnancy management. The aim of this study was to determine the risk of confined placental mosaicism in short term culture of chorionic villous samples. We conducted a retrospective review of karyotype analysis results obtained after chorionic villous sampling (CVS in two years period. A 420 samples of chorionic villi were taken transabdominally and obtained by a semidirect method (overnight incubating culture. All fetuses with CVS mosaicism were under the intensive perinatal care. In all cases of chromosome mosaicism the additional karyotyping was performed from fetal blood samples after 22nd gestational week in order to exclude true fetal mosaicism. After delivery newborns were examined by experienced pediatrician. From 420 analyzed samples in 11 (2,6% cases we found placental mosaicism. No anomalies were seen in genetic sonogram of this fetuses and mosaicism was confirmed only in one case. Confined placental mosaicism (CPM was found in 2,1% (9/420 of all analyzed cases, and it made 90% of all placental mosaicism. In 60% (6/10 of placental mosaicism cases we found mosaicism with single aberrant cell. Trisomy 21 mosaicism was the most frequent aberration found in 30% of cases. Finding of mosaicism in chorionic villi sample is at special importance for genetic counseling, because every case has to be reveled individually regarding the type and level of mosaicism. Anyway, in every case of placental mosaicism intensive antenatal monitoring is necessary, with additional chromosome analysis from different tissue in consideration of previous findings.

  6. A Combination Forecasting Model for Short-term Wind Power Combination Based on Time Varying Probability Weight%一种基于时变概率权的短期风功率组合预测模型

    Institute of Scientific and Technical Information of China (English)

    杨苹; 叶超

    2016-01-01

    High-precision short-term wind power forecasting is useful to realize optimizing dispatching of the power system with a large number of wind turbines. In allusion to the problem of unstable forecasting of some single models such as auto-regressive and moving average (ARMA),back propagation (BP)neutral network,least squares support vector machine (LS-SVM)and so on,as well as shortages of fixed weight of covariance and the probability weight combined model,this paper presents a kind of combined forecasting model based on time varying probability by combining with daily variation character-istic of wind power. Practical examples indicate that the combined model can effectively improve precision of forecasting while the time varying probability weight combined model can dynamically adjust weight of each single model and further improve forecasting precision.%高精度的短期风功率预测有利于实现接入大量风机的电力系统优化调度。针对自回归滑动平均(auto-re-gressive and moving average,ARMA)、反向传播(back propagation,BP)神经网络、最小二乘支持向量机(least squares support vector machine,LS-SVM)等单一模型预测不稳定的问题,以及协方差、概率权组合模型权重固定的不足,结合风功率的日变化特性,提出一种基于时变概率权的组合预测模型。实际算例表明:组合模型能有效提高风功率预测的精度,而时变概率权组合模型能够动态调整各单一模型的权重,进一步提高预测精度。

  7. Short-term wind speed forecasting model based on ARMA-LSSVM and wavelet transform%基于小波变换的ARMA-LSSVM短期风速预测

    Institute of Scientific and Technical Information of China (English)

    赵辉; 李斌; 李彪; 岳有军

    2012-01-01

    对风电场风速的准确预测,可以有效减轻并网后风电对电网的影响,提高风电市场竞争力.提出将时间序列自回归滑动平均模型(Auto Regressive Moving Average,ARMA)与最小二乘支持向量机模型(Least Square Support Vector Machine,LS-SVM)相结合的混合模型短期风速预测方法.采用小波变换(Wavelet Transform,WT)方法将历史风速序列分解成具有不同频率特征的序列.根据分解后各分量的特点,对于低频趋势分量选取LS-SVM方法进行预测,而高频波动分量则选取ARMA模型进行预测,采用小波重构得到最终预测结果.仿真实例表明,不同的预测方法整体的预测精度不同,而混合模型预测的均方根误差最低为11.5%,与单一预测方法相比,混合模型提高了预测精度.%A wind speed forecasting with high accuracy can effectively reduce or avoid the adverse effect of wind farm on power grids, meanwhile it can enhance the competitive ability of wind power in electricity market. A short-term wind speed forecasting method based on auto-regressive moving average (ARMA) model and least square support vector machine (LS-SWM) model was proposed. By using wavelet transform method, the historical load data was decomposed into series with different frequency characteristics. The low frequency components were predicted by LS-SVM model, while the high frequency components were predicted by ARMA model. The final forecasting results were obtained with wavelet reconstruction. Research results show that the prediction accuracy is different from each method. The mean square error of the proposed hybrid forecast model is 11.5%, better than the results by single forecasting methods.

  8. Atmospheric corrosion of carbon steel resulting from short term exposures

    Energy Technology Data Exchange (ETDEWEB)

    Balasubramanian, R.; Cook, D.C.; Perez, T.; Reyes, J. [Department of Physics, Old Dominion University, Norfolk, VA 23529 (United States)

    1998-12-31

    The study of corrosion products from short term atmospheric exposures of carbon steel, is very important to understand the processes that lead to corrosion of steels, and ultimately improve the performance of such steel in highly corrosive environments. Many regions along the Gulf of Mexico have extremely corrosive environments due to high mean annual temperature, humidity, time-of-wetness and every high atmospheric pollutants. The process the formation of corrosion products resulting from short term exposure of carbon steel, both as a function of environmental conditions and exposure time, has been investigated. Two sets of coupons were exposed at marine and marine locations, in Campeche, Mexico. Each set was exposed between 1 and 12 months to study the corrosion as a function of time. During the exposure periods, the relative humidity, rainfall, mean temperature, wind speed and wind direction were monitored along with the chloride and sulfur dioxide concentrations in the air. The corroded coupons were analyzed by Moessbauer, Raman, Infrared spectroscopies and X-ray diffraction in order to completely identify the oxides and map their location in the corrosion coating. Scattering and transmission Moessbauer analysis showed some layering of the oxides with lepidocrocite and akaganeite closer to the surface. The fraction of akaganeite phase increased at sites with higher chloride concentrations. A detailed analysis on the development of the oxide phases as a function of exposure time and environmental conditions will be presented. (Author)

  9. Trend analysis and short-term forecast of incident HIV infection in ...

    African Journals Online (AJOL)

    Trend analysis and short-term forecast of incident HIV infection in Ghana. ... The study uses time-series modelling to determine and predict trends in incident HIV ... in incident cases than that predicted by the National AIDS Control Programme.

