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Sample records for modelling short-term dynamic

  1. Modeling Long-Term Fluvial Incision : Shall we Care for the Details of Short-Term Fluvial Dynamics?

    Lague, D.; Davy, P.

    2008-12-01

    Fluvial incision laws used in numerical models of coupled climate, erosion and tectonics systems are mainly based on the family of stream power laws for which the rate of local erosion E is a power function of the topographic slope S and the local mean discharge Q : E = K Qm Sn. The exponents m and n are generally taken as (0.35, 0.7) or (0.5, 1), and K is chosen such that the predicted topographic elevation given the prevailing rates of precipitation and tectonics stay within realistic values. The resulting topographies are reasonably realistic, and the coupled system dynamics behaves somehow as expected : more precipitation induces increased erosion and localization of the deformation. Yet, if we now focus on smaller scale fluvial dynamics (the reach scale), recent advances have suggested that discharge variability, channel width dynamics or sediment flux effects may play a significant role in controlling incision rates. These are not factored in the simple stream power law model. In this work, we study how these short- term details propagate into long-term incision dynamics within the framework of surface/tectonics coupled numerical models. To upscale the short term dynamics to geological timescales, we use a numerical model of a trapezoidal river in which vertical and lateral incision processes are computed from fluid shear stress at a daily timescale, sediment transport and protection effects are factored in, as well as a variable discharge. We show that the stream power law model might still be a valid model but that as soon as realistic effects are included such as a threshold for sediment transport, variable discharge and dynamic width the resulting exponents m and n can be as high as 2 and 4. This high non-linearity has a profound consequence on the sensitivity of fluvial relief to incision rate. We also show that additional complexity does not systematically translates into more non-linear behaviour. For instance, considering only a dynamical width

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

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

    2016-09-01

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

  3. Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

    Wenlei Bai

    2017-12-01

    Full Text Available The deterministic methods generally used to solve DC optimal power flow (OPF do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.

  4. Short-term Consumer Benefits of Dynamic Pricing

    Dupont, Benjamin; De Jonghe, Cedric; Kessels, Kris; Belmans, Ronnie

    2011-01-01

    Consumer benefits of dynamic pricing depend on a variety of factors. Consumer characteristics and climatic circumstances widely differ, which forces a regional comparison. This paper presents a general overview of demand response programs and focuses on the short-term benefits of dynamic pricing for an average Flemish residential consumer. It reaches a methodology to develop a cost reflective dynamic pricing program and to estimate short-term bill savings. Participating in a dynamic pricing p...

  5. An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom

    Yao Junyang

    2014-06-01

    Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.

  6. Short-term mechanisms influencing volumetric brain dynamics

    Nikki Dieleman

    2017-01-01

    Full Text Available With the use of magnetic resonance imaging (MRI and brain analysis tools, it has become possible to measure brain volume changes up to around 0.5%. Besides long-term brain changes caused by atrophy in aging or neurodegenerative disease, short-term mechanisms that influence brain volume may exist. When we focus on short-term changes of the brain, changes may be either physiological or pathological. As such determining the cause of volumetric dynamics of the brain is essential. Additionally for an accurate interpretation of longitudinal brain volume measures by means of neurodegeneration, knowledge about the short-term changes is needed. Therefore, in this review, we discuss the possible mechanisms influencing brain volumes on a short-term basis and set-out a framework of MRI techniques to be used for volumetric changes as well as the used analysis tools. 3D T1-weighted images are the images of choice when it comes to MRI of brain volume. These images are excellent to determine brain volume and can be used together with an analysis tool to determine the degree of volume change. Mechanisms that decrease global brain volume are: fluid restriction, evening MRI measurements, corticosteroids, antipsychotics and short-term effects of pathological processes like Alzheimer's disease, hypertension and Diabetes mellitus type II. Mechanisms increasing the brain volume include fluid intake, morning MRI measurements, surgical revascularization and probably medications like anti-inflammatory drugs and anti-hypertensive medication. Exercise was found to have no effect on brain volume on a short-term basis, which may imply that dehydration caused by exercise differs from dehydration by fluid restriction. In the upcoming years, attention should be directed towards studies investigating physiological short-term changes within the light of long-term pathological changes. Ultimately this may lead to a better understanding of the physiological short-term effects of

  7. A capacity expansion model dealing with balancing requirements, short-term operations and long-run dynamics

    Villavicencio, Manuel

    2017-01-01

    One of the challenges of current power systems is presented by the need to adequately integrate increasing shares of variable renewable energies (VRE) such as wind and photovoltaic (PV) technologies. The study of capacity investments under this context raises refreshed interrogations about the optimal power generation mix when considering system adequacy, operability and reliability issues. This paper analyses the influence of such considerations and adopts a resource-adequacy approach to propose a stylized capacity expansion model (CEM) that endogenously optimize investments in both generation capacity and new flexibility options such as electric energy storage (EES) and demand side management (DSM) capabilities. Three formulations are tested in order to seize the relevance of system dynamics representation over the valuation of capacity and flexibility investments. In each formulation the complexity of the representation of operating constraints increases. The resource-adequacy approach is then enlarged with a multi-service representation of the power system introducing non-contingency reserve considerations. Therefore, investments decisions are enhanced by information from system operations requirements given by the hourly economic dispatch and also by a reliability criterion given by reserve needs. The formulations are tested on a case study in order to capture the trade-offs of adding more details on the system representation while exogenously imposing supplementary VRE penetration. The results obtained show the importance of adopting a sufficiently detailed representation of system requirements to accurately capture the value of capacity and flexibility when important VRE penetration levels are to be studied, but also to appropriately estimate resulting system cost and CO_2 emissions. (author)

  8. Short term load forecasting: two stage modelling

    SOARES, L. J.

    2009-06-01

    Full Text Available This paper studies the hourly electricity load demand in the area covered by a utility situated in the Seattle, USA, called Puget Sound Power and Light Company. Our proposal is put into proof with the famous dataset from this company. We propose a stochastic model which employs ANN (Artificial Neural Networks to model short-run dynamics and the dependence among adjacent hours. The model proposed treats each hour's load separately as individual single series. This approach avoids modeling the intricate intra-day pattern (load profile displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is evaluated in similiar mode a TLSAR (Two-Level Seasonal Autoregressive model proposed by Soares (2003 using the years of 1995 and 1996 as the holdout sample. Moreover, we conclude that non linearity is present in some series of these data. The model results are analyzed. The experiment shows that our tool can be used to produce load forecasting in tropical climate places.

  9. Model documentation report: Short-Term Hydroelectric Generation Model

    1993-08-01

    The purpose of this report is to define the objectives of the Short- Term Hydroelectric Generation Model (STHGM), describe its basic approach, and to provide details on the model structure. This report is intended as a reference document for model analysts, users, and the general public. Documentation of the model is in accordance with the Energy Information Administration's (AYE) legal obligation to provide adequate documentation in support of its models (Public Law 94-385, Section 57.b.2). The STHGM performs a short-term (18 to 27- month) forecast of hydroelectric generation in the United States using an autoregressive integrated moving average (UREMIA) time series model with precipitation as an explanatory variable. The model results are used as input for the short-term Energy Outlook

  10. Behavioural Models of Motor Control and Short-Term Memory

    Imanaka, Kuniyasu; Funase, Kozo; Yamauchi, Masaki

    1995-01-01

    We examined in this review article the behavioural and conceptual models of motor control and short-term memory which have intensively been investigated since the 1970s. First, we reviewed both the dual-storage model of short-term memory in which movement information is stored and a typical model of motor control which emphasizes the importance of efferent factors. We then examined two models of preselection effects: a cognitive model and a cognitive/ efferent model. Following this we reviewe...

  11. Emulating short-term synaptic dynamics with memristive devices

    Berdan, Radu; Vasilaki, Eleni; Khiat, Ali; Indiveri, Giacomo; Serb, Alexandru; Prodromakis, Themistoklis

    2016-01-01

    Neuromorphic architectures offer great promise for achieving computation capacities beyond conventional Von Neumann machines. The essential elements for achieving this vision are highly scalable synaptic mimics that do not undermine biological fidelity. Here we demonstrate that single solid-state TiO2 memristors can exhibit non-associative plasticity phenomena observed in biological synapses, supported by their metastable memory state transition properties. We show that, contrary to conventional uses of solid-state memory, the existence of rate-limiting volatility is a key feature for capturing short-term synaptic dynamics. We also show how the temporal dynamics of our prototypes can be exploited to implement spatio-temporal computation, demonstrating the memristors full potential for building biophysically realistic neural processing systems.

  12. A new ensemble model for short term wind power prediction

    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...

  13. A novel TRNSYS type for short-term borehole heat exchanger simulation: B2G model

    De Rosa, Mattia; Ruiz-Calvo, Félix; Corberán, José M.; Montagud, Carla; Tagliafico, Luca A.

    2015-01-01

    Highlights: • A novel dynamic borehole heat exchanger model is presented. • Theoretical approach for model parameters calculation is described. • The short-term model is validated against experimental data of a real GSHP. • Strong dynamic conditions due to the ON–OFF regulation are investigated. - Abstract: Models of ground source heat pump (GSHP) systems are used as an aid for the correct design and optimization of the system. For this purpose, it is necessary to develop models which correctly reproduce the dynamic thermal behavior of each component in a short-term basis. Since the borehole heat exchanger (BHE) is one of the main components, special attention should be paid to ensuring a good accuracy on the prediction of the short-term response of the boreholes. The BHE models found in literature which are suitable for short-term simulations usually present high computational costs. In this work, a novel TRNSYS type implementing a borehole-to-ground (B2G) model, developed for modeling the short-term dynamic performance of a BHE with low computational cost, is presented. The model has been validated against experimental data from a GSHP system located at Universitat Politècnica de València, Spain. Validation results show the ability of the model to reproduce the short-term behavior of the borehole, both for a step-test and under normal operating conditions

  14. Short-term forecasting model for aggregated regional hydropower generation

    Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo

    2014-01-01

    Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations

  15. Short-term mechanisms influencing volumetric brain dynamics

    Dieleman, Nikki; Koek, Huiberdina L.; Hendrikse, Jeroen

    2017-01-01

    With the use of magnetic resonance imaging (MRI) and brain analysis tools, it has become possible to measure brain volume changes up to around 0.5%. Besides long-term brain changes caused by atrophy in aging or neurodegenerative disease, short-term mechanisms that influence brain volume may exist.

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

    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.

  17. Short-Term Memory and Its Biophysical Model

    Wang, Wei; Zhang, Kai; Tang, Xiao-wei

    1996-12-01

    The capacity of short-term memory has been studied using an integrate-and-fire neuronal network model. It is found that the storage of events depend on the manner of the correlation between the events, and the capacity is dominated by the value of after-depolarization potential. There is a monotonic increasing relationship between the value of after-depolarization potential and the memory numbers. The biophysics relevance of the network model is discussed and different kinds of the information processes are studied too.

  18. The IEA Model of Short-term Energy Security

    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.

  19. DYNAMICS OF THE ANXIETY DISORDERS IN THE COURSE OF SHORT-TERM PSYCHOTHERAPY

    T.N. Hmylova

    2008-06-01

    Full Text Available The tendency of psychotherapy modern concepts referring to the short-term forms having been taken into account, we carried out the research aimed at the study of short-term form personality-oriented psychotherapy effect on the anxiety disorder dynamics. 103 patients with neurotic disorders were examined in the neurosis and psychotherapy department of the Bekhterev Psychoneurological Research Institute. The findings revealed the situational and personal anxiety level to be objectively decreased in the short-term group psychotherapy course. The short-term group psychotherapy was proved to bean effective method in anxiety disorders treatment considering indications and limitations.

  20. Stability Analysis on Sparsely Encoded Associative Memory with Short-Term Synaptic Dynamics

    Xu, Muyuan; Katori, Yuichi; Aihara, Kazuyuki

    This study investigates the stability of sparsely encoded associative memory in a network composed of stochastic neurons. The incorporation of short-term synaptic dynamics significantly changes the stability with respect to synaptic properties. Various states including static and oscillatory states are found in the network dynamics. Specifically, the sparseness of memory patterns raises the problem of spurious states. A mean field model is used to analyze the detailed structure in the stability and show that the performance of memory retrieval is recovered by appropriate feedback.

  1. Simple and reliable procedure for the evaluation of short-term dynamic processes in power systems

    Popovic, D P

    1986-10-01

    An efficient approach is presented to the solution of the short-term dynamics model in power systems. It consists of an adequate algebraic treatment of the original system of nonlinear differential equations, using linearization, decomposition and Cauchy's formula. The simple difference equations obtained in this way are incorporated into a model of the electrical network, which is of a low order compared to the ones usually used. Newton's method is applied to the model formed in this way, which leads to a simple and reliable iterative procedure. The characteristics of the procedure developed are demonstrated on examples of transient stability analysis of real power systems. 12 refs.

  2. Short-term generation scheduling model of Fujian hydro system

    Wang Jinwen [School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)], E-mail: dr.jinwen.wang@gmail.com

    2009-04-15

    The Fujian hydropower system (FHS) is one of the provincial hydropower systems with the most complicated hydraulic topology in China. This paper describes an optimization program that is required by Fujian Electric Power Company Ltd. (FEPCL) to aid the shift engineers in making decisions with the short-term hydropower scheduling such that the generation benefit can be maximal. The problem involves 27 reservoirs and is formulated as a nonlinear and discrete programming. It is a very challenging task to solve such a large-scale problem. In this paper, the Lagrangian multipliers are introduced to decompose the primal problem into a hydro subproblem and many individual plant-based subproblems, which are respectively solved by the improved simplex-like method (SLM) and the dynamic programming (DP). A numerical example is given and the derived solution is very close to the optimal one, with the distance in benefit less than 0.004%. All the data needed for the numerical example are presented in detail for further tests and studies from more experts and researchers.

  3. Short-term generation scheduling model of Fujian hydro system

    Wang, Jinwen [School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2009-04-15

    The Fujian hydropower system (FHS) is one of the provincial hydropower systems with the most complicated hydraulic topology in China. This paper describes an optimization program that is required by Fujian Electric Power Company Ltd. (FEPCL) to aid the shift engineers in making decisions with the short-term hydropower scheduling such that the generation benefit can be maximal. The problem involves 27 reservoirs and is formulated as a nonlinear and discrete programming. It is a very challenging task to solve such a large-scale problem. In this paper, the Lagrangian multipliers are introduced to decompose the primal problem into a hydro subproblem and many individual plant-based subproblems, which are respectively solved by the improved simplex-like method (SLM) and the dynamic programming (DP). A numerical example is given and the derived solution is very close to the optimal one, with the distance in benefit less than 0.004%. All the data needed for the numerical example are presented in detail for further tests and studies from more experts and researchers. (author)

  4. Short-Term Power Plant GHG Emissions Forecasting Model

    Vidovic, D.

    2016-01-01

    In 2010, the share of greenhouse gas (GHG) emissions from power generation in the total emissions at the global level was about 25 percent. From January 1st, 2013 Croatian facilities have been involved in the European Union Emissions Trading System (EU ETS). The share of the ETS sector in total GHG emissions in Croatia in 2012 was about 30 percent, where power plants and heat generation facilities contributed to almost 50 percent. Since 2013 power plants are obliged to purchase all emission allowances. The paper describes the short-term climate forecasting model of greenhouse gas emissions from power plants while covering the daily load diagram of the system. Forecasting is done on an hourly domain typically for one day, it is possible and more days ahead. Forecasting GHG emissions in this way would enable power plant operators to purchase additional or sell surplus allowances on the market at the time. Example that describes the operation of the above mentioned forecasting model is given at the end of the paper.(author).

  5. Short-term solar irradiation forecasting based on Dynamic Harmonic Regression

    Trapero, Juan R.; Kourentzes, Nikolaos; Martin, A.

    2015-01-01

    Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and is gaining prominence with the current shift to renewable sources of energy. In order to integrate the electricity generated by solar energy into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1–24 h) solar irradiation forecasting. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithms applied offer adaptive predictions. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves the lowest relative Root Mean Squared Error; about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24 h ahead. - Highlights: • Solar irradiation forecasts at short-term are required to operate solar power plants. • This paper assesses the Dynamic Harmonic Regression to forecast solar irradiation. • Models are evaluated using hourly GHI and DNI data collected in Spain. • The results show that forecasting accuracy is improved by using the model proposed

  6. Rich spectrum of neural field dynamics in the presence of short-term synaptic depression

    Wang, He; Lam, Kin; Fung, C. C. Alan; Wong, K. Y. Michael; Wu, Si

    2015-09-01

    In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred stimuli of the neurons. We investigate how short-term synaptic depression (STD) can reshape the intrinsic dynamics of the CANN model and its responses to a single static input. In particular, CANNs with STD can support various complex firing patterns and chaotic behaviors. These chaotic behaviors have the potential to encode various stimuli in the neuronal system.

  7. Nonlinear Dynamical Modes as a Basis for Short-Term Forecast of Climate Variability

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

    2017-12-01

    We study abilities of data-driven stochastic models constructed by nonlinear dynamical decomposition of spatially distributed data to quantitative (short-term) forecast of climate characteristics. We compare two data processing techniques: (i) widely used empirical orthogonal function approach, and (ii) nonlinear dynamical modes (NDMs) framework [1,2]. We also make comparison of two kinds of the prognostic models: (i) traditional autoregression (linear) model and (ii) model in the form of random ("stochastic") nonlinear dynamical system [3]. We apply all combinations of the above-mentioned data mining techniques and kinds of models to short-term forecasts of climate indices based on sea surface temperature (SST) data. We use NOAA_ERSST_V4 dataset (monthly SST with space resolution 20 × 20) covering the tropical belt and starting from the year 1960. We demonstrate that NDM-based nonlinear model shows better prediction skill versus EOF-based linear and nonlinear models. Finally we discuss capability of NDM-based nonlinear model for long-term (decadal) prediction of climate variability. [1] D. Mukhin, A. Gavrilov, E. Loskutov , A.Feigin, J.Kurths, 2015: Principal nonlinear dynamical modes of climate variability, Scientific Reports, rep. 5, 15510; doi: 10.1038/srep15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J., 2016: Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. [3] Ya. Molkov, D. Mukhin, E. Loskutov, A. Feigin, 2012: Random dynamical models from time series. Phys. Rev. E, Vol. 85, n.3.

  8. Short-term memory in olfactory network dynamics

    Stopfer, Mark; Laurent, Gilles

    1999-01-01

    Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clo...

  9. Short-term memory in olfactory network dynamics

    Stopfer, Mark; Laurent, Gilles

    1999-12-01

    Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas.. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clock. Here we examine, using locusts, the changes in population dynamics of projection-neuron assemblies over repeated odour stimulations, as would occur when an animal first encounters and then repeatedly samples an odour for identification or localization. We find that the responses of these assemblies rapidly decrease in intensity, while they show a marked increase in spike time precision and inter-neuronal oscillatory coherence. Once established, this enhanced precision in the representation endures for several minutes. This change is stimulus-specific, and depends on events within the antennal lobe circuits, independent of olfactory receptor adaptation: it may thus constitute a form of sensory memory. Our results suggest that this progressive change in olfactory network dynamics serves to converge, over repeated odour samplings, on a more precise and readily classifiable odour representation, using relational information contained across neural assemblies.

  10. How long-term dynamics of sediment subduction controls short-term dynamics of seismicity

    Brizzi, S.; van Zelst, I.; van Dinther, Y.; Funiciello, F.; Corbi, F.

    2017-12-01

    Most of the world's greatest earthquakes occur along the subduction megathrust. Weak and porous sediments have been suggested to homogenize the plate interface and thereby promote lateral rupture propagation and great earthquakes. However, the importance of sediment thickness, let alone their physical role, is not yet unequivocally established. Based on a multivariate statistical analysis of a global database of 62 subduction segments, we confirm that sediment thickness is one of the key parameters controlling the maximum magnitude a megathrust can generate. Moreover, Monte Carlo simulations highlighted that the occurrence of great earthquakes on sediment-rich subduction segments is very unlikely (p-value≪0.05) related to pure chance. To understand how sediments in the subduction channel regulate earthquake size, this study extends and demystifies multivariate, spatiotemporally limited data through numerical modeling. We use the 2D Seismo-Thermo-Mechanical modeling approach to simulate both the long- and short-term dynamics of subduction and related seismogenesis (van Dinther et al., JGR, 2013). These models solve for the conservation of mass, momentum and energy using a visco-elasto-plastic rheology with rate-dependent friction. Results show that subducted sediments have a strong influence on the long-term evolution of the convergent margin. Increasing the sediment thickness on the incoming plate from 0 to 6 km causes a decrease of slab dip from 23° to 10°. This, in addition to increased radiogenic heating, extends isotherms, thereby widening the seismogenic portion of the megathrust from 80 to 150 km. Consequently, over tens of thousands of years, we observe that the maximum moment magnitude of megathrust earthquakes increases from 8.2 to 9.2 for these shallower and warmer interfaces. In addition, we observe more and larger splay faults, which could enhance vertical seafloor displacements. These results highlight the primary role of subducted sediments in

  11. Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

    Mingfei Niu; Shaolong Sun; Jie Wu; Yuanlei Zhang

    2015-01-01

    The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias c...

  12. Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (the POUNDS Lost study).

    Thomas, Diana M; Ivanescu, Andrada E; Martin, Corby K; Heymsfield, Steven B; Marshall, Kaitlyn; Bodrato, Victoria E; Williamson, Donald A; Anton, Stephen D; Sacks, Frank M; Ryan, Donna; Bray, George A

    2015-03-01

    Currently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds. The objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1. Data consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing model. The AUC statistic quantified the ROC curve's capacity to classify participants likely to lose models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated. Optimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50). The newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention. © 2015 American Society for Nutrition.

  13. Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (the POUNDS Lost study)123

    Ivanescu, Andrada E; Martin, Corby K; Heymsfield, Steven B; Marshall, Kaitlyn; Bodrato, Victoria E; Williamson, Donald A; Anton, Stephen D; Sacks, Frank M; Ryan, Donna; Bray, George A

    2015-01-01

    Background: Currently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds. Objective: The objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1. Design: Data consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing <5% of body weight in 1 y. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds were calculated for each model. The AUC statistic quantified the ROC curve’s capacity to classify participants likely to lose <5% of their body weight at the end of 1 y. The models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated. Results: Optimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50). Conclusions: The newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention. The POUNDS Lost study was registered at clinicaltrials.gov as NCT00072995. PMID:25733628

  14. A fuzzy inference model for short-term load forecasting

    Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad

    2009-01-01

    This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes

  15. Factors in Outcomes of Short-Term Dynamic Psychotherapy for Chronic vs. Nonchronic Major Depression

    LUBORSKY, LESTER; DIGUER, LOUIS; CACCIOLA, JOHN; BARBER, JACQUES P.; MORAS, KARLA; SCHMIDT, KELLY; DERUBEIS, ROBERT J.

    1996-01-01

    The benefits, and variables influencing the benefits, of short-term dynamic psychotherapy for chronic major depression versus nonchronic major depression were examined for 49 patients. The two diagnostic groups started at the same level on the Beck Depression Inventory (BDI) and Global Assessment of Functioning Scale (GAF) and benefited similarly. The bases for the benefits were examined by linear models explaining 35% of termination BDI variance and 47% of termination GAF scores. By far the largest contributor to outcome was initial GAF, followed by presence of more than one comorbid Axis I diagnosis. Initial level of depression on the BDI was not a significant predictor of termination BDI. The chronic/ nonchronic distinction accounted for less than 1% of explained variance, and little was added by personality disorder, age, or gender. PMID:22700274

  16. Simulating the Effects of Short-Term Synaptic Plasticity on Postsynaptic Dynamics in the Globus Pallidus

    Moran eBrody

    2013-08-01

    Full Text Available The rat globus pallidus (GP is one of the nuclei of the basal ganglia and plays an important role in a variety of motor and cognitive processes. In vivo studies have shown that repetitive stimulation evokes complex modulations of GP activity. In vitro and computational studies have suggested that short-term synaptic plasticity (STP could be one of the underlying mechanisms. The current study used simplified single compartment modeling to explore the possible effect of STP on the activity of GP neurons during low and high frequency stimulation. To do this we constructed a model of a GP neuron connected to a small network of neurons from the three major input sources to GP neurons: striatum (Str, subthalamic nucleus (STN and GP collaterals. All synapses were implemented with a kinetic model of STP. The in vitro recordings of responses to low frequency repetitive stimulation were highly reconstructed, including rate changes and locking to the stimulus. Mainly involved were fast forms of plasticity which have been found at these synapses. . The simulations were qualitatively compared to a data set previously recorded in vitro in our lab. Reconstructions of experimental responses to high frequency stimulation required adding slower forms of plasticity to the STN and GP collateral synapses, as well as adding metabotropic receptors to the STN-GP synapses. These finding suggest the existence of as yet unreported slower short-term dynamics in the GP. The computational model made additional predictions about GP activity during low and high frequency stimulation that may further our understanding of the mechanisms underlying repetative stimulation of the GP.

  17. Short-term dynamics of second-growth mixed mesophytic forest strata in West Virginia

    Cynthia C. Huebner; Steven L. Stephenson; Harold S. Adams; Gary W. Miller

    2007-01-01

    The short-term dynamics of mixed mesophytic forest strata in West Virginia were examined using similarity analysis and linear correlation of shared ordination space. The overstory tree, understory tree, shrub/vine, and herb strata were stable over a six year interval, whereas the tree seedling and sapling strata were unstable. All strata but the shrub/vine and tree...

  18. Dynamical prediction and pattern mapping in short-term load forecasting

    Aguirre, Luis Antonio; Rodrigues, Daniela D.; Lima, Silvio T. [Departamento de Engenharia Eletronica, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil); Martinez, Carlos Barreira [Departamento de Engenharia Hidraulica e Recursos Hidricos, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil)

    2008-01-15

    This work will not put forward yet another scheme for short-term load forecasting but rather will provide evidences that may improve our understanding about fundamental issues which underlay load forecasting problems. In particular, load forecasting will be decomposed into two main problems, namely dynamical prediction and pattern mapping. It is argued that whereas the latter is essentially static and becomes nonlinear when weekly features in the data are taken into account, the former might not be deterministic at all. In such cases there is no determinism (serial correlations) in the data apart from the average cycle and the best a model can do is to perform pattern mapping. Moreover, when there is determinism in addition to the average cycle, the underlying dynamics are sometimes linear, in which case there is no need to resort to nonlinear models to perform dynamical prediction. Such conclusions were confirmed using real load data and surrogate data analysis. In a sense, the paper details and organizes some general beliefs found in the literature on load forecasting. This sheds some light on real model-building and forecasting problems and helps understand some apparently conflicting results reported in the literature. (author)

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

    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.…

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

    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.

  1. Ordered short-term memory differs in signers and speakers: Implications for models of short-term memory

    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, Supalla, Newport, & Bavelier, 2004). Here, we test the hypothesis that this population difference reflects differences in the way speakers and signers maintain temporal order information in short-te...

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

    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

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

    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

  4. Modelling the short term herding behaviour of stock markets

    Shapira, Yoash; Berman, Yonatan; Ben-Jacob, Eshel

    2014-01-01

    Modelling the behaviour of stock markets has been of major interest in the past century. The market can be treated as a network of many investors reacting in accordance to their group behaviour, as manifested by the index and effected by the flow of external information into the system. Here we devise a model that encapsulates the behaviour of stock markets. The model consists of two terms, demonstrating quantitatively the effect of the individual tendency to follow the group and the effect of the individual reaction to the available information. Using the above factors we were able to explain several key features of the stock market: the high correlations between the individual stocks and the index; the Epps effect; the high fluctuating nature of the market, which is similar to real market behaviour. Furthermore, intricate long term phenomena are also described by this model, such as bursts of synchronized average correlation and the dominance of the index as demonstrated through partial correlation. (paper)

  5. Error analysis of short term wind power prediction models

    De Giorgi, Maria Grazia; Ficarella, Antonio; Tarantino, Marco

    2011-01-01

    The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction. (author)

  6. Error analysis of short term wind power prediction models

    De Giorgi, Maria Grazia; Ficarella, Antonio; Tarantino, Marco [Dipartimento di Ingegneria dell' Innovazione, Universita del Salento, Via per Monteroni, 73100 Lecce (Italy)

    2011-04-15

    The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction. (author)

  7. A Probabilistic Palimpsest Model of Visual Short-term Memory

    Matthey, Loic; Bays, Paul M.; Dayan, Peter

    2015-01-01

    Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ. PMID:25611204

  8. Limitless capacity: A dynamic object-oriented approach to short-term memory

    Bill eMacken

    2015-03-01

    Full Text Available The notion of capacity-limited processing systems is a core element of cognitive accounts of limited and variable performance, enshrined within the short-term memory construct. We begin with a detailed critical analysis of the conceptual bases of this view and argue that there are fundamental problems – ones that go to the heart of cognitivism more generally – that render it untenable. In place of limited capacity systems, we propose a framework for explaining performance that focuses on the dynamic interplay of three aspects of any given setting: the particular task that must be accomplished, the nature and form of the material upon which the task must be performed, and the repertoire of skills and perceptual-motor functions possessed by the participant. We provide empirical examples of the applications of this framework in areas of performance typically accounted for by reference to capacity-limited short-term memory processes.

  9. Limitless capacity: a dynamic object-oriented approach to short-term memory.

    Macken, Bill; Taylor, John; Jones, Dylan

    2015-01-01

    The notion of capacity-limited processing systems is a core element of cognitive accounts of limited and variable performance, enshrined within the short-term memory construct. We begin with a detailed critical analysis of the conceptual bases of this view and argue that there are fundamental problems - ones that go to the heart of cognitivism more generally - that render it untenable. In place of limited capacity systems, we propose a framework for explaining performance that focuses on the dynamic interplay of three aspects of any given setting: the particular task that must be accomplished, the nature and form of the material upon which the task must be performed, and the repertoire of skills and perceptual-motor functions possessed by the participant. We provide empirical examples of the applications of this framework in areas of performance typically accounted for by reference to capacity-limited short-term memory processes.

  10. Dynamic visual noise reduces confidence in short-term memory for visual information.

    Kemps, Eva; Andrade, Jackie

    2012-05-01

    Previous research has shown effects of the visual interference technique, dynamic visual noise (DVN), on visual imagery, but not on visual short-term memory, unless retention of precise visual detail is required. This study tested the prediction that DVN does also affect retention of gross visual information, specifically by reducing confidence. Participants performed a matrix pattern memory task with three retention interval interference conditions (DVN, static visual noise and no interference control) that varied from trial to trial. At recall, participants indicated whether or not they were sure of their responses. As in previous research, DVN did not impair recall accuracy or latency on the task, but it did reduce recall confidence relative to static visual noise and no interference. We conclude that DVN does distort visual representations in short-term memory, but standard coarse-grained recall measures are insensitive to these distortions.

  11. Patient affect experiencing following therapist interventions in short-term dynamic psychotherapy.

    Town, Joel M; Hardy, Gillian E; McCullough, Leigh; Stride, Chris

    2012-01-01

    The aim of this research was to examine the relationship between therapist interventions and patient affect responses in Short-Term Dynamic Psychotherapy (STDP). The Affect Experiencing subscale from the Achievement of Therapeutic Objectives Scale (ATOS) was adapted to measure individual immediate affect experiencing (I-AES) responses in relation to therapist interventions coded within the preceding speaking turn, using the Psychotherapy Interaction Coding (PIC) system. A hierarchical linear modelling procedure was used to assess the change in affect experiencing and the relationship between affect experiencing and therapist interventions within and across segments of therapy. Process data was taken from six STDP cases; in total 24 hours of video-taped sessions were examined. Therapist interventions were found to account for a statistically significant amount of variance in immediate affect experiencing. Higher levels of immediate affect experiencing followed the therapist's use of Confrontation, Clarification and Support compared to Questions, Self-disclosure and Information interventions. Therapist Confrontation interventions that attempted to direct pressure towards either the visceral experience of affect or a patient's defences against feelings led to the highest levels of immediate affect experiencing. The type of therapist intervention accounts for a small but significant amount of the variation observed in a patient's immediate emotional arousal. Empirical findings support clinical theory in STDP that suggests strategic verbal responses promote the achievement of this specific therapeutic objective.

  12. Exploiting short-term memory in soft body dynamics as a computational resource.

    Nakajima, K; Li, T; Hauser, H; Pfeifer, R

    2014-11-06

    Soft materials are not only highly deformable, but they also possess rich and diverse body dynamics. Soft body dynamics exhibit a variety of properties, including nonlinearity, elasticity and potentially infinitely many degrees of freedom. Here, we demonstrate that such soft body dynamics can be employed to conduct certain types of computation. Using body dynamics generated from a soft silicone arm, we show that they can be exploited to emulate functions that require memory and to embed robust closed-loop control into the arm. Our results suggest that soft body dynamics have a short-term memory and can serve as a computational resource. This finding paves the way towards exploiting passive body dynamics for control of a large class of underactuated systems. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  13. Remembering over the short-term: the case against the standard model.

    Nairne, James S

    2002-01-01

    Psychologists often assume that short-term storage is synonymous with activation, a mnemonic property that keeps information in an immediately accessible form. Permanent knowledge is activated, as a result of on-line cognitive processing, and an activity trace is established "in" short-term (or working) memory. Activation is assumed to decay spontaneously with the passage of time, so a refreshing process-rehearsal-is needed to maintain availability. Most of the phenomena of immediate retention, such as capacity limitations and word length effects, are assumed to arise from trade-offs between rehearsal and decay. This "standard model" of how we remember over the short-term still enjoys considerable popularity, although recent research questions most of its main assumptions. In this chapter I review the recent research and identify the empirical and conceptual problems that plague traditional conceptions of short-term memory. Increasingly, researchers are recognizing that short-term retention is cue driven, much like long-term memory, and that neither rehearsal nor decay is likely to explain the particulars of short-term forgetting.

  14. Aspects if stochastic models for short-term hydropower scheduling and bidding

    Belsnes, Michael Martin [Sintef Energy, Trondheim (Norway); Follestad, Turid [Sintef Energy, Trondheim (Norway); Wolfgang, Ove [Sintef Energy, Trondheim (Norway); Fosso, Olav B. [Dep. of electric power engineering NTNU, Trondheim (Norway)

    2012-07-01

    This report discusses challenges met when turning from deterministic to stochastic decision support models for short-term hydropower scheduling and bidding. The report describes characteristics of the short-term scheduling and bidding problem, different market and bidding strategies, and how a stochastic optimization model can be formulated. A review of approaches for stochastic short-term modelling and stochastic modelling for the input variables inflow and market prices is given. The report discusses methods for approximating the predictive distribution of uncertain variables by scenario trees. Benefits of using a stochastic over a deterministic model are illustrated by a case study, where increased profit is obtained to a varying degree depending on the reservoir filling and price structure. Finally, an approach for assessing the effect of using a size restricted scenario tree to approximate the predictive distribution for stochastic input variables is described. The report is a summary of the findings of Work package 1 of the research project #Left Double Quotation Mark#Optimal short-term scheduling of wind and hydro resources#Right Double Quotation Mark#. The project aims at developing a prototype for an operational stochastic short-term scheduling model. Based on the investigations summarized in the report, it is concluded that using a deterministic equivalent formulation of the stochastic optimization problem is convenient and sufficient for obtaining a working prototype. (author)

  15. EDM - A model for optimising the short-term power operation of a complex hydroelectric network

    Tremblay, M.; Guillaud, C.

    1996-01-01

    In order to optimize the short-term power operation of a complex hydroelectric network, a new model called EDM was added to PROSPER, a water management analysis system developed by SNC-Lavalin. PROSPER is now divided into three parts: an optimization model (DDDP), a simulation model (ESOLIN), and an economic dispatch model (EDM) for the short-term operation. The operation of the KSEB hydroelectric system (located in southern India) with PROSPER was described. The long-term analysis with monthly time steps is assisted by the DDDP, and the daily analysis with hourly or half-hourly time steps is performed with the EDM model. 3 figs

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

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

    2014-01-01

    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. - Highlights: • We take the Fick diffusion and the Soret diffusion into account in the ion drift theory. • We develop a new model based on the old HP model. • The new model can describe the forgetting effect and the spike-rate-dependent property of memristor. • The new model can solve the boundary effect of all window functions discussed in [13]. • A new Hopfield neural network with the forgetting ability is built by the new memristor model

  17. Development of a short-term model to predict natural gas demand, March 1989

    Lihn, M.L.

    1989-03-01

    Project management decisions for the Gas Research Institute (GRI) R and D program require an appreciation of the short-term outlook for gas consumption. This paper provides a detailed discussion of the methodology used to develop short-term models for the residential, commercial, industrial, and electric utility sectors. The relative success of the models in projecting gas demand, compared with actual gas demand, is reviewed for each major gas-consuming sector. The comparison of actual to projected gas demand has pointed out several problems with the model, and possible solutions to these problems are discussed

  18. Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

    Lei, Yu; Guo, Min; Zhao, Danning; Cai, Hongbing; Hu, Dandan

    2016-03-01

    A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

  19. Task set induces dynamic reallocation of resources in visual short-term memory.

    Sheremata, Summer L; Shomstein, Sarah

    2017-08-01

    Successful interaction with the environment requires the ability to flexibly allocate resources to different locations in the visual field. Recent evidence suggests that visual short-term memory (VSTM) resources are distributed asymmetrically across the visual field based upon task demands. Here, we propose that context, rather than the stimulus itself, determines asymmetrical distribution of VSTM resources. To test whether context modulates the reallocation of resources to the right visual field, task set, defined by memory-load, was manipulated to influence visual short-term memory performance. Performance was measured for single-feature objects embedded within predominantly single- or two-feature memory blocks. Therefore, context was varied to determine whether task set directly predicts changes in visual field biases. In accord with the dynamic reallocation of resources hypothesis, task set, rather than aspects of the physical stimulus, drove improvements in performance in the right- visual field. Our results show, for the first time, that preparation for upcoming memory demands directly determines how resources are allocated across the visual field.

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

    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...

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

    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.

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

    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…

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

    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…

  4. Modelling the short-term response of the Greenland ice-sheet to global warming

    Wal, R.S.W. van de; Oerlemans, J.