  10. Changes in brain tissue and behavior patterns induced by single short-term fasting in mice

    National Research Council Canada - National Science Library

    Hisatomi, Yuko; Asakura, Kyo; Kugino, Kenji; Kurokawa, Mamoru; Asakura, Tomiko; Nakata, Keiko

    2013-01-01

    .... To clarify the impact of malnutrition on brain function, we conducted a single short-term fasting study as an anorexia model using male adult mice and determined if changes occurred in migratory...

  11. Short-term wind speed forecasting based on WD-CFA-LSSVM model%基于小波变换和改进萤火虫算法优化LSSVM的短期风速预测

    Institute of Scientific and Technical Information of China (English)

    方必武; 刘涤尘; 王波; 闫秉科; 汪勋婷

    2016-01-01

    准确预测风速对风电规模化并网至关重要。为提高短期风速预测精度,提出一种基于小波分解和改进的萤火虫算法优化最小二乘支持向量机超参数的风速预测模型。首先利用小波变换将风速时序分解为近似序列和细节序列,然后对各序列分别利用一种新颖的混沌萤火虫算法优化LSSVM进行预测,最后将各序列预测值叠加得到最终风速预测值。在两种时间尺度的实测数据上进行仿真计算。结果表明,该算法较交叉验证的 LSSVM, IPSO-LSSVM, WD-DE-LSSVM及BP神经网络等多种经典算法预测精度更高,表明了该算法的有效性和优越性。%Accurately predicting wind speed is of key importance for large scale wind power connecting to the grid. To improve the short-term wind speed forecasting accuracy, a least squares support vector machine wind speed prediction model based on wavelet decomposition and improved firefly algorithm is proposed. Firstly, the actual wind speed series is decomposed and reconstructed to approximate series and detail series, then the series are separately predicted by LSSVM optimized by chaotic firefly algorithm, at last the separate prediction series are superposed as the ultimate prediction wind speed. To verify the proposed model, two different time scale actual wind speed data are applied to simulation. The results show that the proposed model has higher prediction accuracy than classical model like CV-LSSVM, IPSO-LSSVM, WD-DE-LSSVM and BP neural networks, showing its validity and superiority.

  12. Robust short-term memory without synaptic learning.

    Directory of Open Access Journals (Sweden)

    Samuel Johnson

    Full Text Available Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds. The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.

  13. A method for short term electricity spot price forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V. [VTT Energy, Espoo (Finland); Laitinen, E.; Haekli, J. [Vaasa Univ. (Finland); Antila, E. [ABB Transmit Oy (Finland)

    1998-08-01

    In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market

  14. Robust Short-Term Memory without Synaptic Learning

    Science.gov (United States)

    Johnson, Samuel; Marro, J.; Torres, Joaquín J.

    2013-01-01

    Short-term memory in the brain cannot in general be explained the way long-term memory can – as a gradual modification of synaptic weights – since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings. PMID:23349664

  15. Effective Short-term Forecasting of Wind Farms Power

    Directory of Open Access Journals (Sweden)

    Elżbieta Bogalecka

    2015-09-01

    Full Text Available Forecasting a specific wind farm’s (WF generation capacity within a 24 hour perspective requires both a reliable forecast of wind, as well as supporting tools. This tool is a dedicated model of wind farm power. This model should include not only general rules of wind to mechanical energy conversion, but also the farm’s specific features. There are many factors that influence a farm’s generation capacity, and any forecast of it, even with an accurate weather forecast, carries error. This paper presents analytical, statistical, and neuron models of wind farm power. The study is based on data from a real wind farm. Most attention is paid to the neuron models, due to a neuron network’s capability to restore farm-specific details. The research aims to answer the headline question: whether and to what extent a wind farm’s power can be forecast short-term?

  16. Establishment and evaluation of a model of short-term carcinogenesis for C57BL/6 mice%小鼠原发性肝癌短期诱导模型的建立和评价

    Institute of Scientific and Technical Information of China (English)

    刘勇; 赛岩; 刘冬梅; 刘杨; 文川; 范礼斌; 崔春萍

    2011-01-01

    Objective To establish a primary hepatoma model of mice in one month. Methods Physical injury and toxicity were induced in the liver of C57BL/6 mice through DEN intraperitoneal injection, liver regeneration was provoked with 30% partial hepatectomy (PH) ,2AAF was used to inhibit cell division of hepatic parenchymal cells to make sure that DEN toxicity would directly affect the liver oval cells to induce the mutation of the liver oval cells. Results 8(8/12) mice survived in the experimental group. The survival of each group accessed by RIA, IHC and WB indicated that almost each parameter or index was obviously changed. Conclusion According to the parameters appraised,the model of short-term carcinogenesis for C57BL/6 has been established successfully.%目的 用一个月的时间建立小鼠原发性肝癌模型.方法 用二乙基亚硝胺(DEN)通过腹腔注射的方式对C57BL/6小鼠进行肝脏物理损伤和毒性诱导,再经过30%部分肝切除(PH)刺激肝再生,同时辅以二乙酰基芴(2-AAF)抑制肝实质细胞分裂,使DEN的毒性诱导作用直接作用于肝脏卵圆细胞(OVC),诱导突变.结果 模型组存活8只(8/12),将存活的各组小鼠通过放射免疫分析(RIA)、免疫组化(IHC)和免疫印迹(WB)方法鉴定短期诱导小鼠原发性肝癌建模是否成功,结果发现原发性肝癌的绝大多数指标明显改变.结论 鉴定结果表明采用化学损伤结合部分肝切除方法,经过一个月时间成功诱导并建立了C57BL/6小鼠的原发性肝癌模型.

  17. Short-term energy outlook, Annual supplement 1995

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-07-25

    This supplement is published once a year as a complement to the Short- Term Energy Outlook, Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts. Chap. 2 analyzes the response of the US petroleum industry to the recent four Federal environmental rules on motor gasoline. Chap. 3 compares the EIA base or mid case energy projections for 1995 and 1996 (as published in the first quarter 1995 Outlook) with recent projections made by four other major forecasting groups. Chap. 4 evaluates the overall accuracy. Chap. 5 presents the methology used in the Short- Term Integrated Forecasting Model for oxygenate supply/demand balances. Chap. 6 reports theoretical and empirical results from a study of non-transportation energy demand by sector. The empirical analysis involves the short-run energy demand in the residential, commercial, industrial, and electrical utility sectors in US.