    1997-01-01

    A two-dimensional vertically integrated ice flow model has been developed to test the importance of various processes and concepts used for the prediction of the contribution of the Greenland ice-sheet to sea-level rise over the next 350 y (short-term response). The mass balance is modelled by the

  5. Short-term wind power prediction based on LSSVM–GSA model

    Yuan, Xiaohui; Chen, Chen; Yuan, Yanbin; Huang, Yuehua; Tan, Qingxiong

    2015-01-01

    Highlights: • A hybrid model is developed for short-term wind power prediction. • The model is based on LSSVM and gravitational search algorithm. • Gravitational search algorithm is used to optimize parameters of LSSVM. • Effect of different kernel function of LSSVM on wind power prediction is discussed. • Comparative studies show that prediction accuracy of wind power is improved. - Abstract: Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction

  6. Short-term memory in Down syndrome: applying the working memory model.

    Jarrold, C; Baddeley, A D

    2001-10-01

    This paper is divided into three sections. The first reviews the evidence for a verbal short-term memory deficit in Down syndrome. Existing research suggests that short-term memory for verbal information tends to be impaired in Down syndrome, in contrast to short-term memory for visual and spatial material. In addition, problems of hearing or speech do not appear to be a major cause of difficulties on tests of verbal short-term memory. This suggests that Down syndrome is associated with a specific memory problem, which we link to a potential deficit in the functioning of the 'phonological loop' of Baddeley's (1986) model of working memory. The second section considers the implications of a phonological loop problem. Because a reasonable amount is known about the normal functioning of the phonological loop, and of its role in language acquisition in typical development, we can make firm predictions as to the likely nature of the short-term memory problem in Down syndrome, and its consequences for language learning. However, we note that the existing evidence from studies with individuals with Down syndrome does not fit well with these predictions. This leads to the third section of the paper, in which we consider key questions to be addressed in future research. We suggest that there are two questions to be answered, which follow directly from the contradictory results outlined in the previous section. These are 'What is the precise nature of the verbal short-term memory deficit in Down syndrome', and 'What are the consequences of this deficit for learning'. We discuss ways in which these questions might be addressed in future work.

  7. Statistical mechanics of neocortical interactions: Constraints on 40-Hz models of short-term memory

    Ingber, Lester

    1995-10-01

    Calculations presented in L. Ingber and P.L. Nunez, Phys. Rev. E 51, 5074 (1995) detailed the evolution of short-term memory in the neocortex, supporting the empirical 7+/-2 rule of constraints on the capacity of neocortical processing. These results are given further support when other recent models of 40-Hz subcycles of low-frequency oscillations are considered.

  8. Computer simulation of yielding supports under static and short-term dynamic load

    Kumpyak Oleg

    2018-01-01

    Full Text Available Dynamic impacts that became frequent lately cause large human and economic losses, and their prevention methods are not always effective and reasonable. The given research aims at studying the way of enhancing explosion safety of building structures by means of yielding supports. The paper presents results of numerical studies of strength and deformation property of yielding supports in the shape of annular tubes under static and short-term dynamic loading. The degree of influence of yielding supports was assessed taking into account three peculiar stages of deformation: elastic; elasto-plastic; and elasto-plastic with hardening. The methodology for numerical studies performance was described using finite element analysis with program software Ansys Mechanical v17.2. It was established that rigidity of yielding supports influences significantly their stress-strain state. The research determined that with the increase in deformable elements rigidity dependence between load and deformation of the support in elastic and plastic stages have linear character. Significant reduction of the dynamic response and increase in deformation time of yielding supports were observed due to increasing the plastic component. Therefore, it allows assuming on possibility of their application as supporting units in RC beams.

  9. Short-term exposure to a synthetic estrogen disrupts mating dynamics in a pipefish.

    Partridge, Charlyn; Boettcher, Anne; Jones, Adam G

    2010-11-01

    Sexual selection is responsible for the evolution of some of the most elaborate traits occurring in nature, many of which play a vital role in competition over access to mates and individual reproductive fitness. Because expression of these traits is typically regulated by sex-steroids there is a significant potential for their expression to be affected by the presence of certain pollutants, such as endocrine disrupting compounds. Endocrine disruptors have been shown to alter primary sexual traits and impact reproduction, but few studies have investigated how these compounds affect secondary sexual trait expression and how that may, in turn, impact mating dynamics. In this study we examine how short-term exposure to a synthetic estrogen impacts secondary sexual trait expression and mating dynamics in the Gulf pipefish, a species displaying sex-role reversal. Our results show that only 10days of exposure to 17α-ethinylestradiol results in adult male pipefish developing female-like secondary sexual traits. While these males are capable of reproduction, females discriminate against exposed males in mate choice trials. In natural populations, this type of discrimination would reduce male mating opportunities, thus potentially reducing their long-term reproductive success. Importantly, the effects of these compounds on mating dynamics and mating opportunity would not be observed using the current standard methods of assessing environmental contamination. However, disrupting these processes could have profound effects on the viability of exposed populations. Copyright © 2010 Elsevier Inc. All rights reserved.

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

    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.

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

    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...... 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....

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

    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.

  13. Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

    Rui Xue

    2015-01-01

    Full Text Available Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

  14. Short-term wavelike dynamics of bacterial populations in response to nutrient input from fresh plant residues

    Zelenev, V.V.; Bruggen, van A.H.C.; Semenov, A.M.

    2005-01-01

    The objectives of the research were to investigate short-term dynamics of bacterial populations in soil after a disturbance in the form of fresh organic matter incorporation and to investigate how these dynamics are linked to those of some environmental parameters. To reach these objectives, soil

  15. Constraints on Dynamic Triggering from very Short term Microearthquake Aftershocks at Parkfield

    Ampuero, J.; Rubin, A.

    2004-12-01

    The study of microearthquakes helps bridge the gap between laboratory experiments and data from large earthquakes, the two disparate scales that have contributed so far to our understanding of earthquake physics. Although they are frequent, microearthquakes are difficult to analyse. Applying high precision relocation techniques, Rubin and Gillard (2000) observed a pronounced asymmetry in the spatial distribution of the earliest and nearest aftershocks of microearthquakes along the San Andreas fault (they occur more often to the NW of the mainshock). It was suggested that this could be related to the velocity contrast across the fault. Preferred directivity of dynamic rupture pulses running along a bimaterial interface (to the SE in the case of the SAF) is expected on theoretical grounds. Our numerical simulations of crack-like rupture on such interfaces show a pronounced asymmetry of the stress histories beyond the rupture ends, and suggest two possible mechanisms for the observed asymmetry: First, that it results from an asymmmetry in the static stress field following arrest of the mainshock (closer to failure to the NW), or second, that it is due to a short-duration tensile pulse that propagates to the SE, which could reduce the number of aftershocks to the SE by dynamic triggering of any nucleation site close enough to failure to have otherwise produced an aftershock. To distinguish betwen these mechanisms we need observations of dynamic triggering in microseismicity. For small events triggered at a distance of some mainshock radii, triggering time scales are so short that seismograms of both events overlap. To detect the occurrence of compound events and very short term aftershocks in the HRSN Parkfield archived waveforms we have developed an automated search algorithm based on empirical Green's function (EGF) deconvolution. Optimal EGFs are first selected by the coherency of the cross-component convolution with respect to the target event. Then Landweber

  16. Neural dynamics underlying attentional orienting to auditory representations in short-term memory.

    Backer, Kristina C; Binns, Malcolm A; Alain, Claude

    2015-01-21

    Sounds are ephemeral. Thus, coherent auditory perception depends on "hearing" back in time: retrospectively attending that which was lost externally but preserved in short-term memory (STM). Current theories of auditory attention assume that sound features are integrated into a perceptual object, that multiple objects can coexist in STM, and that attention can be deployed to an object in STM. Recording electroencephalography from humans, we tested these assumptions, elucidating feature-general and feature-specific neural correlates of auditory attention to STM. Alpha/beta oscillations and frontal and posterior event-related potentials indexed feature-general top-down attentional control to one of several coexisting auditory representations in STM. Particularly, task performance during attentional orienting was correlated with alpha/low-beta desynchronization (i.e., power suppression). However, attention to one feature could occur without simultaneous processing of the second feature of the representation. Therefore, auditory attention to memory relies on both feature-specific and feature-general neural dynamics. Copyright © 2015 the authors 0270-6474/15/351307-12$15.00/0.

  17. Modelling the short term interest with stochastic differential equation in continuous time: linear versus non-linear mode

    2014-01-01

    M.Com. (Financial Economics) Recently, there has been a growth in the bond market. This growth has brought with it an ever-increasing volume and range of interest rate depended derivative products known as interest rate derivatives. Amongst the variables used in pricing these derivative products is the short-term interest rate. A numbers of short-term interest rate models that are used to fit the short-term interest rate exist. Therefore, understanding the features characterised by various...

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

    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.

  19. Short-term Wind Forecasting at Wind Farms using WRF-LES and Actuator Disk Model

    Kirkil, Gokhan

    2017-04-01

    Short-term wind forecasts are obtained for a wind farm on a mountainous terrain using WRF-LES. Multi-scale simulations are also performed using different PBL parameterizations. Turbines are parameterized using Actuator Disc Model. LES models improved the forecasts. Statistical error analysis is performed and ramp events are analyzed. Complex topography of the study area affects model performance, especially the accuracy of wind forecasts were poor for cross valley-mountain flows. By means of LES, we gain new knowledge about the sources of spatial and temporal variability of wind fluctuations such as the configuration of wind turbines.

  20. Short term outcome of posterior dynamic stabilization system in degenerative lumbar diseases.

    Yang, Mingyuan; Li, Chao; Chen, Ziqiang; Bai, Yushu; Li, Ming

    2014-11-01

    Decompression and fusion is considered as the 'gold standard' for the treatment of degenerative lumbar diseases, however, many disadvantages have been reported in several studies, recently like donor site pain, pseudoarthrosis, nonunion, screw loosening, instrumentation failure, infection, adjacent segment disease (ASDis) and degeneration. Dynamic neutralization system (Dynesys) avoids many of these disadvantages. This system is made up of pedicle screws, polyethylene terephthalate cords, and polycarbonate urethane spacers to stabilize the functional spinal unit and preserve the adjacent motion after surgeries. This was a retrospective cohort study to compare the effect of Dynesys for treating degenerative lumbar diseases with posterior lumbar interbody fusion (PLIF) based on short term followup. Seventy five consecutive patients of lumbar degenerative disease operated between October 2010 and November 2012 were studied with a minimum followup of 2 years. Patients were divided into two groups according to the different surgeries. 30 patients underwent decompression and implantation of Dynesys in two levels (n = 29) or three levels (n = 1) and 45 patients underwent PLIF in two levels (n = 39) or three levels (n = 6). Clinical and radiographic outcomes between two groups were reviewed. Thirty patients (male:17, female:13) with a mean age of 55.96 ± 7.68 years were included in Dynesys group and the PLIF group included 45 patients (male:21, female:24) with a mean age of 54.69 ± 3.26 years. The average followup in Dynesys group and PLIF group was 2.22 ± 0.43 year (range 2-3.5 year) and 2.17 ± 0.76 year (range 2-3 year), respectively. Dynesys group showed a shorter operation time (141.06 ± 11.36 min vs. 176.98 ± 6.72 min, P degenerative disease showed clinical benefits with motion preservation of the operated segments, but does not have the significant advantage on motion preservation at adjacent segments, to avoid the degeneration of adjacent intervertebral disk.

  1. Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

    Aiqing Kang

    2017-01-01

    Full Text Available Hybrid Ensemble Empirical Mode Decomposition (EEMD and Least Square Support Vector Machine (LSSVM is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP, Auto-Regressive Integrated Moving Average (ARIMA, combination of Empirical Mode Decomposition (EMD with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

  2. Role of short-term dispersal on the dynamics of Zika virus in an extreme idealized environment

    Victor M. Moreno

    2017-02-01

    Full Text Available In November 2015, El Salvador reported their first case of Zika virus (ZIKV infection, an event followed by an explosive outbreak that generated over 6000 suspected cases in a period of two months. National agencies began implementing control measures that included vector control and recommending an increased use of repellents. Further, in response to the alarming and growing number of microcephaly cases in Brazil, the importance of avoiding pregnancies for two years was stressed. In this paper, we explore the role of mobility within communities characterized by extreme poverty, crime and violence. Specifically, the role of short term mobility between two idealized interconnected highly distinct communities is explored in the context of ZIKV outbreaks. We make use of a Lagrangian modeling approach within a two-patch setting in order to highlight the possible effects that short-term mobility, within highly distinct environments, may have on the dynamics of ZIKV outbreak when the overall goal is to reduce the number of cases not just in the most affluent areas but everywhere. Outcomes depend on existing mobility patterns, levels of disease risk, and the ability of federal or state public health services to invest in resource limited areas, particularly in those where violence is systemic. The results of simulations in highly polarized and simplified scenarios are used to assess the role of mobility. It quickly became evident that matching observed patterns of ZIKV outbreaks could not be captured without incorporating increasing levels of heterogeneity. The number of distinct patches and variations on patch connectivity structure required to match ZIKV patterns could not be met within the highly aggregated model that is used in the simulations. Keywords: Vector-borne diseases, Zika virus, Residence times, Multi-patch model

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

    Elsner, James B; Jagger, Thomas H; Fricker, Tyler

    2016-01-01

    This paper estimates regional 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 a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public.

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

    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.

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

    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.

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

    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.

  7. Kinetic modeling of the purging of activated carbon after short term methyl iodide loading

    Friedrich, V.; Lux, I.

    1991-01-01

    A bimolecular reaction model containing the physico-chemical parameters of the adsorption and desorption was developed earlier to describe the kinetics of methyl iodide retention by activated carbon adsorber. Both theoretical model and experimental investigations postulated constant upstream methyl iodide concentration till the maximum break-through. The work reported here includes the extension of the theoretical model to the general case when the concentration of the challenging gas may change in time. The effect of short term loading followed by purging with air, and an impulse-like increase in upstream gas concentration has been simulated. The case of short term loading and subsequent purging has been experimentally studied to validate the model. The investigations were carried out on non-impregnated activated carbon. A 4 cm deep carbon bed had been challenged by methyl iodide for 30, 90, 120 and 180 min and then purged with air, downstream methyl iodide concentration had been measured continuously. The main characteristics of the observed downstream concentration curves (time and slope of break-through, time and amplitude of maximum values) showed acceptable agreement with those predicted by the model

  8. Time Perspective and Identity Formation: Short-Term Longitudinal Dynamics in College Students

    Luyckx, Koen; Lens, Willy; Smits, Ilse; Goossens, Luc

    2010-01-01

    Planning for the future and developing a personalized identity are conceived of as important developmental tasks that adolescents and emerging adults are confronted with on the pathway to adulthood. The present study set out to examine whether both tasks develop in tandem by using a short-term longitudinal dataset consisting of 371 college…

  9. LTS and FS inhibitory interneurons, short-term synaptic plasticity, and cortical circuit dynamics.

    Itai Hayut

    2011-10-01

    Full Text Available Somatostatin-expressing, low threshold-spiking (LTS cells and fast-spiking (FS cells are two common subtypes of inhibitory neocortical interneuron. Excitatory synapses from regular-spiking (RS pyramidal neurons to LTS cells strongly facilitate when activated repetitively, whereas RS-to-FS synapses depress. This suggests that LTS neurons may be especially relevant at high rate regimes and protect cortical circuits against over-excitation and seizures. However, the inhibitory synapses from LTS cells usually depress, which may reduce their effectiveness at high rates. We ask: by which mechanisms and at what firing rates do LTS neurons control the activity of cortical circuits responding to thalamic input, and how is control by LTS neurons different from that of FS neurons? We study rate models of circuits that include RS cells and LTS and FS inhibitory cells with short-term synaptic plasticity. LTS neurons shift the RS firing-rate vs. current curve to the right at high rates and reduce its slope at low rates; the LTS effect is delayed and prolonged. FS neurons always shift the curve to the right and affect RS firing transiently. In an RS-LTS-FS network, FS neurons reach a quiescent state if they receive weak input, LTS neurons are quiescent if RS neurons receive weak input, and both FS and RS populations are active if they both receive large inputs. In general, FS neurons tend to follow the spiking of RS neurons much more closely than LTS neurons. A novel type of facilitation-induced slow oscillations is observed above the LTS firing threshold with a frequency determined by the time scale of recovery from facilitation. To conclude, contrary to earlier proposals, LTS neurons affect the transient and steady state responses of cortical circuits over a range of firing rates, not only during the high rate regime; LTS neurons protect against over-activation about as well as FS neurons.

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

    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.

  11. Short-term electric power demand forecasting based on economic-electricity transmission model

    Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

    2018-04-01

    Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

  12. Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model

    Herui Cui

    2015-01-01

    Full Text Available Short-term electric load is significantly affected by weather, especially the temperature effects in summer. External factors can result in mutation structures in load data. Under the influence of the external temperature factors, city electric load cannot be easily forecasted as usual. This research analyzes the relationship between electricity load and daily temperature in city. An improved ARIMAX model is proposed in this paper to deal with the mutation data structures. It is found that information amount of the improved ARIMAX model is smaller than that of the classic method and its relative error is less than AR, ARMA and Sigmoid-Function ANN models. The forecasting results are more accurately fitted. This improved model is highly valuable when dealing with mutation data structure in the field of load forecasting. And it is also an effective technique in forecasting electric load with temperature effects.

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

    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.

  14. [Economic Short-Term Cost Model for Stereotactic Radiotherapy of Neovascular AMD].

    Neubauer, A S; Reznicek, L; Minartz, C; Ziemssen, F

    2016-08-01

    Stereotactic radiation therapy (Oraya, OT) is available as a second line therapy for patients who, despite intensive anti-VEGF therapy for neovascular AMD, do not show an improvement in CNV. As OT is expensive (5,308 €), the short term economics for starting this therapy were investigated. A short-term cost model was set up in MS Excel with a two year time horizon. On the basis of the data of the randomised, controlled INTREPID pivotal trial and current treatment practice in Germany, the costs were compared of conventional anti-VEGF therapy, with or without a single OT treatment. Patients with an active lesion after initial anti-VEGF therapy and a maximum lesion diameter ≤ 4 mm were included. Modeled cost components/aspects were direct savings from injection number, control follow-up examinations and aids, as well as anti-VEGF switches. Costs for Germany were employed and a univariate sensitivity analysis was performed to address the existing uncertainty. For the patients with a maximum AMD lesion diameter ≤ 4 mm and a macula volume > 7.4 mm(3), the INTREPID trial showed a mean reduction of 3.68 intravitreal injections for 16 Gy radiation versus sham over a time period of 2 years. These 3.68 IVM result in ~ 4,500 € direct cost savings. Moreover, due to the higher response rate with 16 Gy radiation, the number of follow-up visits and aids can be reduced, which results in savings between 207 € and 1,224 € over 2 years. After radiation, fewer anti-VEGF switches for low or non-responders are expected, which is modeled to result in ~ 1.7 fewer injections over 2 years. Due to overall fewer injections, fewer endophthalmitis cases would be expected. However, endophthalmitis and microvascular abnormalities, which can be observed in a few cases, are associated with low or non-quantifiable costs in this cost-cost comparison model. In summary, cost reductions of between 6,400 and 8,500 € are predicted in the model over two years

  15. Strength of tensed and compressed concrete segments in crack spacing under short-term dynamic load

    Galyautdinov Zaur

    2018-01-01

    Full Text Available Formation of model describing dynamic straining of reinforced concrete requires taking into account the basic aspects influencing the stress-strain state of structures. Strength of concrete segments in crack spacing is one of the crucial aspects that affect general strain behavior of reinforced concrete. Experimental results demonstrate significant change in strength of tensed and compressed concrete segments in crack spacing both under static and under dynamic loading. In this case, strength depends on tensile strain level and the slope angle of rebars towards the cracks direction. Existing theoretical and experimental studies estimate strength of concrete segments in crack spacing under static loading. The present work presents results of experimental and theoretical studies of dynamic strength of plates between cracks subjected to compression-tension. Experimental data was analyzed statistically; the dependences were suggested to describe dynamic strength of concrete segments depending on tensile strain level and slope angle of rebars to cracks direction.