  18. Short-Term Solar Collector Power Forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Perers, Bengt

    2011-01-01

    This paper describes a new approach to online forecasting of power output from solar thermal collectors. The method is suited for online forecasting in many applications and in this paper it is applied to predict hourly values of power from a standard single glazed large area flat plate collector....... The method is applied for horizons of up to 42 hours. Solar heating systems naturally come with a hot water tank, which can be utilized for energy storage also for other energy sources. Thereby such systems can become an important part of energy systems with a large share of uncontrollable energy sources......, such as wind power. In such a scenario online forecasting is a vital tool for optimal control and utilization of solar heating systems. The method is a two-step scheme, where first a non-linear model is applied to transform the solar power into a stationary process, which then is forecasted with robust time...

  19. Advancement and prospect of short-term numerical climate prediction

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The defects of present methods of short-term numerical climate prediction are discussed in this paper, and four challenging problems are put forward. Considering our under developed computer conditions, we should innovate in the approcuch of numerical climate prediction on the basis of our own achievements and experiences in the field of short-term numerical climate prediction. It is possibly an effective way to settle the present defects of short-term numerical climate prediction.``

  20. The Domestic Market for Short-term Debt Securities

    OpenAIRE

    Matthew Boge; Ian Wilson

    2011-01-01

    The market for short-term debt is dominated by the issuance of bank securities. Yields on these securities act as an important reference rate within the financial system. The turmoil in global markets during recent years has led to significant changes in the short-term debt market as the funding profiles of banks and other issuers of short-term securities has altered.

  1. Short-term facilitation may stabilize parametric working memory trace

    Directory of Open Access Journals (Sweden)

    Vladimir eItskov

    2011-10-01

    Full Text Available Networks with continuous set of attractors are considered to be a paradigmatic model for parametric working memory, but require fine-tuning of connections and are thus structurally unstable. Here we analyzed the network with ring attractor, where connections are not perfectly tuned and the activity state therefore drifts in the absence of the stabilizing stimulus. We derive an analytical expression for the drift dynamics and conclude that the network cannot function as working memory for a period of several seconds, a typical delay time in monkey memory experiments. We propose that short-term synaptic facilitation in recurrent connections significantly improves the robustness of the model by slowing down the drift of activity bump. Extending the calculation of the drift velocity to network with synaptic facilitation, we conclude that facilitation can slow down the drift by a large factor, rendering the network suitable as a model of working memory.

  2. 一种改进的用于城市主干道行驶时间短时预测的自适应指数平滑(IAES)模型%An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street

    Institute of Scientific and Technical Information of China (English)

    李志鹏; 虞鸿; 刘允才; 刘富强

    2008-01-01

    Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.

  3. Impact of medication adherence on absenteeism and short-term disability for five chronic diseases.

    Science.gov (United States)

    Carls, Ginger S; Roebuck, M Christopher; Brennan, Troyen A; Slezak, Julie A; Matlin, Olga S; Gibson, Teresa B

    2012-07-01

    To estimate the impact of medication adherence on absenteeism and short-term disability among employees with chronic disease. Cross-sectional analysis of administrative health care claims, absenteeism, and short-term disability data using multivariate regression and instrumental variable models for five cohorts of employees: diabetes, hypertension, congestive heart failure, dyslipidemia, and asthma/chronic obstructive pulmonary disease. Adherence was defined as possessing medication on at least 80% of days during follow-up. Adherent employees with diabetes, hypertension, dyslipidemia, and asthma/chronic obstructive pulmonary disease realized between 1.7 and 7.1 fewer days absent from work and between 1.1 and 5.0 fewer days on short-term disability. Absenteeism and short-term disability days by adherent employees with congestive heart failure were not significantly different from nonadherent employees with the condition in most specifications. Appropriate management of chronic conditions can help employers minimize losses due to missed work.

  4. Grey-identification model based wind power generation short-term prediction%基于灰色-辨识模型的风电功率短期预测

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

      为了准确预测风电机组的输出功率,针对实际风场,给出一种基于灰色 GM(1,1)模型和辨识模型的风电功率预测建模方法,采用残差修正的方法对风速进行预测,得出准确的风速预测序列。同时为了提高风电功率预测的精度,引入 FIR-MA迭代辨识模型,从分段函数的角度对风电场实际风速-风电功率曲线进行拟合,取得合适的 FIR-MA 模型。利用该模型对额定容量为850 kW 的风电机组进行建模,采用平均绝对误差和均方根误差,以及单点误差作为评价指标,与风电场的实测数据进行比较分析。仿真结果表明,基于灰色-辨识模型的风电机组输出功率预测方法是有效和实用的,该模型能够很好地预测风电机组的实时输出功率,从而提高风电场输出功率预测的精确性。%To predict the output power of wind turbine accurately, based on the GM (1, 1) model and the identification method, a wind power generation short-term prediction method is presented for the real wind farm. The revision of residual error is applied to forecast the wind speed and get the accurate predicted wind speed series. Then, in order to increase the prediction precision of wind power, the FIR-MA iterative identification model is adopted to fit the real relationship between sequential wind speed and wind power and get the proper FIR-MA model. By modeling the wind turbine whose rated capacity is 850 kW, this paper compares the predicted wind generation power with the observed data using mean absolute percentage error, root mean square error and single point error as its evaluation indexes. The simulation shows the effectiveness and the practical applicability of the presented method, which can predict the real time generation power of wind turbineness and raise the accuracy of the wind power prediction. Finally, the simulation using the actual data from wind farm in China proves the efficiency of the

  5. A Kalman Filter Based Correction Model for Short-Term Wind Power Prediction%卡尔曼滤波修正的风电场短期功率预测模型

    Institute of Scientific and Technical Information of China (English)

    赵攀; 戴义平; 夏俊荣; 盛迎新

    2011-01-01

    A Kalman filter based correction model for short-term wind power prediction was proposed to solve the problem of wind energy prediction accuracy constraint induced by the systematic errors in meteorological parameters from the numerical weather prediction (NWP) model. The wind speed data from NWP were corrected dynamically by using the Kalman filter algorithm and the improved NWP set used for wind power prediction was formed by combining the corrected wind speed data with other meteorological data. The original neural network prediction model and the corrected neural network prediction model were trained by using the raw NWP set and the improved NWP set, respectively. The analysis on the comparison between the simulation data and the measured data in a same time interval shows that, the corrected wind speed series by the Kalman filter are very close to observed wind speed; the mean error and the mean absolute error are smaller; the root mean square error decreases from 17. 73% to 11.32%. It seems that the wind power prediction model proposed has a clearly higher accuracy.%针对数值天气预报模型输出的气象参数存在系统误差而导致风电场功率预测精度受到制约的问题,提出了一种基于卡尔曼滤波修正的风电场短期功率预测模型.使用卡尔曼滤波算法对数值天气预报输出的风速数据进行动态修正,并结合其他气象数据形成新的用于风电功率预测的修正气象数据集合;根据原始气象数据和修正气象数据这2个训练集分别建立了风电场功率输出的原始神经网络、修正神经网络的预测模型.经同一时间区间内的实测数据与模型分析数据的对比分析表明:通过卡尔曼滤波修正的风速数据能够很好地跟踪实际风速数据的变化趋势,平均误差与绝对平均误差比较小;所提模型能够显著降低预测结果的均方根误差,使其从未修正前的17.73%降低至11.32%,证明预测精度得到了明显提高.

  6. Working memory training improves visual short-term memory capacity.

    Science.gov (United States)

    Schwarb, Hillary; Nail, Jayde; Schumacher, Eric H

    2016-01-01

    Since antiquity, philosophers, theologians, and scientists have been interested in human memory. However, researchers today are still working to understand the capabilities, boundaries, and architecture. While the storage capabilities of long-term memory are seemingly unlimited (Bahrick, J Exp Psychol 113:1-2, 1984), working memory, or the ability to maintain and manipulate information held in memory, seems to have stringent capacity limits (e.g., Cowan, Behav Brain Sci 24:87-185, 2001). Individual differences, however, do exist and these differences can often predict performance on a wide variety of tasks (cf. Engle What is working-memory capacity? 297-314, 2001). Recently, researchers have promoted the enticing possibility that simple behavioral training can expand the limits of working memory which indeed may also lead to improvements on other cognitive processes as well (cf. Morrison and Chein, Psychol Bull Rev 18:46-60 2011). However, initial investigations across a wide variety of cognitive functions have produced mixed results regarding the transferability of training-related improvements. Across two experiments, the present research focuses on the benefit of working memory training on visual short-term memory capacity-a cognitive process that has received little attention in the training literature. Data reveal training-related improvement of global measures of visual short-term memory as well as of measures of the independent sub-processes that contribute to capacity (Awh et al., Psychol Sci 18(7):622-628, 2007). These results suggest that the ability to inhibit irrelevant information within and between trials is enhanced via n-back training allowing for selective improvement on untrained tasks. Additionally, we highlight a potential limitation of the standard adaptive training procedure and propose a modified design to ensure variability in the training environment.

  7. Routes to short term memory indexing: Lessons from deaf native users of American Sign Language

    Science.gov (United States)

    Hirshorn, Elizabeth A.; Fernandez, Nina M.; Bavelier, Daphne

    2012-01-01

    Models of working memory (WM) have been instrumental in understanding foundational cognitive processes and sources of individual differences. However, current models cannot conclusively explain the consistent group differences between deaf signers and hearing speakers on a number of short-term memory (STM) tasks. Here we take the perspective that these results are not due to a temporal order-processing deficit in deaf individuals, but rather reflect different biases in how different types of memory cues are used to do a given task. We further argue that the main driving force behind the shifts in relative biasing is a consequence of language modality (sign vs. speech) and the processing they afford, and not deafness, per se. PMID:22871205

  8. Short-term Memory Training in Listening Comprehension

    Institute of Scientific and Technical Information of China (English)

    李一菡

    2015-01-01

    Listening comprehension is a basic skill in English learning.Here,we will talk about the relationship between the short-term memory and listening comprehension, and try to find the way of the short-term memory training to improve the skill of the students in middle school.

  9. Pediatric polytrauma : Short-term and long-term outcomes

    NARCIS (Netherlands)

    vanderSluis, CK; Kingma, J; Eisma, WH; tenDuis, HJ

    1997-01-01

    Objective: To assess the short-term and long-term outcomes of pediatric polytrauma patients and to analyze the extent to which short-term outcomes can predict long-term outcomes. Materials and Methods: Ail pediatric polytrauma patients (Injury Severity Score of greater than or equal to 16, less than

  10. 75 FR 58285 - Short-Term, Small Amount Loans

    Science.gov (United States)

    2010-09-24

    ... Part 701 RIN 3133-AD71 Short-Term, Small Amount Loans Agency: National Credit Union Administration... unions (FCUs) to offer short-term, small amount loans (STS loans) as a viable alternative to predatory payday loans. The amendment permits FCUs to charge a higher interest rate for an STS loan than is...

  11. 22 CFR 62.21 - Short-term scholars.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Short-term scholars. 62.21 Section 62.21 Foreign Relations DEPARTMENT OF STATE PUBLIC DIPLOMACY AND EXCHANGES EXCHANGE VISITOR PROGRAM Specific Program Provisions § 62.21 Short-term scholars. (a) Introduction. These regulations govern scholars...

  12. Short-Term Robustness of Production Management Systems : New Methodology

    NARCIS (Netherlands)

    Kleijnen, J.P.C.; Gaury, E.G.A.