  16. Revisiting short-term price and volatility dynamics in day-ahead electricity markets with rising wind power

    Li, Yuanjing

    2015-01-01

    This paper revisits the short-term price and volatility dynamics in day-ahead electricity markets in consideration of an increasing share of wind power, using an example of the Nord Pool day-ahead market and the Danish wind generation. To do so, a GARCH process is applied, and market coupling and the counterbalance effect of hydropower in the Scandinavian countries are additionally accounted for. As results, we found that wind generation weakly dampens spot prices with an elasticity of 0.008 and also reduces price volatility with an elasticity of 0.02 in the Nordic day-ahead market. The results shed lights on the importance of market coupling and interactions between wind power and hydropower in the Nordic system through cross-border exchanges, which play an essential role in price stabilization. Additionally, an EGARCH specification confirms an asymmetric influence of the price innovations, whereby negative shocks produce larger volatility in the Nordic spot market. While considering heavy tails in error distributions can improve model fits significantly, the EGARCH model outperforms the GARCH model on forecast evaluations. (author)

  17. Presynaptic learning and memory with a persistent firing neuron and a habituating synapse: a model of short term persistent habituation.

    Ramanathan, Kiruthika; Ning, Ning; Dhanasekar, Dhiviya; Li, Guoqi; Shi, Luping; Vadakkepat, Prahlad

    2012-08-01

    Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show that, depending on the amount of synaptic resources, persistent firing either results in continued habituation or maintains the response, both leading to longer recovery times. The effectiveness of the model as an element in a bio-inspired memory system is discussed.

  18. Predicting long-term and short-term tidal flat morphodynamics using a dynamic equilibrium theory

    Hu, Z.; Wang, Z.B.; Zitman, T.J.; Stive, M.J.F.; Bouma, T.J.

    2015-01-01

    Dynamic equilibrium theory is a fruitful concept, which we use to systematically explain the tidal flat morphodynamic response to tidal currents, wind waves, sediment supply, and other sedimentological drivers. This theory stems from a simple analytical model that derives the tide- or wave-dominated

  19. Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

    Serinaldi, Francesco

    2011-01-01

    In the context of the liberalized and deregulated electricity markets, price forecasting has become increasingly important for energy company's plans and market strategies. Within the class of the time series models that are used to perform price forecasting, the subclasses of methods based on stochastic time series and causal models commonly provide point forecasts, whereas the corresponding uncertainty is quantified by approximate or simulation-based confidence intervals. Aiming to improve the uncertainty assessment, this study introduces the Generalized Additive Models for Location, Scale and Shape (GAMLSS) to model the dynamically varying distribution of prices. The GAMLSS allow fitting a variety of distributions whose parameters change according to covariates via a number of linear and nonlinear relationships. In this way, price periodicities, trends and abrupt changes characterizing both the position parameter (linked to the expected value of prices), and the scale and shape parameters (related to price volatility, skewness, and kurtosis) can be explicitly incorporated in the model setup. Relying on the past behavior of the prices and exogenous variables, the GAMLSS enable the short-term (one-day ahead) forecast of the entire distribution of prices. The approach was tested on two datasets from the widely studied California Power Exchange (CalPX) market, and the less mature Italian Power Exchange (IPEX). CalPX data allow comparing the GAMLSS forecasting performance with published results obtained by different models. The study points out that the GAMLSS framework can be a flexible alternative to several linear and nonlinear stochastic models. - Research Highlights: ► Generalized Additive Models for Location, Scale and Shape (GAMLSS) are used to model electricity prices' time series. ► GAMLSS provide the entire dynamicaly varying distribution function of prices resorting to a suitable set of covariates that drive the instantaneous values of the parameters

  20. Persistent short-term memory defects following sleep deprivation in a drosophila model of Parkinson disease.

    Seugnet, Laurent; Galvin, James E; Suzuki, Yasuko; Gottschalk, Laura; Shaw, Paul J

    2009-08-01

    Parkinson disease (PD) is the second most common neurodegenerative disorder in the United States. It is associated with motor deficits, sleep disturbances, and cognitive impairment. The pathology associated with PD and the effects of sleep deprivation impinge, in part, upon common molecular pathways suggesting that sleep loss may be particularly deleterious to the degenerating brain. Thus we investigated the long-term consequences of sleep deprivation on shortterm memory using a Drosophila model of Parkinson disease. Transgenic strains of Drosophila melanogaster. Using the GAL4-UAS system, human alpha-synuclein was expressed throughout the nervous system of adult flies. Alpha-synuclein expressing flies (alpha S flies) and the corresponding genetic background controls were sleep deprived for 12 h at age 16 days and allowed to recover undisturbed for at least 3 days. Short-term memory was evaluated using aversive phototaxis suppression. Dopaminergic systems were assessed using mRNA profiling and immunohistochemistry. MEASURMENTS AND RESULTS: When sleep deprived at an intermediate stage of the pathology, alpha S flies showed persistent short-term memory deficits that lasted > or = 3 days. Cognitive deficits were not observed in younger alpha S flies nor in genetic background controls. Long-term impairments were not associated with accelerated loss of dopaminergic neurons. However mRNA expression of the dopamine receptors dDA1 and DAMB were significantly increased in sleep deprived alpha S flies. Blocking D1-like receptors during sleep deprivation prevented persistent shortterm memory deficits. Importantly, feeding flies the polyphenolic compound curcumin blocked long-term learning deficits. These data emphasize the importance of sleep in a degenerating/reorganizing brain and shows that pathological processes induced by sleep deprivation can be dissected at the molecular and cellular level using Drosophila genetics.

  1. The (lack of) effect of dynamic visual noise on the concreteness effect in short-term memory.

    Castellà, Judit; Campoy, Guillermo

    2018-05-17

    It has been suggested that the concreteness effect in short-term memory (STM) is a consequence of concrete words having more distinctive and richer semantic representations. The generation and storage of visual codes in STM could also play a crucial role on the effect because concrete words are more imaginable than abstract words. If this were the case, the introduction of a visual interference task would be expected to disrupt recall of concrete words. A Dynamic Visual Noise (DVN) display, which has been proven to eliminate the concreteness effect on long-term memory (LTM), was presented along encoding of concrete and abstract words in a STM serial recall task. Results showed a main effect of word type, with more item errors in abstract words, a main effect of DVN, which impaired global performance due to more order errors, but no interaction, suggesting that DVN did not have any impact on the concreteness effect. These findings are discussed in terms of LTM participation through redintegration processes and in terms of the language-based models of verbal STM.

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

    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.

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

    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.

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

    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)

  5. Early short-term management of control-actuator failures in a linear dynamic system

    Ben-Haim, Y.

    1989-01-01

    Early short-term management of malfunction attempts to maintain system stability during the early development stages of a failure. This is achieved in two stages. First, the failure is partially diagnosed by comparing observed system behavior against the performance expected for each of the selected set of hypothesized malfunctions. Second, the normal controller is replaced by a compensatory controller whose aim is to maintain system stability while compensating for the failure. Malfunctions involving control actuators are studied here. The aim of this study is to develop a technique for choosing the set of hypothesized failures and compensatory controllers which assure that the state of the system remains within specified bounds for a given duration after initiation of failure, regardless of the precise temporal development of the failure

  6. Probalistisk short-term risk modeling for back-end fuel cycle and waste management facilities

    Kjellbert, N.A.

    1980-03-01

    This study of probabilistic short-term risk modeling of back-end fuel cycle and waste management facilities represents the continuation of work started in 1977. The purpose of the report is to present a more detailed survey of models and analysis techniques that mey be applicable. The definition of the risk concept and the nature of the facilities and events which are to be analyzed are described. The most important criteria are that the model or method shall be quantitative, logically/scientifically based, and be able to handle systems of some complexity. Several formalized analysis methods are described, most of them emanating from reliability theory. No single model will fulfill all criteria simultaneously, to the degree desired. Nevertheless, fault tree analysis seems to be an efficient tool in many applications, although it must probably be used together with other models in most cases. Other methodologies described can also be useful, such as failure modes and effects analysis, renewal theory and Markov chains, reliability block diagrams, event trees and cause/consequence diagrams, the GO methodology, Monte Carlo simulation, and, often necessary, various consequence modeling techniques. (author)

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

    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

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

    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.

  9. Short-Term Solar Irradiance Forecasts Using Sky Images and Radiative Transfer Model

    Juan Du

    2018-05-01

    Full Text Available In this paper, we propose a novel forecast method which addresses the difficulty in short-term solar irradiance forecasting that arises due to rapidly evolving environmental factors over short time periods. This involves the forecasting of Global Horizontal Irradiance (GHI that combines prediction sky images with a Radiative Transfer Model (RTM. The prediction images (up to 10 min ahead are produced by a non-local optical flow method, which is used to calculate the cloud motion for each pixel, with consecutive sky images at 1 min intervals. The Direct Normal Irradiance (DNI and the diffuse radiation intensity field under clear sky and overcast conditions obtained from the RTM are then mapped to the sky images. Through combining the cloud locations on the prediction image with the corresponding instance of image-based DNI and diffuse radiation intensity fields, the GHI can be quantitatively forecasted for time horizons of 1–10 min ahead. The solar forecasts are evaluated in terms of root mean square error (RMSE and mean absolute error (MAE in relation to in-situ measurements and compared to the performance of the persistence model. The results of our experiment show that GHI forecasts using the proposed method perform better than the persistence model.

  10. Short-term airing by natural ventilation - modeling and control strategies.

    Perino, M; Heiselberg, P

    2009-10-01

    The need to improve the energy efficiency of buildings requires new and more efficient ventilation systems. It has been demonstrated that innovative operating concepts that make use of natural ventilation seem to be more appreciated by occupants. This kind of system frequently integrates 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 for temperature and IAQ control. However, in order to promote a wider applications of these systems, an improvement in the knowledge of their working principles and the availability of new design and simulation tools is necessary. In this context, the paper analyses and presents the results of a research 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. Practical Implications Numerical and experimental analyses have proved that short-term airing by intermittent ventilation is an effective measure to satisfactorily control IAQ. Different control strategies have been investigated to optimize the capabilities of the systems. The proposed zonal model has provided good performances and could be adopted as a design tool, while CFD simulations can be profitably used for detailed studies of the pollutant concentration distribution in a room and to address local discomfort problems.

  11. Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

    Hui Wang

    2017-10-01

    Full Text Available Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

  12. Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy

    Smith, Keith; Ricaud, Benjamin; Shahid, Nauman; Rhodes, Stephen; Starr, John M.; Ibáñez, Augustin; Parra, Mario A.; Escudero, Javier; Vandergheynst, Pierre

    2017-02-01

    Visual short-term memory binding tasks are a promising early marker for Alzheimer’s disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function. Electroencephalogram recordings were obtained from encoding and maintenance periods of tasks performed by healthy young volunteers. We probe the task’s transient physiological underpinnings by contrasting shape only (Shape) and shape-colour binding (Bind) conditions, displayed in the left and right sides of the screen, separately. Particularly, we introduce and implement a novel technique named Modular Dirichlet Energy (MDE) which allows robust and flexible analysis of the functional network with unprecedented temporal precision. We find that connectivity in the Bind condition is less integrated with the global network than in the Shape condition in occipital and frontal modules during the encoding period of the right screen condition. Using MDE we are able to discern driving effects in the occipital module between 100-140 ms, coinciding with the P100 visually evoked potential, followed by a driving effect in the frontal module between 140-180 ms, suggesting that the differences found constitute an information processing difference between these modules. This provides temporally precise information over a heterogeneous population in promising tasks for the detection of AD.

  13. Short-term dynamics of culturable bacteria in a soil amended with biotransformed dry olive residue.

    Siles, J A; Pascual, J; González-Menéndez, V; Sampedro, I; García-Romera, I; Bills, G F

    2014-03-01

    Dry olive residue (DOR) transformation by wood decomposing basidiomycetes (e.g. Coriolopsis floccosa) is a possible strategy for eliminating the liabilities related to the use of olive oil industry waste as an organic soil amendment. The effects of organic fertilization with DOR on the culturable soil microbiota are largely unknown. Therefore, the objectives of this study were to measure the short-term effects of DOR and C. floccosa-transformed DOR on the culturable bacterial soil community, while at the same time documenting the bacterial diversity of an agronomic soil in the southeastern Iberian Peninsula. The control soil was compared with the same soil treated with DOR and with C. floccosa-transformed DOR for 0, 30 and 60 days. Impact was measured from total viable cells and CFU counts, as well as the isolation and characterization of 900 strains by fatty acid methyl ester profiles and 16S rRNA partial sequencing. The bacterial diversity was distributed between Actinobacteria, Alphaproteobacteria, Gammaproteobacteria, Betaproteobacteria, Bacilli, Sphingobacteria and Cytophagia. Analysis of the treatments and controls demonstrated that soil amendment with untransformed DOR produced important changes in bacterial density and diversity. However, when C. floccosa-transformed DOR was applied, bacterial proliferation was observed but bacterial diversity was less affected, and the distribution of microorganisms was more similar to the unamended soil. Copyright © 2013 Elsevier GmbH. All rights reserved.

  14. Short-Term Dynamics of Behavioral Thermoregulation by Adults of the Grasshopper Melanoplus sanguinipes

    O'Neill, Kevin M.; Rolston, Marni G.

    2007-01-01

    The short-term behavioral responses of adult grasshoppers, Melanoplus sanguinipes (F.) (Orthoptera: Acrididae), were examined after they experienced changes in microclimate when beingforced to change positions in their habitat. It was also determined if and when behavioral tactics allowed adults to achieve body temperatures within their preferred range. The preferred or set-point range, here taken as the interquartile range of temperatures selected on a laboratory thermal gradient, was estimated to be 37.4–40.5°C. In the field, adults progressed through a relatively consistent daily sequence of behaviors, basking on the soil early in the day, but moving onto vegetation as temperatures increased. Although basking allowed grasshoppers to maximize body temperature within the available range, as much as 7°C in excess of air temperature, they could not attain preferred body temperatures until soil surface temperatures reach about 35°C. Basking was more effective in grazed than ungrazed pastures due to a lower degree of shading of the soil surface. As soil surface temperatures exceeded 35°C, grasshoppers could achieve body temperatures within the preferred range by moving to the appropriate height on vegetation. These results illustrate the advantage of assessing behavior in the field in relation to preferred body temperatures determined in the laboratory. PMID:20302531

  15. Proteome dynamics and physiological responses to short-term salt stress in Leymus chinensis leaves.

    Jikai Li

    Full Text Available Salt stress is becoming an increasing threat to global agriculture. In this study, physiological and proteomics analysis were performed using a salt-tolerant grass species, Leymus chinensis (L. chinensis. The aim of this study is to understand the potential mechanism of salt tolerance in L. chinensis that used for crop molecular breeding. A series of short-term (<48 h NaCl treatments (0 ~ 700 mM were conducted. Physiological data indicated that the root and leaves growth were inhibited, chlorophyll contents decreased, while hydraulic conductivity, proline, sugar and sucrose were accumulated under salt stress. For proteomic analysis, we obtained 274 differentially expressed proteins in response to NaCl treatments. GO analysis revealed that 44 out of 274 proteins are involved in the biosynthesis of amino acids and carbon metabolism. Our findings suggested that L. chinensis copes with salt stress by stimulating the activities of POD, SOD and CAT enzymes, speeding up the reactions of later steps of citrate cycle, and synthesis of proline and sugar. In agreement with our physiological data, proteomic analysis also showed that salt stress depress the expression of photosystem relevant proteins, Calvin cycle, and chloroplast biosynthesis.

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

    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.

  17. Machine Learning Techniques for Modelling Short Term Land-Use Change

    Mileva Samardžić-Petrović

    2017-11-01

    Full Text Available The representation of land use change (LUC is often achieved by using data-driven methods that include machine learning (ML techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT, Neural Networks (NN, and Support Vector Machines (SVM for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.

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

    Lara, Antonio H; Wallis, Jonathan D

    2012-03-14

    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.

  19. Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons

    Hazem eToutounji

    2014-05-01

    Full Text Available The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered, or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with eighteen degrees of freedom, and obstacle-avoidance of a wheel-driven robot.

  20. Short-term poverty dynamics of rural households: Evidence from Central Sulawesi, Indonesia

    Stefan Schwarze

    2011-12-01

    Full Text Available The understanding of poverty dynamics is crucial for the design of appropriate poverty reduction strategies. Taking the case of Central Sulawesi, we investigate the determinants of both chronic and transitory poverty using data from 264 randomly selected households interviewed in 2005 and 2007. Regarding the US 1$/day poverty line, the headcount index declined from 19.3% in 2005 to 18.2% in 2007. However, we observed an increasing number of people living on less than US 2$/day expressed in purchasing power parity (PPP. The results of the estimated multinomial logit model applied in this study indicate that a lack of non-agricultural employment opportunities and low endowment of social capital are major determinants of chronic as well as transitory poverty in this province of Indonesia. These results are used to draw policy conclusions with respect to the alleviation of transitory and chronic poverty in Central Sulawesi.

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

    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).

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

    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

  3. Least square regression based integrated multi-parameteric demand modeling for short term load forecasting

    Halepoto, I.A.; Uqaili, M.A.

    2014-01-01

    Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintained. (author)

  4. Short term decisions for long term problems - The effect of foresight on model based energy systems analysis

    Keppo, Ilkka; Strubegger, Manfred

    2010-01-01

    This paper presents the development and demonstration of a limited foresight energy system model. The presented model is implemented as an extension to a large, linear optimization model, MESSAGE. The motivation behind changing the model is to provide an alternative decision framework, where information for the full time frame is not available immediately and sequential decision making under incomplete information is implied. While the traditional optimization framework provides the globally optimal decisions for the modeled problem, the framework presented here may offer a better description of the decision environment, under which decision makers must operate. We further modify the model to accommodate flexible dynamic constraints, which give an option to implement investments faster, albeit with a higher cost. Finally, the operation of the model is demonstrated using a moving window of foresight, with which decisions are taken for the next 30 years, but can be reconsidered later, when more information becomes available. We find that the results demonstrate some of the pitfalls of short term planning, e.g. lagging investments during earlier periods lead to higher requirements later during the century. Furthermore, the energy system remains more reliant on fossil based energy carriers, leading to higher greenhouse gas emissions.

  5. An Agent-Based Model for the Role of Short-Term Memory Enhancement in the Emergence of Grammatical Agreement.

    Vera, Javier

    2018-01-01

    What is the influence of short-term memory enhancement on the emergence of grammatical agreement systems in multi-agent language games? Agreement systems suppose that at least two words share some features with each other, such as gender, number, or case. Previous work, within the multi-agent language-game framework, has recently proposed models stressing the hypothesis that the emergence of a grammatical agreement system arises from the minimization of semantic ambiguity. On the other hand, neurobiological evidence argues for the hypothesis that language evolution has mainly related to an increasing of short-term memory capacity, which has allowed the online manipulation of words and meanings participating particularly in grammatical agreement systems. Here, the main aim is to propose a multi-agent language game for the emergence of a grammatical agreement system, under measurable long-range relations depending on the short-term memory capacity. Computer simulations, based on a parameter that measures the amount of short-term memory capacity, suggest that agreement marker systems arise in a population of agents equipped at least with a critical short-term memory capacity.

  6. Comfrey (Symphytum Officinale. l.) and Experimental Hepatic Carcinogenesis: A Short-term Carcinogenesis Model Study.

    Gomes, Maria Fernanda Pereira Lavieri; de Oliveira Massoco, Cristina; Xavier, José Guilherme; Bonamin, Leoni Villano

    2010-06-01

    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 χ(2)) 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.

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

    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…

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

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

    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

  9. Short term outcome of posterior dynamic stabilization system in degenerative lumbar diseases

    Mingyuan Yang

    2014-01-01

    Conclusion: Dynamic stabilization system treating lumbar degenerative disease showed clinical benefits with motion preservation of the operated segments, but does not have the significant advantage on motion preservation at adjacent segments, to avoid the degeneration of adjacent intervertebral disk.

  10. Short-term observation of beach dynamics using cross-shore profiles and foreshore sediment

    Dora, G.U.; SanilKumar, V.; Johnson, G.; Philip, C.S.; Vinayaraj, P.

    Cross-shore beach profiles and textural characteristics of foreshore sediment were analyzed for understanding an annual cycle of intertidal beach dynamics at Devbag, an Island sheltered estuarine coast. Cross-shore transects were monitored in a...

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

    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.

  12. Grouping influences output interference in short-term memory: a mixture modeling study

    Min-Suk eKang

    2016-05-01

    Full Text Available Output interference is a source of forgetting induced by recalling. We investigated how grouping influences output interference in short-term memory. In Experiment 1, the participants were asked to remember four colored items. Those items were grouped by temporal coincidence as well as spatial alignment: two items were presented in the first memory array and two were presented in the second, and the items in both arrays were either vertically or horizontally aligned as well. The participants then performed two recall tasks in sequence by selecting a color presented at a cued location from a color wheel. In the same-group condition, the participants reported both items from the same memory array; however, in the different-group condition, the participants reported one item from each memory array. We analyzed participant responses with a mixture model, which yielded two measures: guess rate and precision of recalled memories. The guess rate in the second recall was higher for the different-group condition than for the same-group condition; however, the memory precisions obtained for both conditions were similarly degraded in the second recall. In Experiment 2, we varied the probability of the same- and different-group conditions with a ratio of 3 to 7. We expected output interference to be higher in the same-group condition than in the different-group condition. This is because items of the other group are more likely to be probed in the second recall phase and, thus, protecting those items during the first recall phase leads to a better performance. Nevertheless, the same pattern of results was robustly reproduced, suggesting grouping shields the grouped items from output interference because of the secured accessibility. We discussed how grouping influences output interference.

  13. Short Term Strategies for a Dynamic Multi-Period Routing Problem

    Angelelli, E.; Bianchessi, N.; Mansini, R.; Speranza, M. G.

    2009-01-01

    We consider a Dynamic Multi-Period Routing Problem (DMPRP) faced by a company which deals with on-line pick-up requests and has to serve them by a fleet of uncapacitated vehicles over a finite time horizon. When a request is issued, a deadline of a given number of days d ≤ 2 is associated to it: if

  14. Firm Size and Short-Term Dynamics in Aggregate Entry and Exit

    Manjon, M.C.

    2004-01-01

    Much of the research on industry dynamics focuses on the interdependence between the sectorial rates of entry and exit.This paper argues that the size of firms and the reaction-adjustment period are important conditions missed in this literature.I illustrate the effects of this omission using data

  15. Model and economic uncertainties in balancing short-term and long-term objectives in water-flooding optimization.

    Siraj, M.M.; Hof, Van den P.M.J.; Jansen, J.D.

    2015-01-01

    Model-based optimization of oil production has a significant scope to increase ultimate recovery or financial life-cycle performance. The Net Present Value (NPV) objective in such an optimization framework, because of its nature, focuses on the long-term gains while the short-term production is not

  16. Combining weather radar nowcasts and numerical weather prediction models to estimate short-term quantitative precipitation and uncertainty

    Jensen, David Getreuer

    The topic of this Ph.D. thesis is short term forecasting of precipitation for up to 6 hours called nowcasts. The focus is on improving the precision of deterministic nowcasts, assimilation of radar extrapolation model (REM) data into Danish Meteorological Institutes (DMI) HIRLAM numerical weather...

  17. Dynamic visual noise affects visual short-term memory for surface color, but not spatial location.

    Dent, Kevin

    2010-01-01

    In two experiments participants retained a single color or a set of four spatial locations in memory. During a 5 s retention interval participants viewed either flickering dynamic visual noise or a static matrix pattern. In Experiment 1 memory was assessed using a recognition procedure, in which participants indicated if a particular test stimulus matched the memorized stimulus or not. In Experiment 2 participants attempted to either reproduce the locations or they picked the color from a whole range of possibilities. Both experiments revealed effects of dynamic visual noise (DVN) on memory for colors but not for locations. The implications of the results for theories of working memory and the methodological prospects for DVN as an experimental tool are discussed.

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

    Wang, Jun; Mccabe, Mckinleigh; Wu, Lei; Dong, Xiaomeng; Wang, Xianmao; Haskin, Troy Christopher; Corradini, Michael L.

    2017-01-01

    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

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

    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

  20. Application of Quantitative Models, MNLR and ANN in Short Term Forecasting of Ship Data

    P.Oliver Jayaprakash; K. Gunasekaran

    2011-01-01

    Forecasting has been the trouble-free way for the port authorities to derive the future expected values of service time of Bulk cargo ships handled at ports of South India. The short term forecasting could be an effective tool for estimating the resource requirements of recurring ships of similar tonnage and Cargo. Forecasting the arrival data related to port based ship operations customarily done using the standard algorithms and assumptions. The regular forecasting methods were decompositio...

  1. Short-term memory

    Toulouse, G.

    This is a rather bold attempt to bridge the gap between neuron structure and psychological data. We try to answer the question: Is there a relation between the neuronal connectivity in the human cortex (around 5,000) and the short-term memory capacity (7±2)? Our starting point is the Hopfield model (Hopfield 1982), presented in this volume by D.J. Amit.

  2. Short-term recurrent chaos and role of Toxin Producing Phytoplankton (TPP) on chaotic dynamics in aquatic systems

    Upadhyay, Ranjit Kumar; Rao, V. Sree Hari

    2009-01-01

    We propose a new mathematical model for aquatic populations. This model incorporates mutual interference in all the three populations and an extra mortality term in zooplankton population and also taking into account the toxin liberation process of TPP population. The proposed model generalizes several other known models in the literature. The principal interest in this paper is in a numerical study of the model's behaviour. It is observed that both types of food chains display same type of chaotic behaviour, short-term recurrent chaos, with different generating mechanisms. Toxin producing phytoplankton (TPP) reduces the grazing pressure of zooplankton. To observe the role of TPP, we consider Holling types I, II and III functional forms for this process. Our study suggests that toxic substances released by TPP population may act as bio-control by changing the state of chaos to order and extinction.

  3. Examining the short term effects of emotion under an Adaptation Level Theory model of tinnitus perception.

    Durai, Mithila; O'Keeffe, Mary G; Searchfield, Grant D

    2017-03-01

    Existing evidence suggests a strong relationship between tinnitus and emotion. The objective of this study was to examine the effects of short-term emotional changes along valence and arousal dimensions on tinnitus outcomes. Emotional stimuli were presented in two different modalities: auditory and visual. The authors hypothesized that (1) negative valence (unpleasant) stimuli and/or high arousal stimuli will lead to greater tinnitus loudness and annoyance than positive valence and/or low arousal stimuli, and (2) auditory emotional stimuli, which are in the same modality as the tinnitus, will exhibit a greater effect on tinnitus outcome measures than visual stimuli. Auditory and visual emotive stimuli were administered to 22 participants (12 females and 10 males) with chronic tinnitus, recruited via email invitations send out to the University of Auckland Tinnitus Research Volunteer Database. Emotional stimuli used were taken from the International Affective Digital Sounds- Version 2 (IADS-2) and the International Affective Picture System (IAPS) (Bradley and Lang, 2007a, 2007b). The Emotion Regulation Questionnaire (Gross and John, 2003) was administered alongside subjective ratings of tinnitus loudness and annoyance, and psychoacoustic sensation level matches to external sounds. Males had significantly different emotional regulation scores than females. Negative valence emotional auditory stimuli led to higher tinnitus loudness ratings in males and females and higher annoyance ratings in males only; loudness matches of tinnitus remained unchanged. The visual stimuli did not have an effect on tinnitus ratings. The results are discussed relative to the Adaptation Level Theory Model of Tinnitus. The results indicate that the negative valence dimension of emotion is associated with increased tinnitus magnitude judgements and gender effects may also be present, but only when the emotional stimulus is in the auditory modality. Sounds with emotional associations may be

  4. The relation between short-term emotion dynamics and psychological well-being: A meta-analysis.

    Houben, Marlies; Van Den Noortgate, Wim; Kuppens, Peter

    2015-07-01

    Not only how good or bad people feel on average, but also how their feelings fluctuate across time is crucial for psychological health. The last 2 decades have witnessed a surge in research linking various patterns of short-term emotional change to adaptive or maladaptive psychological functioning, often with conflicting results. A meta-analysis was performed to identify consistent relationships between patterns of short-term emotion dynamics-including patterns reflecting emotional variability (measured in terms of within-person standard deviation of emotions across time), emotional instability (measured in terms of the magnitude of consecutive emotional changes), and emotional inertia of emotions over time (measured in terms of autocorrelation)-and relatively stable indicators of psychological well-being or psychopathology. We determined how such relationships are moderated by the type of emotional change, type of psychological well-being or psychopathology involved, valence of the emotion, and methodological factors. A total of 793 effect sizes were identified from 79 articles (N = 11,381) and were subjected to a 3-level meta-analysis. The results confirmed that overall, low psychological well-being co-occurs with more variable (overall ρ̂ = -.178), unstable (overall ρ̂ = -.205), but also more inert (overall ρ̂ = -.151) emotions. These effect sizes were stronger when involving negative compared with positive emotions. Moreover, the results provided evidence for consistency across different types of psychological well-being and psychopathology in their relation with these dynamical patterns, although specificity was also observed. The findings demonstrate that psychological flourishing is characterized by specific patterns of emotional fluctuations across time, and provide insight into what constitutes optimal and suboptimal emotional functioning. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

  5. Reduced short term adaptation to robot generated dynamic environment in children affected by Cerebral Palsy.

    Masia, Lorenzo; Frascarelli, Flaminia; Morasso, Pietro; Di Rosa, Giuseppe; Petrarca, Maurizio; Castelli, Enrico; Cappa, Paolo

    2011-05-21

    It is known that healthy adults can quickly adapt to a novel dynamic environment, generated by a robotic manipulandum as a structured disturbing force field. We suggest that it may be of clinical interest to evaluate to which extent this kind of motor learning capability is impaired in children affected by cerebal palsy. We adapted the protocol already used with adults, which employs a velocity dependant viscous field, and compared the performance of a group of subjects affected by Cerebral Palsy (CP group, 7 subjects) with a Control group of unimpaired age-matched children. The protocol included a familiarization phase (FA), during which no force was applied, a force field adaptation phase (CF), and a wash-out phase (WO) in which the field was removed. During the CF phase the field was shut down in a number of randomly selected "catch" trials, which were used in order to evaluate the "learning index" for each single subject and the two groups. Lateral deviation, speed and acceleration peaks and average speed were evaluated for each trajectory; a directional analysis was performed in order to inspect the role of the limb's inertial anisotropy in the different experimental phases. During the FA phase the movements of the CP subjects were more curved, displaying greater and variable directional error; over the course of the CF phase both groups showed a decreasing trend in the lateral error and an after-effect at the beginning of the wash-out, but the CP group had a non significant adaptation rate and a lower learning index, suggesting that CP subjects have reduced ability to learn to compensate external force. Moreover, a directional analysis of trajectories confirms that the control group is able to better predict the force field by tuning the kinematic features of the movements along different directions in order to account for the inertial anisotropy of arm. Spatial abnormalities in children affected by cerebral palsy may be related not only to disturbance in

  6. Reduced short term adaptation to robot generated dynamic environment in children affected by Cerebral Palsy

    Di Rosa Giuseppe

    2011-05-01

    Full Text Available Abstract Background It is known that healthy adults can quickly adapt to a novel dynamic environment, generated by a robotic manipulandum as a structured disturbing force field. We suggest that it may be of clinical interest to evaluate to which extent this kind of motor learning capability is impaired in children affected by cerebal palsy. Methods We adapted the protocol already used with adults, which employs a velocity dependant viscous field, and compared the performance of a group of subjects affected by Cerebral Palsy (CP group, 7 subjects with a Control group of unimpaired age-matched children. The protocol included a familiarization phase (FA, during which no force was applied, a force field adaptation phase (CF, and a wash-out phase (WO in which the field was removed. During the CF phase the field was shut down in a number of randomly selected "catch" trials, which were used in order to evaluate the "learning index" for each single subject and the two groups. Lateral deviation, speed and acceleration peaks and average speed were evaluated for each trajectory; a directional analysis was performed in order to inspect the role of the limb's inertial anisotropy in the different experimental phases. Results During the FA phase the movements of the CP subjects were more curved, displaying greater and variable directional error; over the course of the CF phase both groups showed a decreasing trend in the lateral error and an after-effect at the beginning of the wash-out, but the CP group had a non significant adaptation rate and a lower learning index, suggesting that CP subjects have reduced ability to learn to compensate external force. Moreover, a directional analysis of trajectories confirms that the control group is able to better predict the force field by tuning the kinematic features of the movements along different directions in order to account for the inertial anisotropy of arm. Conclusions Spatial abnormalities in children affected

  7. Short-term Periodization Models: Effects on Strength and Speed-strength Performance.

    Hartmann, Hagen; Wirth, Klaus; Keiner, Michael; Mickel, Christoph; Sander, Andre; Szilvas, Elena

    2015-10-01

    Dividing training objectives into consecutive phases to gain morphological adaptations (hypertrophy phase) and neural adaptations (strength and power phases) is called strength-power periodization (SPP). These phases differ in program variables (volume, intensity, and exercise choice or type) and use stepwise intensity progression and concomitant decreasing volume, converging to peak intensity (peaking phase). Undulating periodization strategies rotate these program variables in a bi-weekly, weekly, or daily fashion. The following review addresses the effects of different short-term periodization models on strength and speed-strength both with subjects of different performance levels and with competitive athletes from different sports who use a particular periodization model during off-season, pre-season, and in-season conditioning. In most periodization studies, it is obvious that the strength endurance sessions are characterized by repetition zones (12-15 repetitions) that induce muscle hypertrophy in persons with a low performance level. Strictly speaking, when examining subjects with a low training level, many periodization studies include mainly hypertrophy sessions interspersed with heavy strength/power sessions. Studies have demonstrated equal or statistically significant higher gains in maximal strength for daily undulating periodization compared with SPP in subjects with a low to moderate performance level. The relatively short intervention period and the lack of concomitant sports conditioning call into question the practical value of these findings for competitive athletes. Possibly owing to differences in mesocycle length, conditioning programs, and program variables, competitive athletes either maintained or improved strength and/or speed-strength performance by integrating daily undulating periodization and SPP during off-season, pre-season and in-season conditioning. In high-performance sports, high-repetition strength training (>15) should be

  8. Dynamics of short-term acclimation to UV radiation in marine diatoms.

    Fouqueray, Manuela; Mouget, Jean-Luc; Morant-Manceau, Annick; Tremblin, Gérard

    2007-11-12

    In order to investigate the dynamics of the acclimation of marine diatoms to ultraviolet radiation (UVR), Amphora coffeaeformis, Odontella aurita and Skeletonema costatum were exposed for 5 h per day to a combination of UVA and UVB (UVBR/UVAR ratio 4.5%) with a total UVR daily dose of 110 kJ m(-2), which is equivalent to that observed in the natural environment. This treatment was applied in the middle of the photoperiod and was repeated on five successive days. During the UVR treatment, chlorophyll fluorescence parameters were monitored, damage and repair constants were calculated from effective quantum yield values (phi(PSII)), and rapid light curves (electron transport rate versus irradiance curves using short light steps of different intensity) were plotted to determine the maximum relative electron transport rate (rETR(max)) and maximum light use efficiency (alpha). In all species the growth rate was lower than control from day 1-3, but increased thereafter, except for S. costatum. The cellular chlorophyll a content increased significantly with repeated daily exposure to UVR for A. coffeaeformis only. In all species, the fluorescence parameters (F(m), the maximum fluorescence level measured in the dark, phi(PSII), rETR(max) and alpha) decreased during UVR exposure, in contrast to F(0) (the minimum fluorescence level measured in the dark). The response to UVR stress was species-specific. S. costatum was very sensitive, and failed to survive for more than three days, whereas A. coffeaeformis and O. aurita were able to acclimate to UVR stress. These two species used different strategies. In A. coffeaeformis, the repair constant was lower than the damage constant, but phi(PSII) values returned to baseline values at the beginning of each experimental day, indicating that an effective active recovery process occurred after stress. In O. aurita, the repair processes took place during the stress, and could account for the UVR tolerance of this species.

  9. Modeling of hot tensile and short-term creep strength for LWR piping materials under severe accident conditions

    Harada, Y.; Maruyama, Y.; Chino, E.; Shibazaki, H.; Kudo, T.; Hidaka, A.; Hashimoto, K.; Sugimoto, J.

    2000-01-01

    The analytical study on severe accident shows the possibility of the reactor coolant system (RCS) piping failure before reactor pressure vessel failure under the high primary pressure sequence at pressurized water reactors. The establishment of the high-temperature strength model of the realistic RCS piping materials is important in order to predict precisely the accident progression and to evaluate the piping behavior with small uncertainties. Based on material testing, the 0.2% proof stress and the ultimate tensile strength above 800degC were given by the equations of second degree as a function of the reciprocal absolute temperature considering the strength increase due to fine precipitates for the piping materials. The piping materials include type 316 stainless steel, type 316 stainless steel of nuclear grade, CF8M cast duplex stainless steel and STS410 carbon steel. Also the short-term creep rupture time and the minimum creep rate at high-temperature were given by the modified Norton's Law as a function of stress and temperature considering the effect of the precipitation formation and resolution on the creep strength. The present modified Norton's Law gives better results than the conventional Larson-Miller method. Correlating the creep data (the applied stress versus the minimum creep rate) with the tensile data (the 0.2% proof stress or the ultimate tensile strength versus the strain rate), it was found that the dynamic recrystallization significantly occurred at high-temperature. (author)

  10. Statistical Language Modeling for Historical Documents using Weighted Finite-State Transducers and Long Short-Term Memory

    Al Azawi, Mayce

    2015-01-01

    The goal of this work is to develop statistical natural language models and processing techniques based on Recurrent Neural Networks (RNN), especially the recently introduced Long Short- Term Memory (LSTM). Due to their adapting and predicting abilities, these methods are more robust, and easier to train than traditional methods, i.e., words list and rule-based models. They improve the output of recognition systems and make them more accessible to users for browsing and reading...

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

    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.

  12. Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

    Murat Luy

    2018-05-01

    Full Text Available The estimation of hourly electricity load consumption is highly important for planning short-term supply–demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base—which is defined by expert insights and decisions—gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014. The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment.

  13. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

    Ping-Huan Kuo

    2018-01-01

    Full Text Available One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE and Cumulative Variation of Root Mean Square Error (CV-RMSE are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

  14. NUMERICAL SIMULATION OF YIELDING SUPPORTS IN THE SHAPE OF ANNULAR TUBES UNDER STATIC AND SHORT-TERM DYNAMIC LOADING

    Oleg G. Kumpyak

    2017-12-01

    Full Text Available Occurrence of extreme man-made impacts on buildings and structures has become frequent lately as a consequence of condensed explosives or explosive combustion of gas- vapor or air-fuel mixtures. Such accidents involve large human and economic losses, and their prevention methods are not always effective and reasonable. The given research aims at studying the way of enhancing explosion safety of building structures by means of yielding supports. The paper presents results of numerical studies (finite element, 3D nonlinear of strength and deformability of yielding supports in the shape of annular tubes under static and short-term dynamic loading. The degree of influence of yielding supports was assessed taking into account three peculiar stages of deformation: elastic; elasto-plastic; elasto-plastic with hardening. The methodology for numerical studies performance was described. It was established that rigidity of yielding supports influences significantly their stress-strain state. The research determined that with increase of deformable elements rigidity dependency between load and deformation of yielding supports in elastic and plastic stages have linear character. Significant reduction of dynamic response and increase of deformation time of yielding supports was observed by increasing the plastic component. Therefore it allows assuming on possibility of their application as supporting units in reinforced concrete constructions

  15. A Distributed Web-based Solution for Ionospheric Model Real-time Management, Monitoring, and Short-term Prediction

    Kulchitsky, A.; Maurits, S.; Watkins, B.