    2000-01-01

    This paper investigates the short-term robustness of production planning and control systems. This robustness is defined here as the systems ability to maintain short-term service probabilities (i.e., the probability that the fill rate remains within a prespecified range), in a variety of environmen

  13. Short-term growth in asthmatic children using fluticasone propionate

    NARCIS (Netherlands)

    Visser, MJ; van Aalderen, WMC; Elliott, BM; Odink, RJ; Brand, PLP

    1998-01-01

    Background: Inhaled corticosteroids may reduce short-term growth velocity in asthmatic children and knemometry is the most sensitive tool to detect this short-term growth suppression. Study objective: To compare lower leg growth velocity, as measured by knemometry, in asthmatic children during and a

  14. Comparison of Sugammadex and Neostigmine in Short Term Surgery

    Directory of Open Access Journals (Sweden)

    Fatih Koc

    2014-03-01

    Full Text Available Aim: This study compared the efficacy and cost effectivines of sugammadex and neostigmine for reversal of neuromuscular blockade induced by rocuronium for short term elective surgery. Material and Method: After written informed consent, 33 patients aged 18%u201365, ASA I-III, who were undergoing short term surgery (

  15. Short-term energy outlook annual supplement, 1993

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1993-08-06

    The Short-Term Energy Outlook Annual Supplement (supplement) is published once a year as a complement to the Short-Term Energy Outlook (Outlook), Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts.

  16. Short-Term Robustness of Production Management Systems : New Methodology

    NARCIS (Netherlands)

    Kleijnen, J.P.C.; Gaury, E.G.A.

    2000-01-01

    This paper investigates the short-term robustness of production planning and control systems. This robustness is defined here as the systems ability to maintain short-term service probabilities (i.e., the probability that the fill rate remains within a prespecified range), in a variety of

  17. Operations Management in Short Term Power Markets

    DEFF Research Database (Denmark)

    Heide-Jørgensen, Ditte Mølgård

    in the way the dynamic programming algorithm handles the integer variables leading to two different non-anticipativity assumptions. In the fourth chapter Open- and closed-loop equilibrium models for the day-ahead and balancing markets we look into how power producers act in market which is not perfectly....... The model is formulated with both an open-loop and closed-loop approach, and we find that the solution to the more realistic, but also computationally harder closed-loop model differs substantially from the open-loop solution. Again the day-ahead market is assumed to have hourly time resolution......Electricity market models have often been modelled as deterministic or at most two-stage stochastic models with an hourly time resolution. This thesis looks into possible ways of extending such models and formulating new models to handle both higher time resolution than hourly and stochastics...

  18. 短时絮凝-高速磁沉降溢流污水快速处理装置开发%DEVELOPMENT OF RAPID PROCESSING DEVICE OF SHORT-TERM FLOCCULATION AND HIGH-SPEED MAGNETIC SETTLEMENT TREATING OVERFLOWS

    Institute of Scientific and Technical Information of China (English)

    解清杰; 段明飞; 吴春笃; 刘兴

    2012-01-01

    An short-term flocculation and high speed magnetic settlement reactor were developed. The equipment depended on dosing magnetic seed and external magnetic field to enhance coagulation and shorten sludge settling time. The reactor had advantages such as short hydraulic retention time, small floor space and flexible operation, etc. It had been proved in experiments that when the hydraulic retention time of the equipment was 8min, the optimum operating condition of the reactor was as follows: the dosage of PAC and magnetic seed were 30 mg ·L-1, 350 mg ·L-1, respectively, and the intensity of magnetic field was 300 mT. Under this condition, the removal rate of ammonia nitrogen, phosphorus, TSS and COD were 76.67% ,85.25%, 92.5% 76.22%, respectively.%开发了一种短时絮凝-高速磁沉降反应器,依靠投加磁种和外加磁场来强化絮凝和缩短污泥沉降时间.该反应器具有水力停留时间短、占地面积小、操作灵活等优点.试验证明,对于溢流污水,当水力停留时间为8 min时,且在PAC投加量为30 mg·L-1、磁种投加量为350 mg·L-1、磁场强度为300 mT时,反应器具有最佳运行工况,其对氨氮、总磷、SS和COD去除率分别达到76.67%、85.25%、92.5%、76.22%.

  19. The effects of short-term hypergravity on Caenorhabditis elegans

    Science.gov (United States)

    Saldanha, Jenifer N.; Pandey, Santosh; Powell-Coffman, Jo Anne

    2016-08-01

    As we seek to recognize the opportunities of advanced aerospace technologies and spaceflight, it is increasingly important to understand the impacts of hypergravity, defined as gravitational forces greater than those present on the earth's surface. The nematode Caenorhabditis elegans has been established as a powerful model to study the effects of altered gravity regimens and has displayed remarkable resilience to space travel. In this study, we investigate the effects of short-term and defined hypergravity exposure on C. elegans motility, brood size, pharyngeal pumping rates, and lifespan. The results from this study advance our understanding of the effects of shorter durations of exposure to increased gravitational forces on C. elegans, and also contribute to the growing body of literature on the impacts of altered gravity regimens on earth's life forms.

  20. Sleep deprivation accelerates delay-related loss of visual short-term memories without affecting precision.

    Science.gov (United States)

    Wee, Natalie; Asplund, Christopher L; Chee, Michael W L

    2013-06-01

    Visual short-term memory (VSTM) is an important measure of information processing capacity and supports many higher-order cognitive processes. We examined how sleep deprivation (SD) and maintenance duration interact to influence the number and precision of items in VSTM using an experimental design that limits the contribution of lapses at encoding. For each trial, participants attempted to maintain the location and color of three stimuli over a delay. After a retention interval of either 1 or 10 seconds, participants reported the color of the item at the cued location by selecting it on a color wheel. The probability of reporting the probed item, the precision of report, and the probability of reporting a nonprobed item were determined using a mixture-modeling analysis. Participants were studied twice in counterbalanced order, once after a night of normal sleep and once following a night of sleep deprivation. Sleep laboratory. Nineteen healthy college age volunteers (seven females) with regular sleep patterns. Approximately 24 hours of total SD. SD selectively reduced the number of integrated representations that can be retrieved after a delay, while leaving the precision of object information in the stored representations intact. Delay interacted with SD to lower the rate of successful recall. Visual short-term memory is compromised during sleep deprivation, an effect compounded by delay. However, when memories are retrieved, they tend to be intact.

  1. [Short-term memory characteristics of vibration intensity tactile perception on human wrist].

    Science.gov (United States)

    Hao, Fei; Chen, Li-Juan; Lu, Wei; Song, Ai-Guo

    2014-12-25

    In this study, a recall experiment and a recognition experiment were designed to assess the human wrist's short-term memory characteristics of tactile perception on vibration intensity, by using a novel homemade vibrotactile display device based on the spatiotemporal combination vibration of multiple micro vibration motors as a test device. Based on the obtained experimental data, the short-term memory span, recognition accuracy and reaction time of vibration intensity were analyzed. From the experimental results, some important conclusions can be made: (1) The average short-term memory span of tactile perception on vibration intensity is 3 ± 1 items; (2) The greater difference between two adjacent discrete intensities of vibrotactile stimulation is defined, the better average short-term memory span human wrist gets; (3) There is an obvious difference of the average short-term memory span on vibration intensity between the male and female; (4) The mechanism of information extraction in short-term memory of vibrotactile display is to traverse the scanning process by comparison; (5) The recognition accuracy and reaction time performance of vibrotactile display compares unfavourably with that of visual and auditory. The results from this study are important for designing vibrotactile display coding scheme.

  2. Long-term associative learning predicts verbal short-term memory performance.

    Science.gov (United States)

    Jones, Gary; Macken, Bill

    2017-10-02

    Studies using tests such as digit span and nonword repetition have implicated short-term memory across a range of developmental domains. Such tests ostensibly assess specialized processes for the short-term manipulation and maintenance of information that are often argued to enable long-term learning. However, there is considerable evidence for an influence of long-term linguistic learning on performance in short-term memory tasks that brings into question the role of a specialized short-term memory system separate from long-term knowledge. Using natural language corpora, we show experimentally and computationally that performance on three widely used measures of short-term memory (digit span, nonword repetition, and sentence recall) can be predicted from simple associative learning operating on the linguistic environment to which a typical child may have been exposed. The findings support the broad view that short-term verbal memory performance reflects the application of long-term language knowledge to the experimental setting.

  3. Very-short-term wind power probabilistic forecasts by sparse vector autoregression

    DEFF Research Database (Denmark)

    Dowell, Jethro; Pinson, Pierre

    2016-01-01

    A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...... numerical advantages over conventional vector autoregressive models. The proposed method is tested on a dataset of 5 minute mean wind power generation at 22 wind farms in Australia. 5-minute-ahead forecasts are produced and evaluated in terms of point and probabilistic forecast skill scores and calibration...

  4. Short-term fasting alters cytochrome P450-mediated drug metabolism in humans.

    Science.gov (United States)

    Lammers, Laureen A; Achterbergh, Roos; de Vries, Emmely M; van Nierop, F Samuel; Klümpen, Heinz-Josef; Soeters, Maarten R; Boelen, Anita; Romijn, Johannes A; Mathôt, Ron A A

    2015-06-01

    Experimental studies indicate that short-term fasting alters drug metabolism. However, the effects of short-term fasting on drug metabolism in humans need further investigation. Therefore, the aim of this study was to evaluate the effects of short-term fasting (36 h) on P450-mediated drug metabolism. In a randomized crossover study design, nine healthy subjects ingested a cocktail consisting of five P450-specific probe drugs [caffeine (CYP1A2), S-warfarin (CYP2C9), omeprazole (CYP2C19), metoprolol (CYP2D6), and midazolam (CYP3A4)] on two occasions (control study after an overnight fast and after 36 h of fasting). Blood samples were drawn for pharmacokinetic analysis using nonlinear mixed effects modeling. In addition, we studied in Wistar rats the effects of short-term fasting on hepatic mRNA expression of P450 isoforms corresponding with the five studied P450 enzymes in humans. In the healthy subjects, short-term fasting increased oral caffeine clearance by 20% (P = 0.03) and decreased oral S-warfarin clearance by 25% (P fasting increased mRNA expression of the orthologs of human CYP1A2, CYP2C19, CYP2D6, and CYP3A4 (P fasting alters cytochrome P450-mediated drug metabolism in a nonuniform pattern. Therefore, short-term fasting is another factor affecting cytochrome P450-mediated drug metabolism in humans.

  5. Online short-term solar power forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

    2009-01-01

    -minute observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques...

  6. Short-term predictions in forex trading

    Science.gov (United States)

    Muriel, A.

    2004-12-01

    Using a kinetic equation that is used to model turbulence (Physica A, 1985-1988, Physica D, 2001-2003), we redefine variables to model the time evolution of the foreign exchange rates of three major currencies. We display live and predicted data for one period of trading in October, 2003.

  7. Auditory short-term memory in the primate auditory cortex.

    Science.gov (United States)

    Scott, Brian H; Mishkin, Mortimer

    2016-06-01

    Sounds are fleeting, and assembling the sequence of inputs at the ear into a coherent percept requires auditory memory across various time scales. Auditory short-term memory comprises at least two components: an active ׳working memory' bolstered by rehearsal, and a sensory trace that may be passively retained. Working memory relies on representations recalled from long-term memory, and their rehearsal may require phonological mechanisms unique to humans. The sensory component, passive short-term memory (pSTM), is tractable to study in nonhuman primates, whose brain architecture and behavioral repertoire are comparable to our own. This review discusses recent advances in the behavioral and neurophysiological study of auditory memory with a focus on single-unit recordings from macaque monkeys performing delayed-match-to-sample (DMS) tasks. Monkeys appear to employ pSTM to solve these tasks, as evidenced by the impact of interfering stimuli on memory performance. In several regards, pSTM in monkeys resembles pitch memory in humans, and may engage similar neural mechanisms. Neural correlates of DMS performance have been observed throughout the auditory and prefrontal cortex, defining a network of areas supporting auditory STM with parallels to that supporting visual STM. These correlates include persistent neural firing, or a suppression of firing, during the delay period of the memory task, as well as suppression or (less commonly) enhancement of sensory responses when a sound is repeated as a ׳match' stimulus. Auditory STM is supported by a distributed temporo-frontal network in which sensitivity to stimulus history is an intrinsic feature of auditory processing. This article is part of a Special Issue entitled SI: Auditory working memory.

  8. Short-term wind power prediction

    DEFF Research Database (Denmark)

    Joensen, Alfred K.

    2003-01-01

    The present thesis consists of 10 research papers published during the period 1997-2002 together with a summary report. The objective of the work described in the thesis is to develop models and methods for calculation of high accuracy predictions of wind power generated electricity......, and to implement these models and methods in an on-line software application. The economical value of having predictions available is also briefly considered. The summary report outlines the background and motivation for developing wind power prediction models. The meteorological theory which is relevant...... for the thesis is outlined and the background for the models and methods which are proposed in the various papers is described. The software system, Zephyr, which has been developed is also described in the summary report. The main part of the papers have been written in conjunction with two research projects...