    2006-12-01

    provide inputs for the next ionospheic model time step and then stored in a MySQL database as the first part of the time-specific record. The RMM then performs synchronization of the input times with the current model time, prepares a decision on initialization for the next model time step, and monitors its execution. Then, as soon as the model completes computations for the next time step, RMM visualizes the current model output into various short-term (about 1-2 hours) forecasting products and compares prior results with available ionospheric measurements. The RMM places prepared images into the MySQL database, which can be located on a different computer node, and then proceeds to the next time interval continuing the time-loop. The upper-level interface of this real-time system is the a PHP-based Web site (http://www.arsc.edu/SpaceWeather/new). This site provides general information about the Earth polar and adjacent mid-latitude ionosphere, allows for monitoring of the current developments and short-term forecasts, and facilitates access to the comparisons archive stored in the database.

  16. Functional hoarseness in children: short-term play therapy with family dynamic counseling as therapy of choice.

    Kollbrunner, Jürg; Seifert, Eberhard

    2013-09-01

    Children with nonorganic voice disorders (NVDs) are treated mainly using direct voice therapy techniques such as the accent method or glottal attack changes and indirect methods such as vocal hygiene and voice education. However, both approaches tackle only the symptoms and not etiological factors in the family dynamics and therefore often enjoy little success. The aim of the "Bernese Brief Dynamic Intervention" (BBDI) for children with NVD was to extend the effectiveness of pediatric voice therapies with a psychosomatic concept combining short-term play therapy with the child and family dynamic counseling of the parents. This study compares the therapeutic changes in three groups where different procedures were used, before intervention and 1 year afterward: counseling of parents (one to two consultations; n = 24), Brief Dynamic Intervention on the lines of the BBDI (three to five play therapy sessions with the child plus two to four sessions with the parents; n = 20), and traditional voice therapy (n = 22). A Voice Questionnaire for Parents developed by us with 59 questions to be answered on a four-point Likert scale was used to measure the change. According to the parents' assessment, a significant improvement in voice quality was achieved in all three methods. Counseling of parents (A) appears to have led parents to give their child more latitude, for example, they stopped nagging the child or demanding that he/she should behave strictly by the rules. After BBDI (B), the mothers were more responsive to their children's wishes and the children were more relaxed and their speech became livelier. At home, they called out to them less often at a distance, which probably improved parent-child dialog. Traditional voice therapy (C) seems to have had a positive effect on the children's social competence. BBDI seems to have the deepest, widest, and therefore probably the most enduring therapeutic effect on children with NVD. Copyright © 2013 The Voice Foundation

  17. Modeling the Effects of Trait Diversity on Short-term Adaptive Capacity and Long-term Productivity of Phytoplankton Communities

    Smith, S. L.; Vallina, S. M.; Merico, A.

    2016-02-01

    We examine Biodiversity and Ecosystem Function (BEF) in a model phytoplankton community, using two recently developed mechanisms for sustaining diversity. The Trait Diffusion (TD) formulation represents the maintenance of diversity via endogenous mechanisms, such as inter-generational trait plasticity and rapid evolution. The 'Kill-the-Winner' (KTW) formulation for grazing sustains prey biodiversity via the exogenous mechanism of active prey switching. We implement both TD and KTW in a continuous trait-distribution model using simplified size-scalings to define a gleaner-opportunist trade-off for a phytoplankton community. By simulating semi-continuous culture experiments with periodic external dilutions, we test the dynamic response of the phytoplankton community to different scenarios of pulsed nutrient supply. We quantify the short-term Adaptive Capacity (AC) of the community by the specific growth rate averaged over the first 3 days of perturbations, and the Long-term Productivity (LP) by its average over the entire 120 day period of perturbations. When either the frequency or intensity of pulses is low, both AC and LP tend to decrease with diversity (and vice versa). Trait diversity has more impact on AC, particularly for pulses of high frequency or intensity, for which it tends to increase gradually at first, then steeply, and then to saturate with increasing diversity. For pulses of moderate intensity and frequency, increasing trait diversity from low to moderate levels leads to a trade-off between enhancing AC while slightly decreasing LP. Ultimately, we find that sustaining diversity increases the speed at which the phytoplankton community changes its composition in terms of size and hence nutrient acquisition traits, which may have implications for the transfer of productivity through the foodweb.

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

    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

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

    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 GABA A receptor (GABA A R) antagonists in adult animals, but none of the compounds investigated are approved for human use. We explored the therapeutic potential of flumazenil (FLUM), a GABA A R 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.

  20. Long- and short-term exposure to PM2.5 and mortality: using novel exposure models.

    Kloog, Itai; Ridgway, Bill; Koutrakis, Petros; Coull, Brent A; Schwartz, Joel D

    2013-07-01

    Many studies have reported associations between ambient particulate matter (PM) and adverse health effects, focused on either short-term (acute) or long-term (chronic) PM exposures. For chronic effects, the studied cohorts have rarely been representative of the population. We present a novel exposure model combining satellite aerosol optical depth and land-use data to investigate both the long- and short-term effects of PM2.5 exposures on population mortality in Massachusetts, United States, for the years 2000-2008. All deaths were geocoded. We performed two separate analyses: a time-series analysis (for short-term exposure) where counts in each geographic grid cell were regressed against cell-specific short-term PM2.5 exposure, temperature, socioeconomic data, lung cancer rates (as a surrogate for smoking), and a spline of time (to control for season and trends). In addition, for long-term exposure, we performed a relative incidence analysis using two long-term exposure metrics: regional 10 × 10 km PM2.5 predictions and local deviations from the cell average based on land use within 50 m of the residence. We tested whether these predicted the proportion of deaths from PM-related causes (cardiovascular and respiratory diseases). For short-term exposure, we found that for every 10-µg/m increase in PM 2.5 exposure there was a 2.8% increase in PM-related mortality (95% confidence interval [CI] = 2.0-3.5). For the long-term exposure at the grid cell level, we found an odds ratio (OR) for every 10-µg/m increase in long-term PM2.5 exposure of 1.6 (CI = 1.5-1.8) for particle-related diseases. Local PM2.5 had an OR of 1.4 (CI = 1.3-1.5), which was independent of and additive to the grid cell effect. We have developed a novel PM2.5 exposure model based on remote sensing data to assess both short- and long-term human exposures. Our approach allows us to gain spatial resolution in acute effects and an assessment of long-term effects in the entire population rather than a

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

    Qiang Shang

    Full Text Available 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.

  2. Short-term to seasonal variability in factors driving primary productivity in a shallow estuary: Implications for modeling production

    Canion, Andy; MacIntyre, Hugh L.; Phipps, Scott

    2013-10-01

    The inputs of primary productivity models may be highly variable on short timescales (hourly to daily) in turbid estuaries, but modeling of productivity in these environments is often implemented with data collected over longer timescales. Daily, seasonal, and spatial variability in primary productivity model parameters: chlorophyll a concentration (Chla), the downwelling light attenuation coefficient (kd), and photosynthesis-irradiance response parameters (Pmchl, αChl) were characterized in Weeks Bay, a nitrogen-impacted shallow estuary in the northern Gulf of Mexico. Variability in primary productivity model parameters in response to environmental forcing, nutrients, and microalgal taxonomic marker pigments were analysed in monthly and short-term datasets. Microalgal biomass (as Chla) was strongly related to total phosphorus concentration on seasonal scales. Hourly data support wind-driven resuspension as a major source of short-term variability in Chla and light attenuation (kd). The empirical relationship between areal primary productivity and a combined variable of biomass and light attenuation showed that variability in the photosynthesis-irradiance response contributed little to the overall variability in primary productivity, and Chla alone could account for 53-86% of the variability in primary productivity. Efforts to model productivity in similar shallow systems with highly variable microalgal biomass may benefit the most by investing resources in improving spatial and temporal resolution of chlorophyll a measurements before increasing the complexity of models used in productivity modeling.

  3. The Achievement of Therapeutic Objectives Scale: Interrater Reliability and Sensitivity to Change in Short-Term Dynamic Psychotherapy and Cognitive Therapy

    Valen, Jakob; Ryum, Truls; Svartberg, Martin; Stiles, Tore C.; McCullough, Leigh

    2011-01-01

    This study examined interrater reliability and sensitivity to change of the Achievement of Therapeutic Objectives Scale (ATOS; McCullough, Larsen, et al., 2003) in short-term dynamic psychotherapy (STDP) and cognitive therapy (CT). The ATOS is a process scale originally developed to assess patients' achievements of treatment objectives in STDP,…

  4. Short-term dissolved organic carbon dynamics reflect water management and precipitation patterns in a subtropical estuary

    Peter Regier

    2016-12-01

    a better understanding of short-term DOC dynamics and associated hydrological drivers and indicates the importance of high-frequency measurements to accurately constraining coastal carbon processes and budgets, particularly in coastal systems increasingly altered by hydrologic restoration and climate change.

  5. A New Empirical Model for Short-Term Forecasting of the Broadband Penetration: A Short Research in Greece

    Salpasaranis Konstantinos

    2011-01-01

    Full Text Available The objective of this paper is to present a short research about the overall broadband penetration in Greece. In this research, a new empirical deterministic model is proposed for the short-term forecast of the cumulative broadband adoption. The fitting performance of the model is compared with some widely used diffusion models for the cumulative adoption of new telecommunication products, namely, Logistic, Gompertz, Flexible Logistic (FLOG, Box-Cox, Richards, and Bass models. The fitting process is done with broadband penetration official data for Greece. In conclusion, comparing these models with the empirical model, it could be argued that the latter yields well enough statistics indicators for fitting and forecasting performance. It also stresses the need for further research and performance analysis of the model in other more mature broadband markets.

  6. Short-Term Gains, Long-Term Pains: How Cues about State Aid Learning in Dynamic Environments

    Gureckis, Todd M.; Love, Bradley C.

    2009-01-01

    Successful investors seeking returns, animals foraging for food, and pilots controlling aircraft all must take into account how their current decisions will impact their future standing. One challenge facing decision makers is that options that appear attractive in the short-term may not turn out best in the long run. In this paper, we explore…

  7. Comparison of Two Predictive Models for Short-Term Mortality in Patients after Severe Traumatic Brain Injury.

    Kesmarky, Klara; Delhumeau, Cecile; Zenobi, Marie; Walder, Bernhard

    2017-07-15

    The Glasgow Coma Scale (GCS) and the Abbreviated Injury Score of the head region (HAIS) are validated prognostic factors in traumatic brain injury (TBI). The aim of this study was to compare the prognostic performance of an alternative predictive model including motor GCS, pupillary reactivity, age, HAIS, and presence of multi-trauma for short-term mortality with a reference predictive model including motor GCS, pupil reaction, and age (IMPACT core model). A secondary analysis of a prospective epidemiological cohort study in Switzerland including patients after severe TBI (HAIS >3) with the outcome death at 14 days was performed. Performance of prediction, accuracy of discrimination (area under the receiver operating characteristic curve [AUROC]), calibration, and validity of the two predictive models were investigated. The cohort included 808 patients (median age, 56; interquartile range, 33-71), median GCS at hospital admission 3 (3-14), abnormal pupil reaction 29%, with a death rate of 29.7% at 14 days. The alternative predictive model had a higher accuracy of discrimination to predict death at 14 days than the reference predictive model (AUROC 0.852, 95% confidence interval [CI] 0.824-0.880 vs. AUROC 0.826, 95% CI 0.795-0.857; p predictive model had an equivalent calibration, compared with the reference predictive model Hosmer-Lemeshow p values (Chi2 8.52, Hosmer-Lemeshow p = 0.345 vs. Chi2 8.66, Hosmer-Lemeshow p = 0.372). The optimism-corrected value of AUROC for the alternative predictive model was 0.845. After severe TBI, a higher performance of prediction for short-term mortality was observed with the alternative predictive model, compared with the reference predictive model.

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

    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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

    Wang, Bo; Tai, Neng-ling; Zhai, Hai-qing; Ye, Jian; Zhu, Jia-dong; Qi, Liang-bo

    2008-01-01

    In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (author)

  10. Short-term spheroid culture of primary colorectal cancer cells as an in vitro model for personalizing cancer medicine

    Jeppesen, Maria; Hagel, Grith; Glenthoj, Anders

    2017-01-01

    Chemotherapy treatment of cancer remains a challenge due to the molecular and functional heterogeneity displayed by tumours originating from the same cell type. The pronounced heterogeneity makes it difficult for oncologists to devise an effective therapeutic strategy for the patient. One approac...... and combinations most commonly used for treatment of colorectal cancer. In summary, short-term spheroid culture of primary colorectal adenocarcinoma cells represents a promising in vitro model for use in personalized medicine....... for increasing treatment efficacy is to test the chemosensitivity of cancer cells obtained from the patient's tumour. 3D culture represents a promising method for modelling patient tumours in vitro. The aim of this study was therefore to evaluate how closely short-term spheroid cultures of primary colorectal...... cancer cells resemble the original tumour. Colorectal cancer cells were isolated from human tumour tissue and cultured as spheroids. Spheroid cultures were established with a high success rate and remained viable for at least 10 days. The spheroids exhibited significant growth over a period of 7 days...

  11. Burst-induced anti-Hebbian depression acts through short-term synaptic dynamics to cancel redundant sensory signals.

    Harvey-Girard, Erik; Lewis, John; Maler, Leonard

    2010-04-28

    Weakly electric fish can enhance the detection and localization of important signals such as those of prey in part by cancellation of redundant spatially diffuse electric signals due to, e.g., their tail bending. The cancellation mechanism is based on descending input, conveyed by parallel fibers emanating from cerebellar granule cells, that produces a negative image of the global low-frequency signals in pyramidal cells within the first-order electrosensory region, the electrosensory lateral line lobe (ELL). Here we demonstrate that the parallel fiber synaptic input to ELL pyramidal cell undergoes long-term depression (LTD) whenever both parallel fiber afferents and their target cells are stimulated to produce paired burst discharges. Paired large bursts (4-4) induce robust LTD over pre-post delays of up to +/-50 ms, whereas smaller bursts (2-2) induce weaker LTD. Single spikes (either presynaptic or postsynaptic) paired with bursts did not induce LTD. Tetanic presynaptic stimulation was also ineffective in inducing LTD. Thus, we have demonstrated a form of anti-Hebbian LTD that depends on the temporal correlation of burst discharge. We then demonstrated that the burst-induced LTD is postsynaptic and requires the NR2B subunit of the NMDA receptor, elevation of postsynaptic Ca(2+), and activation of CaMKIIbeta. A model incorporating local inhibitory circuitry and previously identified short-term presynaptic potentiation of the parallel fiber synapses further suggests that the combination of burst-induced LTD, presynaptic potentiation, and local inhibition may be sufficient to explain the generation of the negative image and cancellation of redundant sensory input by ELL pyramidal cells.

  12. A model of microsaccade-related neural responses induced by short-term depression in thalamocortical synapses

    Wujie eYuan

    2013-04-01

    Full Text Available Microsaccades during fixation have been suggested to counteract visual fading. Recent experi- ments have also observed microsaccade-related neural responses from cellular record, scalp elec- troencephalogram (EEG and functional magnetic resonance imaging (fMRI. The underlying mechanism, however, is not yet understood and highly debated. It has been proposed that the neural activity of primary visual cortex (V1 is a crucial component for counteracting visual adaptation. In this paper, we use computational modeling to investigate how short-term depres- sion (STD in thalamocortical synapses might affect the neural responses of V1 in the presence of microsaccades. Our model not only gives a possible synaptic explanation for microsaccades in counteracting visual fading, but also reproduces several features in experimental findings. These modeling results suggest that STD in thalamocortical synapses plays an important role in microsaccade-related neural responses and the model may be useful for further investigation of behavioral properties and functional roles of microsaccades.

  13. A model of microsaccade-related neural responses induced by short-term depression in thalamocortical synapses

    Yuan, Wu-Jie; Dimigen, Olaf; Sommer, Werner; Zhou, Changsong

    2013-01-01

    Microsaccades during fixation have been suggested to counteract visual fading. Recent experiments have also observed microsaccade-related neural responses from cellular record, scalp electroencephalogram (EEG), and functional magnetic resonance imaging (fMRI). The underlying mechanism, however, is not yet understood and highly debated. It has been proposed that the neural activity of primary visual cortex (V1) is a crucial component for counteracting visual adaptation. In this paper, we use computational modeling to investigate how short-term depression (STD) in thalamocortical synapses might affect the neural responses of V1 in the presence of microsaccades. Our model not only gives a possible synaptic explanation for microsaccades in counteracting visual fading, but also reproduces several features in experimental findings. These modeling results suggest that STD in thalamocortical synapses plays an important role in microsaccade-related neural responses and the model may be useful for further investigation of behavioral properties and functional roles of microsaccades. PMID:23630494

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

    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.

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

    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...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  16. Linear and non-linear autoregressive models for short-term wind speed forecasting

    Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.

    2016-01-01

    Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.

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

    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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  18. Mitigation of short-term disturbance negative impacts in the agent-based model of a production companies network

    Shevchuk, G. K.; Berg, D. B.; Zvereva, O. M.; Medvedeva, M. A.

    2017-11-01

    This article is devoted to the study of a supply chain disturbance impact on manufacturing volumes in a production system network. Each network agent's product can be used as a resource by other system agents (manufacturers). A supply chain disturbance can lead to operating cease of the entire network. Authors suggest using of short-term partial resources reservation to mitigate negative consequences of such disturbances. An agent-based model with a reservation algorithm compatible with strategies for resource procurement in terms of financial constraints was engineered. This model works in accordance with the static input-output Leontief 's model. The results can be used for choosing the ways of system's stability improving, and protecting it from various disturbances and imbalance.

  19. Short-term electricity price forecast based on the improved hybrid model

    Dong Yao; Wang Jianzhou; Jiang He; Wu Jie

    2011-01-01

    Highlights: → The proposed models can detach high volatility and daily seasonality of electricity price. → The improved hybrid forecast models can make full use of the advantages of individual models. → The proposed models create commendable improvements that are relatively satisfactorily for current research. → The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

  20. DIAPARK. A theoretical model for radiological assessments after short-term release and deposition of radionuclides

    Eklund, J.; Mueller, H.; Proehl, G.; Jacob, P.

    1995-05-01

    The DIAPARK program package is an enhancement of the ECOSYS-87 model for radioecological assessment, developed by GSF. The program version of the ECOSYS-87 model covers primarily the results and information derived from measurements performed after the Chernobyl reactor accident. Much importance has been attached to making the model fit for customized adjustment by modification of model parameters in order to be able to take into account the various conditions in various regions. The program package allows calculation of the deposition and interception, food contamination (as determined by plant contamination, contamination of animal products, radioactivity levels changed by storage and processing), doses resulting from ingestion and inhalation, and external exposure. (HP) [de

  1. Short-term electricity price forecast based on the improved hybrid model

    Dong Yao, E-mail: dongyao20051987@yahoo.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Wang Jianzhou, E-mail: wjz@lzu.edu.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Jiang He; Wu Jie [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China)

    2011-08-15

    Highlights: {yields} The proposed models can detach high volatility and daily seasonality of electricity price. {yields} The improved hybrid forecast models can make full use of the advantages of individual models. {yields} The proposed models create commendable improvements that are relatively satisfactorily for current research. {yields} The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

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

    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...

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

    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.