  9. Attention Problems, Phonological Short-Term Memory, and Visuospatial Short-Term Memory: Differential Effects on Near- and Long-Term Scholastic Achievement

    Science.gov (United States)

    Sarver, Dustin E.; Rapport, Mark D.; Kofler, Michael J.; Scanlan, Sean W.; Raiker, Joseph S.; Altro, Thomas A.; Bolden, Jennifer

    2012-01-01

    The current study examined individual differences in children's phonological and visuospatial short-term memory as potential mediators of the relationship among attention problems and near- and long-term scholastic achievement. Nested structural equation models revealed that teacher-reported attention problems were associated negatively with…

  10. Attention Problems, Phonological Short-Term Memory, and Visuospatial Short-Term Memory: Differential Effects on Near- and Long-Term Scholastic Achievement

    Science.gov (United States)

    Sarver, Dustin E.; Rapport, Mark D.; Kofler, Michael J.; Scanlan, Sean W.; Raiker, Joseph S.; Altro, Thomas A.; Bolden, Jennifer

    2012-01-01

    The current study examined individual differences in children's phonological and visuospatial short-term memory as potential mediators of the relationship among attention problems and near- and long-term scholastic achievement. Nested structural equation models revealed that teacher-reported attention problems were associated negatively with…

  11. Short-term airing by natural ventilation

    DEFF Research Database (Denmark)

    Perino, Marco; Heiselberg, Per

    2009-01-01

    traditional mechanical ventilation components with natural ventilation devices, such as motorized windows and louvers. Among the various ventilation strategies that are currently available, buoyancy driven single-sided natural ventilation has proved to be very effective and can provide high air change rates...... that was aimed at developing and validating numerical models for the analysis of buoyancy driven single-sided natural ventilation systems. Once validated, these models can be used to optimize control strategies in order to achieve satisfactory indoor comfort conditions and IAQ....

  12. Online short-term solar power forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

    2009-01-01

    This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 hours. The data used is fifteen....... Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to two hours...

  13. Short-Term Foreshocks and Earthquake Prediction

    Science.gov (United States)

    Papadopoulos, G. A.; Minadakis, G.; Orfanogiannaki, K.

    2016-12-01

    Foreshock recognition before main shocks depends on various factors, e.g. geophysical, catalogue completeness, foreshock definition, spatiotemporal windows. Foreshocks move towards the main shock epicenter, their number increases with the inverse of time, their b-value drops. However, only in very few single foreshock sequences these 3-D patterns were recognized at the same time, e.g. before the 2009 L' Aquila (Italy) earthquake (Mw6.3) and the 2010, 2014 and 2015 major earthquakes (Mw8+) that ruptured at the subduction zone of Chile. For the first time we found statistically significant 3-D foreshock patterns before small-to-moderate earthquakes. We present two good examples of earthquakes occurring on 4 March 2012 (Mw5.2) and 3 July 2013 (Mw4.8) in Athos and Polyphyto, both in North Greece. The great similarity with the patterns found before strong and major earthquakes indicates that the foreshock process is scale invariant in a wide magnitude range. It is likely that the process is independent of the faulting type at least for dip-slip faulting. There is also a trend of the main shock magnitude to scale with the foreshock area. These findings imply that foreshock activity is likely governed by pattern universality which may also reflect universality in the deformation process thus opening new ways for the foreshock utilization in the prediction of the main shock.

  14. Short-term synaptic depression is topographically distributed in the cochlear nucleus of the chicken.

    Science.gov (United States)

    Oline, Stefan N; Burger, R Michael

    2014-01-22

    In the auditory system, sounds are processed in parallel frequency-tuned circuits, beginning in the cochlea. Activity of auditory nerve fibers reflects this frequency-specific topographic pattern, known as tonotopy, and imparts frequency tuning onto their postsynaptic target neurons in the cochlear nucleus. In birds, cochlear nucleus magnocellularis (NM) neurons encode the temporal properties of acoustic stimuli by "locking" discharges to a particular phase of the input signal. Physiological specializations exist in gradients corresponding to the tonotopic axis in NM that reflect the characteristic frequency (CF) of their auditory nerve fiber inputs. One feature of NM neurons that has not been investigated across the tonotopic axis is short-term synaptic plasticity. NM offers a rather homogeneous population of neurons with a distinct topographical distribution of synaptic properties that is ideal for the investigation of specialized synaptic plasticity. Here we demonstrate for the first time that short-term synaptic depression (STD) is expressed topographically, where unitary high CF synapses are more robust with repeated stimulation. Correspondingly, high CF synapses drive spiking more reliably than their low CF counterparts. We show that postsynaptic AMPA receptor desensitization does not contribute to the observed difference in STD. Further, rate of recovery from depression, a presynaptic property, does not differ tonotopically. Rather, we show that another presynaptic feature, readily releasable pool (RRP) size, is tonotopically distributed and inversely correlated with vesicle release probability. Mathematical model results demonstrate that these properties of vesicle dynamics are sufficient to explain the observed tonotopic distribution of STD.

  15. Olfactory short-term memory encoding and maintenance - an event-related potential study.

    Science.gov (United States)

    Lenk, Steffen; Bluschke, Annet; Beste, Christian; Iannilli, Emilia; Rößner, Veit; Hummel, Thomas; Bender, Stephan

    2014-09-01

    This study examined whether the memory encoding and short term maintenance of olfactory stimuli is associated with neurophysiological activation patterns which parallel those described for sensory modalities such as vision and auditory. We examined olfactory event-related potentials in an olfactory change detection task in twenty-four healthy adults and compared the measured activation to that found during passive olfactory stimulation. During the early olfactory post-processing phase, we found a sustained negativity over bilateral frontotemporal areas in the passive perception condition which was enhanced in the active memory task. There was no significant lateralization in either experimental condition. During the maintenance interval at the end of the delay period, we still found sustained activation over bilateral frontotemporal areas which was more negative in trials with correct - as compared to incorrect - behavioural responses. This was complemented by a general significantly stronger frontocentral activation. Summarizing, we were able to show that olfactory short term memory involves a parallel sequence of activation as found in other sensory modalities. In addition to olfactory-specific frontotemporal activations in the memory encoding phase, we found slow cortical potentials over frontocentral areas during the memory maintenance phase indicating the activation of a supramodal memory maintenance system. These findings could represent the neurophysiological underpinning of the 'olfactory flacon', the olfactory counter-part to the visual sketchpad and phonological loop embedded in Baddeley's working memory model.