  4. Risk adjustment models for short-term outcomes after surgical resection for oesophagogastric cancer.

    Fischer, C; Lingsma, H; Hardwick, R; Cromwell, D A; Steyerberg, E; Groene, O

    2016-01-01

    Outcomes for oesophagogastric cancer surgery are compared with the aim of benchmarking quality of care. Adjusting for patient characteristics is crucial to avoid biased comparisons between providers. The study objective was to develop a case-mix adjustment model for comparing 30- and 90-day mortality and anastomotic leakage rates after oesophagogastric cancer resections. The study reviewed existing models, considered expert opinion and examined audit data in order to select predictors that were consequently used to develop a case-mix adjustment model for the National Oesophago-Gastric Cancer Audit, covering England and Wales. Models were developed on patients undergoing surgical resection between April 2011 and March 2013 using logistic regression. Model calibration and discrimination was quantified using a bootstrap procedure. Most existing risk models for oesophagogastric resections were methodologically weak, outdated or based on detailed laboratory data that are not generally available. In 4882 patients with oesophagogastric cancer used for model development, 30- and 90-day mortality rates were 2·3 and 4·4 per cent respectively, and 6·2 per cent of patients developed an anastomotic leak. The internally validated models, based on predictors selected from the literature, showed moderate discrimination (area under the receiver operating characteristic (ROC) curve 0·646 for 30-day mortality, 0·664 for 90-day mortality and 0·587 for anastomotic leakage) and good calibration. Based on available data, three case-mix adjustment models for postoperative outcomes in patients undergoing curative surgery for oesophagogastric cancer were developed. These models should be used for risk adjustment when assessing hospital performance in the National Health Service, and tested in other large health systems. © 2015 BJS Society Ltd Published by John Wiley & Sons Ltd.

  5. A nested-grid limited-area model for short term weather forecasting

    Wong, V. C.; Zack, J. W.; Kaplan, M. L.; Coats, G. D.

    1983-01-01

    The present investigation is concerned with a mesoscale atmospheric simulation system (MASS), incorporating the sigma-coordinate primitive equations. The present version of this model (MASS 3.0) has 14 vertical layers, with the upper boundary at 100 mb. There are 128 x 96 grid points in each layer. The earlier version of this model (MASS 2.0) has been described by Kaplan et al. (1982). The current investigation provides a summary of major revisions to that version and a description of the parameterization schemes which are presently included in the model. The planetary boundary layer (PBL) is considered, taking into account aspects of generalized similarity theory and free convection, the surface energy budget, the surface moisture budget, and prognostic equations for the depth h of the PBL. A cloud model is discussed, giving attention to stable precipitation, and cumulus convection.

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

    Nguyen, Hang T.; Nabney, Ian T.

    2010-01-01

    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)

  7. A continuous time model for a short-term multiproduct batch process scheduling

    Jenny Díaz Ramírez

    2018-01-01

    Full Text Available In the chemical industry, it is common to find production systems characterized by having a single stage or a previously identified bottleneck stage, with multiple non-identical parallel stations and with setup costs that depend on the production sequence. This paper proposes a mixed integer production-scheduling model that identifies lot size and product sequence that maximize profit. It considers multiple typical industry conditions, such as penalties for noncompliance or out of service periods of the productive units (or stations for preventive maintenance activities. The model was validated with real data from an oil chemical company.  Aiming to analyze its performance, we applied the model to 155 instances of production, which were obtained using Monte Carlo technique on the historical production data of the same company.  We obtained an average 12 % reduction in the total cost of production and a 19 % increase in the estimated profit.

  8. A planning model for the short-term management of cash.

    Broyles, Robert W; Mattachione, Steven; Khaliq, Amir

    2011-02-01

    This paper develops a model that enables the health administrator to identify the balance that minimizes the projected cost of holding cash. Adopting the principles of mathematical expectation, the model estimates the expected total costs of adopting each of the several strategies concerning the cash balance that the organization might maintain. Expected total costs consist of anticipated short costs, resulting from a potential shortage of funds. Long costs are associated with a potential surplus of funds and an opportunity cost represented by foregone investment income. Of importance to the model is the potential for the health service organization to realize a surplus of funds during periods characterized by a net cash disbursement. The paper also develops an interactive spreadsheet that enables the administrator to perform sensitivity analysis and examine the response of the desired or target cash balance to changes in the parameters that define the expected long and short cost functions.

  9. Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model

    Wu, Jie; Wang, Jianzhou; Lu, Haiyan; Dong, Yao; Lu, Xiaoxiao

    2013-01-01

    Highlights: ► The seasonal and trend items of the data series are forecasted separately. ► Seasonal item in the data series is verified by the Kendall τ correlation testing. ► Different regression models are applied to the trend item forecasting. ► We examine the superiority of the combined models by the quartile value comparison. ► Paired-sample T test is utilized to confirm the superiority of the combined models. - Abstract: For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall τ correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests

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

    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

  11. Overview, comparative assessment and recommendations of forecasting models for short-term water demand prediction

    Anele, AO

    2017-11-01

    Full Text Available -term water demand (STWD) forecasts. In view of this, an overview of forecasting methods for STWD prediction is presented. Based on that, a comparative assessment of the performance of alternative forecasting models from the different methods is studied. Times...

  12. Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks

    Mahmoud, Thair S.; Habibi, Daryoush; Hassan, Mohammed Y.; Bass, Octavian

    2015-01-01

    Highlights: • A novel Short Term Medium Voltage (MV) Load Forecasting (STLF) model is presented. • A knowledge-based STLF error control mechanism is implemented. • An Artificial Neural Network (ANN)-based optimum tuning is applied on STLF. • The relationship between load profiles and operational conditions is analysed. - Abstract: This paper presents an intelligent mechanism for Short Term Load Forecasting (STLF) models, which allows self-adaptation with respect to the load operational conditions. Specifically, a knowledge-based FeedBack Tunning Fuzzy System (FBTFS) is proposed to instantaneously correlate the information about the demand profile and its operational conditions to make decisions for controlling the model’s forecasting error rate. To maintain minimum forecasting error under various operational scenarios, the FBTFS adaptation was optimised using a Multi-Layer Perceptron Artificial Neural Network (MLPANN), which was trained using Backpropagation algorithm, based on the information about the amount of error and the operational conditions at time of forecasting. For the sake of comparison and performance testing, this mechanism was added to the conventional forecasting methods, i.e. Nonlinear AutoRegressive eXogenous-Artificial Neural Network (NARXANN), Fuzzy Subtractive Clustering Method-based Adaptive Neuro Fuzzy Inference System (FSCMANFIS) and Gaussian-kernel Support Vector Machine (GSVM), and the measured forecasting error reduction average in a 12 month simulation period was 7.83%, 8.5% and 8.32% respectively. The 3.5 MW variable load profile of Edith Cowan University (ECU) in Joondalup, Australia, was used in the modelling and simulations of this model, and the data was provided by Western Power, the transmission and distribution company of the state of Western Australia.

  13. Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

    B. M. Brentan

    2017-01-01

    Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

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

    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.

  15. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network

    Yu, Feng; Xu, Xiaozhong

    2014-01-01

    Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms

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

    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.

  17. Selective surface oxidation and segregation upon short term annealing of model alloys and industrial steel grades

    Swaminathan, S.

    2007-07-01

    Segregation and selective oxidation phenomena of minor alloying elements during annealing of steel sheets lead to the formation of bare spots after hot-dip galvanizing. This thesis highlights the influence of annealing conditions and the effect of alloying elements on the selective oxidation in model alloys and some industrial steel grades. Model alloys of binary (Fe-2Si, Fe-2Mn, Fe-0.8Cr), ternary (Fe-2Mn-2Si, Fe-2Mn-0.8Cr, Fe-1Mn-0.8Cr, Fe-1Si-0.8Cr, Fe-2Si-0.8Cr) and quarternary (Fe-2Mn-2Si-0.8Cr) systems were studied. In the case of steels, standard grade interstitial free (IF) steels and experimental grade tensile strength 1000 MPa steel were investigated. All specimens were annealed at 820 C in N{sub 2}-5%H{sub 2} gas atmospheres with the wide range of dew points (i.e. -80 to 0 C). The surface chemistry after annealing and its wettability with liquid Zn have been correlated as a function of dew points by simulating the hot-dip galvanizing process at laboratory scale. (orig.)

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

    Serban, Ioan; Teodorescu, Remus; Marinescu, Corneliu

    2013-01-01

    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...

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

    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.

  20. Initial Experience With the New Ahmed Glaucoma Valve Model M4: Short-term Results.

    Cvintal, Victor; Moster, Marlene R; Shyu, Andrew P; McDermott, Katie; Ekici, Feyzahan; Pro, Michael J; Waisbourd, Michael

    2016-05-01

    To evaluate the clinical outcomes of the new Ahmed glaucoma valve (AGV) model M4. The device consists of a porous polyethylene shell designed for improved tissue integration and reduced encapsulation of the plate for better intraocular pressure (IOP) control. Medical records of patients with an AGV M4 implantation between December 1, 2012 and December 31, 2013 were reviewed. The main outcome measure was surgical failure, defined as either (1) IOP21 mm Hg and/or glaucoma, and/or (3) loss of light perception. Seventy-five eyes of 73 patients were included. Postoperative IOP at all follow-up visits significantly decreased from a baseline IOP of 31.2 mm Hg (P<0.01). However, IOP increased significantly at 3 months (20.4 mm Hg), 6 months (19.3 mm Hg), and 12 months (20.3 mm Hg) compared with 1 month (13.8 mm Hg) postoperatively (P<0.05). At 6 months and 1 year, the cumulative probability of failure was 32% and 72%, respectively. The AGV M4 effectively reduced IOP in the first postoperative month, but IOP steadily increased thereafter. Consequently, failure rates were high after 1 year of follow-up.

  1. Modelling short term individual exposure from airborne hazardous releases in urban environments

    Bartzis, J.G.; Efthimiou, G.C.; Andronopoulos, S.

    2015-01-01

    Highlights: • The statistical behavior of the variability of individual exposure is described with a beta function. • The extreme value in the beta function is properly addressed by [5] correlation. • Two different datasets gave clear support to the proposed novel theory and its hypotheses. - Abstract: A key issue, in order to be able to cope with deliberate or accidental atmospheric releases of hazardous substances, is the ability to reliably predict the individual exposure downstream the source. In many situations, the release time and/or the health relevant exposure time is short compared to mean concentration time scales. In such a case, a significant scatter of exposure levels is expected due to the stochastic nature of turbulence. The problem becomes even more complex when dispersion occurs over urban environments. The present work is the first attempt to approximate on generic terms, the statistical behavior of the abovementioned variability with a beta distribution probability density function (beta-pdf) which has proved to be quite successful. The important issue of the extreme concentration value in beta-pdf seems to be properly addressed by the [5] correlation in which global values of its associated constants are proposed. Two substantially different datasets, the wind tunnel Michelstadt experiment and the field Mock Urban Setting Trial (MUST) experiment gave clear support to the proposed novel theory and its hypotheses. In addition, the present work can be considered as basis for further investigation and model refinements.

  2. Modelling short term individual exposure from airborne hazardous releases in urban environments

    Bartzis, J.G., E-mail: bartzis@uowm.gr [University of Western Macedonia, Dept. of Mechanical Engineering, Sialvera & Bakola Str., 50100, Kozani (Greece); Efthimiou, G.C.; Andronopoulos, S. [Environmental Research Laboratory, INRASTES, NCSR Demokritos, Patriarchou Grigoriou & Neapoleos Str., 15310, Aghia Paraskevi (Greece)

    2015-12-30

    Highlights: • The statistical behavior of the variability of individual exposure is described with a beta function. • The extreme value in the beta function is properly addressed by [5] correlation. • Two different datasets gave clear support to the proposed novel theory and its hypotheses. - Abstract: A key issue, in order to be able to cope with deliberate or accidental atmospheric releases of hazardous substances, is the ability to reliably predict the individual exposure downstream the source. In many situations, the release time and/or the health relevant exposure time is short compared to mean concentration time scales. In such a case, a significant scatter of exposure levels is expected due to the stochastic nature of turbulence. The problem becomes even more complex when dispersion occurs over urban environments. The present work is the first attempt to approximate on generic terms, the statistical behavior of the abovementioned variability with a beta distribution probability density function (beta-pdf) which has proved to be quite successful. The important issue of the extreme concentration value in beta-pdf seems to be properly addressed by the [5] correlation in which global values of its associated constants are proposed. Two substantially different datasets, the wind tunnel Michelstadt experiment and the field Mock Urban Setting Trial (MUST) experiment gave clear support to the proposed novel theory and its hypotheses. In addition, the present work can be considered as basis for further investigation and model refinements.

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

    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.

  4. Identifying attachment ruptures underlying severe music performance anxiety in a professional musician undertaking an assessment and trial therapy of Intensive Short-Term Dynamic Psychotherapy (ISTDP).

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

    2016-01-01

    Kenny has proposed that severe music performance anxiety that is unresponsive to usual treatments such as cognitive-behaviour therapy may be one manifestation of unresolved attachment ruptures in early life. Intensive Short-Term Dynamic Psychotherapy specifically targets early relationship trauma. Accordingly, a trial of Intensive Short-Term Dynamic Psychotherapy with severely anxious musicians was implemented to assess whether resolution of attachment ruptures resulted in clinically significant relief from music performance anxiety. Volunteer musicians participating in a nationally funded study were screened for MPA severity. Those meeting the critical cut-off score on the Kenny Music Performance Anxiety Inventory were offered a trial of Intensive Short-Term Dynamic Psychotherapy. In this paper, we present the theoretical foundations and rationale for the treatment approach, followed by sections of a verbatim transcript and process analysis of the assessment phase of treatment that comprised a 3-h trial therapy session. The 'case' was a professional orchestral musician (male, aged 55) who had suffered severe music performance anxiety over the course of his entire career, which spanned more than 30 years at the time he presented for treatment following his failure to secure a position at audition. The participant was able to access the pain, rage and grief associated with unresolved attachment ruptures with both parents that demonstrated the likely nexus between early attachment trauma and severe music performance anxiety. Intensive Short-Term Dynamic Psychotherapy is a potentially cost-effective treatment for severe music performance anxiety. Further research using designs with higher levels of evidence are required before clinical recommendations can be made for the use of this therapy with this population.

  5. Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize.

    Chenu, Karine; Chapman, Scott C; Hammer, Graeme L; McLean, Greg; Salah, Halim Ben Haj; Tardieu, François

    2008-03-01

    Physiological and genetic studies of leaf growth often focus on short-term responses, leaving a gap to whole-plant models that predict biomass accumulation, transpiration and yield at crop scale. To bridge this gap, we developed a model that combines an existing model of leaf 6 expansion in response to short-term environmental variations with a model coordinating the development of all leaves of a plant. The latter was based on: (1) rates of leaf initiation, appearance and end of elongation measured in field experiments; and (2) the hypothesis of an independence of the growth between leaves. The resulting whole-plant leaf model was integrated into the generic crop model APSIM which provided dynamic feedback of environmental conditions to the leaf model and allowed simulation of crop growth at canopy level. The model was tested in 12 field situations with contrasting temperature, evaporative demand and soil water status. In observed and simulated data, high evaporative demand reduced leaf area at the whole-plant level, and short water deficits affected only leaves developing during the stress, either visible or still hidden in the whorl. The model adequately simulated whole-plant profiles of leaf area with a single set of parameters that applied to the same hybrid in all experiments. It was also suitable to predict biomass accumulation and yield of a similar hybrid grown in different conditions. This model extends to field conditions existing knowledge of the environmental controls of leaf elongation, and can be used to simulate how their genetic controls flow through to yield.

  6. A possibilistic model to determine the cost of environmental quality in mid/short term planning of an electricity distribution system

    Schweickardt, Gustavo Alejandro; Gimenez Alvarez, Juan Manuel

    2012-01-01

    This work presents a Possibilistic Optimization Model to determine the Dynamic Environmental Quality Cost, applied on an Electricity Distribution System and measured as Network System Visual Impact. The Mid/Short Term Planning is the Regulatory Control Period. A multicriteria optimization approach is proposed, and for each criterion, non-stochastic uncertainties are recognized and represented by mean the introduction of Fuzzy Sets. In this way, a possibility in the occurrence of criteria variables values is established. In addition, as consequence of uncertainties of criteria preference ranking, a Model to obtain criteria Priority Vector is introduced. The Environmental Quality Cost determination is based in the relationship between the Investment Cost and an Impact Index of Network System Environmental Quality, proposed in this work. Finally, a simulation on a real system and the most important conclusions are presented.

  7. Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models

    Truong, Nguyen-Vu; Wang, Liuping; Wong, Peter K.C.

    2008-01-01

    Power demand forecasting is of vital importance to the management and planning of power system operations which include generation, transmission, distribution, as well as system's security analysis and economic pricing processes. This paper concerns the modeling and short-term forecast of daily peak power demand in the state of Victoria, Australia. In this study, a two-dimensional wavelet based state dependent parameter (SDP) modelling approach is used to produce a compact mathematical model for this complex nonlinear dynamic system. In this approach, a nonlinear system is expressed by a set of linear regressive input and output terms (state variables) multiplied by the respective state dependent parameters that carry the nonlinearities in the form of 2-D wavelet series expansions. This model is identified based on historical data, descriptively representing the relationship and interaction between various components which affect the peak power demand of a certain day. The identified model has been used to forecast daily peak power demand in the state of Victoria, Australia in the time period from the 9th of August 2007 to the 24th of August 2007. With a MAPE (mean absolute prediction error) of 1.9%, it has clearly implied the effectiveness of the identified model. (author)

  8. A Modified LS+AR Model to Improve the Accuracy of the Short-term Polar Motion Prediction

    Wang, Z. W.; Wang, Q. X.; Ding, Y. Q.; Zhang, J. J.; Liu, S. S.

    2017-03-01

    There are two problems of the LS (Least Squares)+AR (AutoRegressive) model in polar motion forecast: the inner residual value of LS fitting is reasonable, but the residual value of LS extrapolation is poor; and the LS fitting residual sequence is non-linear. It is unsuitable to establish an AR model for the residual sequence to be forecasted, based on the residual sequence before forecast epoch. In this paper, we make solution to those two problems with two steps. First, restrictions are added to the two endpoints of LS fitting data to fix them on the LS fitting curve. Therefore, the fitting values next to the two endpoints are very close to the observation values. Secondly, we select the interpolation residual sequence of an inward LS fitting curve, which has a similar variation trend as the LS extrapolation residual sequence, as the modeling object of AR for the residual forecast. Calculation examples show that this solution can effectively improve the short-term polar motion prediction accuracy by the LS+AR model. In addition, the comparison results of the forecast models of RLS (Robustified Least Squares)+AR, RLS+ARIMA (AutoRegressive Integrated Moving Average), and LS+ANN (Artificial Neural Network) confirm the feasibility and effectiveness of the solution for the polar motion forecast. The results, especially for the polar motion forecast in the 1-10 days, show that the forecast accuracy of the proposed model can reach the world level.

  9. Short-term light and leaf photosynthetic dynamics affect estimates of daily understory photosynthesis in four tree species.