  16. Short-term Forecasting Models on Occurrence of Rice Leaf Roller Based on Kalman Filter Algorithm%基于卡尔曼滤波算法的稻纵卷叶螟短期预测模型

    Institute of Scientific and Technical Information of China (English)

    包云轩; 陈心怡; 谢晓金; 王琳; 陆明红

    2016-01-01

    利用1994-2014年中国南方四大稻区(华南、西南、江岭和江淮稻区)代表性病虫测报站的稻纵卷叶螟逐候田间赶蛾量资料,筛选出影响各站稻纵卷叶螟发生量的关键气象因子,应用卡尔曼滤波方法分别对各站建立稻纵卷叶螟迁入期候发生量的卡尔曼短期预测模型,并计算模型的准确率、误差大小和稳定性。结果表明:(1)稻纵卷叶螟发生量与前一候和前两候的田间蛾量呈极显著正相关(P<0.01),与前一候的近地面最低气温、平均气温和最高气温呈极显著正相关(P<0.01),与前一候的地面气压呈极显著负相关(P<0.01)。(2)经1994-2011年的回检拟合和2012-2014年试报检验,卡尔曼模型的发生量预测综合平均误差为-88.63,平均绝对误差为217.72,均方根误差为605.04。发生量预测综合准确率为84.33%,平均历史拟合率为83.33%,各站卡尔曼模型的预报结果与实测值基本吻合,表明模型可以应用于稻纵卷叶螟候发生量的预测。%In this paper, the pentad systematic investigation data ofC. Medinalis at the four representative plant protection stations of four main rice-growing regions (including the rice-growing region of the south China, the rice-growing region of the southwestern China, the rice-growing region between the Nanling mountains and the Yantze River valley and the rice-growing region between the Yantze River valley and the Huaihe River valley) in China was collected from 1994 to 2014, the key meteorological factors influencing onC. Medinalis’ occurrence amount were screened out and Kalman filter algorithm was used to establish the short-term forecasting models ofC. Medinalis’ pentad occurrence amount at the four plant protection stations, including Quanzhou in the Guangxi Zhuang Autonomous Region, Xiushan in Chongqing city, Xiangyin in Hunan province and Zhangjiagang in Jiangsu province in the immigration and

  17. Research on short term wind speed prediction model based on chaotic time series using support vector machine%基于混沌时间序列的支持向量机短期风速预测模型研究

    Institute of Scientific and Technical Information of China (English)

    黄彦辉; 王龙杰; 杨薛明

    2015-01-01

    风电场风速及风电功率预测技术是加强风电并网管理的关键措施之一。为了提高短期风速预测的精度,减小风电并网对电力系统的电能质量及其安全稳定运行带来的影响,提出了基于混沌时间序列的支持向量机短期风速预测模型。该模型针对风速混沌时间序列建模,并采用基于贝叶斯框架的最小二乘支持向量机对风速进行短期预测。仿真实验结果表明,该预测模型有效地提高了短期风速预测的精度。%Wind speed and wind power forecasting technology are key measures to strengthen the grid-connected man-agement of wind power.In order to improve the accuracy of short-term wind forecasting and reduce the impact of wind power grid-connection on power quality and the safe and stable operation of power system, a short term wind speed prediction model based on chaotic time series using support vector machine is proposed.In this model, short-term wind speed prediction is conducted by using least squares support vector machine under the Bayesian framework based on the modeling of chaotic time series of wind speed.Simulation results show that the proposed model can effectively improve the accuracy of short term wind speed prediction.

  18. Short-term regulation of hydro powerplants. Studies on the environmental effects

    Energy Technology Data Exchange (ETDEWEB)

    Sinisalmi, T. [ed.; Forsius, J.; Muotka, J.; Soimakallio, H. [Imatran Voima Oy, Vantaa (Finland); Riihimaeki, J. [VTT, Espoo (Finland); Vehanen, T. [Finnish Game and Fisheries Research Inst. (Finland); Yrjaenae, T. [North Ostrobothnia Regional Environmental Centre, Oulu (Finland)

    1997-12-31

    The publication is a final report on a project studying effects of short-term regulation of hydro power plants. The project consists of two parts: (1) examining and developing methods for evaluation, (2) applying methods in a case study at the Oulujoki River. The economic value of short-term regulation was studied with a model consisting of an optimization model and a river simulation model. Constraints on water level or discharge variations could be given to the power plants and their economical influence could be studied. Effects on shoreline recreation use due to water level fluctuation were studied with a model where various effects are made commensurable and expressed in monetary terms. A literature survey and field experiments were used to study the methods for assessing effects of short-term regulation on river habitats. The state and development needs of fish stocks and fisheries in large regulated rivers were studied and an environmental classification was made. Remedial measures for the short-term regulated rivers were studied with a literature survey and enquiries. A comprehensive picture of the various effects of short-term regulation was gained in the case study in Oulujoki River (110 km long, 7 power plants). Harmful effects can be reduced with the given recommendations of remedial measures on environment and the usage of the hydro power plants. (orig.) 52 refs.

  19. Concussion Can Spur Short-Term Change in Women's Periods

    Science.gov (United States)

    ... page: https://medlineplus.gov/news/fullstory_167006.html Concussion Can Spur Short-Term Change in Women's Periods ... MONDAY, July 3, 2017 (HealthDay News) -- After a concussion, a young woman might notice that her next ...

  20. Short term variations in particulate matter in Mahi river estuary

    Digital Repository Service at National Institute of Oceanography (India)

    Bhosle, N.B.; Rokade, M.A.; Zingde, M.D.

    The particulate matter (PM) collected from Mahi River Estuary was analysed for organic carbon (POC), nitrogen (PON), and chlorophyll a (Chl a). The concentration of PM, POC, PON and Chl a showed short term variations. Average surface concentration...