    Naumburg, Elke; Ellsworth, David S

    2002-04-01

    Instantaneous measurements of photosynthesis are often implicitly or explicitly scaled to longer time frames to provide an understanding of plant performance in a given environment. For plants growing in a forest understory, results from photosynthetic light response curves in conjunction with diurnal light data are frequently extrapolated to daily photosynthesis (A(day)), ignoring dynamic photosynthetic responses to light. In this study, we evaluated the importance of two factors on A(day) estimates: dynamic physiological responses to photosynthetic photon flux density (PPFD); and time-resolution of the PPFD data used for modeling. We used a dynamic photosynthesis model to investigate how these factors interact with species-specific photosynthetic traits, forest type, and sky conditions to affect the accuracy of A(day) predictions. Increasing time-averaging of PPFD significantly increased the relative overestimation of A(day) similarly for all study species because of the nonlinear response of photosynthesis to PPFD (15% with 5-min PPFD means). Depending on the light environment characteristics and species-specific dynamic responses to PPFD, understory tree A(day) can be overestimated by 6-42% for the study species by ignoring these dynamics. Although these overestimates decrease under cloudy conditions where direct sunlight and consequently understory sunfleck radiation is reduced, they are still significant. Within a species, overestimation of A(day) as a result of ignoring dynamic responses was highly dependent on daily sunfleck PPFD and the frequency and irradiance of sunflecks. Overall, large overestimates of A(day) in understory trees may cause misleading inferences concerning species growth and competition in forest understories with sunlight. We conclude that comparisons of A(day) among co-occurring understory species in deep shade will be enhanced by consideration of sunflecks by using high-resolution PPFD data and understanding the physiological

  10. Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting

    Gallego, Cristobal; Pinson, Pierre; Madsen, Henrik

    2011-01-01

    Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some of these e......Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some...... on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power...... transformation process can be introduced by considering the local wind speed. In both cases, conditional parametric models showed a better performance than the one achieved by the regime-switching strategy. The results attained reinforce the idea that each explanatory variable allows the modelling of different...

  11. Probabilistic short-term forecasting of eruption rate at Kīlauea Volcano using a physics-based model

    Anderson, K. R.

    2016-12-01

    Deterministic models of volcanic eruptions yield predictions of future activity conditioned on uncertainty in the current state of the system. Physics-based eruption models are well-suited for deterministic forecasting as they can relate magma physics with a wide range of observations. Yet, physics-based eruption forecasting is strongly limited by an inadequate understanding of volcanic systems, and the need for eruption models to be computationally tractable. At Kīlauea Volcano, Hawaii, episodic depressurization-pressurization cycles of the magma system generate correlated, quasi-exponential variations in ground deformation and surface height of the active summit lava lake. Deflations are associated with reductions in eruption rate, or even brief eruptive pauses, and thus partly control lava flow advance rates and associated hazard. Because of the relatively well-understood nature of Kīlauea's shallow magma plumbing system, and because more than 600 of these events have been recorded to date, they offer a unique opportunity to refine a physics-based effusive eruption forecasting approach and apply it to lava eruption rates over short (hours to days) time periods. A simple physical model of the volcano ascribes observed data to temporary reductions in magma supply to an elastic reservoir filled with compressible magma. This model can be used to predict the evolution of an ongoing event, but because the mechanism that triggers events is unknown, event durations are modeled stochastically from previous observations. A Bayesian approach incorporates diverse data sets and prior information to simultaneously estimate uncertain model parameters and future states of the system. Forecasts take the form of probability distributions for eruption rate or cumulative erupted volume at some future time. Results demonstrate the significant uncertainties that still remain even for short-term eruption forecasting at a well-monitored volcano - but also the value of a physics

  12. Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

    Fei Wang

    2017-12-01

    Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

  13. Short-term streamflow forecasting with global climate change implications A comparative study between genetic programming and neural network models

    Makkeasorn, A.; Chang, N. B.; Zhou, X.

    2008-05-01

    SummarySustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.

  14. What do short-term and long-term relationships look like? Building the relationship coordination and strategic timing (ReCAST) model.

    Eastwick, Paul W; Keneski, Elizabeth; Morgan, Taylor A; McDonald, Meagan A; Huang, Sabrina A

    2018-05-01

    Close relationships research has examined committed couples (e.g., dating relationships, marriages) using intensive methods that plot relationship development over time. But a substantial proportion of people's real-life sexual experiences take place (a) before committed relationships become "official" and (b) in short-term relationships; methods that document the time course of relationships have rarely been applied to these contexts. We adapted a classic relationship trajectory-plotting technique to generate the first empirical comparisons between the features of people's real-life short-term and long-term relationships across their entire timespan. Five studies compared long-term and short-term relationships in terms of the timing of relationship milestones (e.g., flirting, first sexual intercourse) and the occurrence/intensity of important relationship experiences (e.g., romantic interest, strong sexual desire, attachment). As romantic interest was rising and partners were becoming acquainted, long-term and short-term relationships were indistinguishable. Eventually, romantic interest in short-term relationships plateaued and declined while romantic interest in long-term relationships continued to rise, ultimately reaching a higher peak. As relationships progressed, participants evidenced more features characteristic of the attachment-behavioral system (e.g., attachment, caregiving) in long-term than short-term relationships but similar levels of other features (e.g., sexual desire, self-promotion, intrasexual competition). These data inform a new synthesis of close relationships and evolutionary psychological perspectives called the Relationship Coordination and Strategic Timing (ReCAST) model. ReCAST depicts short-term and long-term relationships as partially overlapping trajectories (rather than relationships initiated with distinct strategies) that differ in their progression along a normative relationship development sequence. (PsycINFO Database Record (c

  15. Sodium nitroprusside enhanced cardiopulmonary resuscitation improves short term survival in a porcine model of ischemic refractory ventricular fibrillation.

    Yannopoulos, Demetris; Bartos, Jason A; George, Stephen A; Sideris, George; Voicu, Sebastian; Oestreich, Brett; Matsuura, Timothy; Shekar, Kadambari; Rees, Jennifer; Aufderheide, Tom P

    2017-01-01

    Sodium nitroprusside (SNP) enhanced CPR (SNPeCPR) demonstrates increased vital organ blood flow and survival in multiple porcine models. We developed a new, coronary occlusion/ischemia model of prolonged resuscitation, mimicking the majority of out-of-hospital cardiac arrests presenting with shockable rhythms. SNPeCPR will increase short term (4-h) survival compared to standard 2015 Advanced Cardiac Life Support (ACLS) guidelines in an ischemic refractory ventricular fibrillation (VF), prolonged CPR model. Sixteen anesthetized pigs had the ostial left anterior descending artery occluded leading to ischemic VF arrest. VF was untreated for 5min. Basic life support was performed for 10min. At minute 10 (EMS arrival), animals received either SNPeCPR (n=8) or standard ACLS (n=8). Defibrillation (200J) occurred every 3min. CPR continued for a total of 45min, then the balloon was deflated simulating revascularization. CPR continued until return of spontaneous circulation (ROSC) or a total of 60min, if unsuccessful. SNPeCPR animals received 2mg of SNP at minute 10 followed by 1mg every 5min until ROSC. Standard ACLS animals received 0.5mg epinephrine every 5min until ROSC. Primary endpoints were ROSC and 4-h survival. All SNPeCPR animals (8/8) achieved sustained ROSC versus 2/8 standard ACLS animals within one hour of resuscitation (p=0.04). The 4-h survival was significantly improved with SNPeCPR compared to standard ACLS, 7/8 versus 1/8 respectively, p=0.0019. SNPeCPR significantly improved ROSC and 4-h survival compared with standard ACLS CPR in a porcine model of prolonged ischemic, refractory VF cardiac arrest. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. Hydrological modelling of the Mabengnong catchment in the southeast Tibet with support of short term intensive precipitation observation

    Wang, L.; Zhang, F.; Zhang, H.; Scott, C. A.; Zeng, C.; SHI, X.

    2017-12-01

    Precipitation is one of the crucial inputs for models used to better understand hydrological processes. In high mountain areas, it is a difficult task to obtain a reliable precipitation data set describing the spatial and temporal characteristic due to the limited meteorological observations and high variability of precipitation. This study carries out intensive observation of precipitation in a high mountain catchment in the southeast of the Tibet during July to August 2013. According to the rain gauges set up at different altitudes, it is found that precipitation is greatly influenced by altitude. The observed precipitation is used to depict the precipitation gradient (PG) and hourly distribution (HD), showing that the average duration is around 0.1, 0.8 and 6.0 hours and the average PG is 0.10, 0.28 and 0.26 mm/d/100m for trace, light and moderate rain, respectively. Based on the gridded precipitation derived from the PG and HD and the nearby Linzhi meteorological station at lower altitude, a distributed biosphere hydrological model based on water and energy budgets (WEB-DHM) is applied to simulate the hydrological processes. Beside the observed runoff, MODIS/Terra snow cover area (SCA) data, and MODIS/Terra land surface temperature (LST) data are also used for model calibration and validation. The resulting runoff, SCA and LST simulations are all reasonable. Sensitivity analyses indicate that runoff is greatly underestimated without considering PG, illustrating that short-term intensive precipitation observation contributes to improving hydrological modelling of poorly gauged high mountain catchments.

  17. Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas

    Zhang, Jianfeng; Zhu, Yan; Zhang, Xiaoping; Ye, Ming; Yang, Jinzhong

    2018-06-01

    Predicting water table depth over the long-term in agricultural areas presents great challenges because these areas have complex and heterogeneous hydrogeological characteristics, boundary conditions, and human activities; also, nonlinear interactions occur among these factors. Therefore, a new time series model based on Long Short-Term Memory (LSTM), was developed in this study as an alternative to computationally expensive physical models. The proposed model is composed of an LSTM layer with another fully connected layer on top of it, with a dropout method applied in the first LSTM layer. In this study, the proposed model was applied and evaluated in five sub-areas of Hetao Irrigation District in arid northwestern China using data of 14 years (2000-2013). The proposed model uses monthly water diversion, evaporation, precipitation, temperature, and time as input data to predict water table depth. A simple but effective standardization method was employed to pre-process data to ensure data on the same scale. 14 years of data are separated into two sets: training set (2000-2011) and validation set (2012-2013) in the experiment. As expected, the proposed model achieves higher R2 scores (0.789-0.952) in water table depth prediction, when compared with the results of traditional feed-forward neural network (FFNN), which only reaches relatively low R2 scores (0.004-0.495), proving that the proposed model can preserve and learn previous information well. Furthermore, the validity of the dropout method and the proposed model's architecture are discussed. Through experimentation, the results show that the dropout method can prevent overfitting significantly. In addition, comparisons between the R2 scores of the proposed model and Double-LSTM model (R2 scores range from 0.170 to 0.864), further prove that the proposed model's architecture is reasonable and can contribute to a strong learning ability on time series data. Thus, one can conclude that the proposed model can

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

    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.

  19. Short term depression unmasks the ghost frequency.

    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.

  20. Microbial dynamics in upflow anaerobic sludge blanket (UASB) bioreactor granules in response to short-term changes in substrate feed

    Kovacik, William P.; Scholten, Johannes C.; Culley, David E.; Hickey, Robert; Zhang, Weiwen; Brockman, Fred J.

    2010-08-01

    The complexity and diversity of the microbial communities in biogranules from an upflow anaerobic sludge blanket (UASB) bioreactor were determined in response to short-term changes in substrate feeds. The reactor was fed simulated brewery wastewater (SBWW) (70% ethanol, 15% acetate, 15% propionate) for 1.5 months (phase 1), acetate / sulfate for 2 months (phase 2), acetate-alone for 3 months (phase 3), and then a return to SBWW for 2 months (phase 4). Performance of the reactor remained relatively stable throughout the experiment as shown by COD removal and gas production. 16S rDNA, methanogen-associated mcrA and sulfate reducer-associated dsrAB genes were PCR amplified, then cloned and sequenced. Sequence analysis of 16S clone libraries showed a relatively simple community composed mainly of the methanogenic Archaea (Methanobacterium and Methanosaeta), members of the Green Non-Sulfur (Chloroflexi) group of Bacteria, followed by fewer numbers of Syntrophobacter, Spirochaeta, Acidobacteria and Cytophaga-related Bacterial sequences. Methanogen-related mcrA clone libraries were dominated throughout by Methanobacter and Methanospirillum related sequences. Although not numerous enough to be detected in our 16S rDNA libraries, sulfate reducers were detected in dsrAB clone libraries, with sequences related to Desulfovibrio and Desulfomonile. Community diversity levels (Shannon-Weiner index) generally decreased for all libraries in response to a change from SBWW to acetate-alone feed. But there was a large transitory increase noted in 16S diversity at the two-month sampling on acetate-alone, entirely related to an increase in Bacterial diversity. Upon return to SBWW conditions in phase 4, all diversity measures returned to near phase 1 levels.

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

    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…

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

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

    1998-01-01

    of T-cell lymphomas. Because of the low incidence of spontaneous tumors and the increased sensitivity to N-ethyl-N-nitrosourea-induced carcinogenesis, E mu-pim-1 mice were suggested to be one of the first potential and attractive candidates to be used in short-term carcinogenicity testing...

  3. Development of a dynamic short-term test method for installed thermal solar systems; Entwicklung eines dynamischen Kurzzeittestverfahrens fuer installierte thermische Solaranlagen

    Beikircher, T.; Gut, M.; Kronthaler, P.; Oberdorf, C.; Schoelkopf, W. [Bayerisches Zentrum fuer Angewandte Energieforschung, Muenchen (Germany). Abt. Solarthermie und Biomasse

    1998-12-31

    A short-term test method for in-situ measurement of the collector field, pipelines and heat exchanger of large-surface solar systems was developed at ZAE Bayern, together with the necessary technical systems (mobile acquisition of meteorological data via telemetering, non-invasive surface temperature sensors and ultrasonic volume flow measuring instruments). Dynamic measurements were made for 11 days in a large-surface solar system of the ``Solarthermie 2000`` programme using the new measuring system. In order to optimize the evaluation procedure, the plant was simulated, and the influence of different models and operating conditions during the measurements on the prediction quality was investigated in detail. On this basis, it was possible to predict the long-term collector yield of the ZfS Hilden, which was measured for a period of 7 months, with an error of less than 5%. The method will be validated in two further industrial-scale systems. [Deutsch] Am ZAE Bayern wurde ein Kurzzeittestverfahren zur insitu-Vermessung des Kollektorkreises (Kollektorfeld, Rohrleitungen, Waermetauscher) grosser Solaranlagen entwickelt. Hierzu wurde eine der Aufgabenstellung angepasste Messtechnik entwickelt (Mobile meteorologische Datenerfassung mit Funkbetrieb, nicht-invasive Oberflaechen-Temperaturfuehler und Ultraschall-Volumenstrommessgeraete). Der Kollektorkreis einer grossen Solaranlage aus dem Programm Solarthermie 2000 wurde dynamisch ueber 11 Tage (29.10.-8.11.1997) mit der neuen Messtechnik vermessen. Zur Entwicklung eines geeigneten Auswerteverfahrens wurde die Anlage simulatorisch abgebildet und der Einfluss verschiedener Modellansaetze und der Betriebsbedingungen waehrend des Mess- und Vorhersagezeitraums auf die Vorhersageguete im Detail untersucht. Mit dem entwickelten Verfahren konnte aus der dynamischen Kurzzeitvermessung der in einer Langzeitmessung der ZfS Hilden ueber 7 Monate ermittelte Kollektorertrag nach dem solaren Waermetauscher auf deutlich besser als 5

  4. Transcranial direct current stimulation improves short-term memory in an animal model of attention-deficit/hyperactivity disorder.

    Leffa, Douglas Teixeira; de Souza, Andressa; Scarabelot, Vanessa Leal; Medeiros, Liciane Fernandes; de Oliveira, Carla; Grevet, Eugenio Horacio; Caumo, Wolnei; de Souza, Diogo Onofre; Rohde, Luis Augusto Paim; Torres, Iraci L S

    2016-02-01

    Attention deficit hyperactivity disorder (ADHD) is characterized by impairing levels of hyperactivity, impulsivity and inattention. However, different meta-analyses have reported disruptions in short and long-term memory in ADHD patients. Previous studies indicate that mnemonic dysfunctions might be the result of deficits in attentional circuits, probably due to ineffective dopaminergic modulation of hippocampal synaptic plasticity. In this study we aimed to evaluate the potential therapeutic effects of a neuromodulatory technique, transcranial direct current stimulation (tDCS), in short-term memory (STM) deficits presented by the spontaneous hypertensive rats (SHR), the most widely used animal model of ADHD. Adult male SHR and Wistar Kyoto rats (WKY) were subjected to a constant electrical current of 0.5 mA intensity applied on the frontal cortex for 20 min/day during 8 days. STM was evaluated with an object recognition test conducted in an open field. Exploration time and locomotion were recorded, and brain regions were dissected to determine dopamine and BDNF levels. SHR spent less time exploring the new object when compared to WKY, and tDCS improved object recognition deficits in SHR without affecting WKY performance. Locomotor activity was higher in SHR and it was not affected by tDCS. After stimulation, dopamine levels were increased in the hippocampus and striatum of both strains, while BDNF levels were increased only in the striatum of WKY. These findings suggest that tDCS on the frontal cortex might be able to improve STM deficits present in SHR, which is potentially related to dopaminergic neurotransmission in the hippocampus and striatum of those animals. Copyright © 2016. Published by Elsevier B.V.

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

    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...

  6. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

    Almonacid, F.; Pérez-Higueras, P.J.; Fernández, Eduardo F.; Hontoria, L.

    2014-01-01

    Highlights: • The output of the majority of renewables energies depends on the variability of the weather conditions. • The short-term forecast is going to be essential for effectively integrating solar energy sources. • A new method based on artificial neural network to predict the power output of a PV generator one hour ahead is proposed. • This new method is based on dynamic artificial neural network to predict global solar irradiance and the air temperature. • The methodology developed can be used to estimate the power output of a PV generator with a satisfactory margin of error. - Abstract: One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy

  7. The Effects of Short-Term Ski Trainings on Dynamic Balance Performance and Vertical Jump in Adolescents

    Camliguney, Asiye Filiz

    2013-01-01

    Skiing is a sport where balance and strength are critical and which can be practiced actively especially from early years to old age. The purpose of this study is to examine the effect of a 5-day training of skiing skills on dynamic balance performance and development of vertical jump strength in adolescents. Sixteen adolescent volunteers who do…

  8. Short-term salt stress strongly affects dynamic photosynthesis, but not steady-state photosynthesis, in tomato (Solanum lycopersicum)

    Zhang, Yuqi; Kaiser, Elias; Zhang, Yating; Yang, Qichang; Li, Tao

    2018-01-01

    Salt stress occurs worldwide due to widespread soil salinization. Also, plants are often subjected to rapidly alternating periods of sun and shade (sunflecks). Despite this combined occurrence of salt stress and sunflecks, dynamic photosynthetic responses to sunflecks under salt stress remain

  9. Short term effects of bioenergy by-products on soil C and N dynamics, nutrient availability and biochemical properties

    Galvez, A.; Sinicco, T.; Cayuela, M.L.; Mingorance, M.D.; Fornasier, F.; Mondini, C.

    2012-01-01

    The shift towards a biobased economy will probably trigger the application of bioenergy by-products to the soil as either amendments or fertilizers. However, limited research has been done to determine how this will influence C and N dynamics and soil functioning. The aim of this work was to

  10. Factors influencing short-term outcomes for older patients accessing emergency departments after a fall: The role of fall dynamics.

    Trevisan, Caterina; Di Gregorio, Patrizia; Debiasi, Eugenio; Pedrotti, Martina; La Guardia, Mario; Manzato, Enzo; Sergi, Giuseppe; March, Albert

    2017-10-01

    While the relevance of falls in raising the risk of fractures, hospitalization and disability in older age is well recognized, the factors influencing the onset of fractures and the need for ward admission after a fall have yet to be fully elucidated. We investigated which factors and fall dynamics were mainly associated with fall-related injuries and hospitalization among elderly persons accessing the Emergency Department (ED) following a fall. The study involved 2144 older subjects who accessed the ED after a fall. Data on the fall´s nature and related injuries, ward admissions, history of falls, dementia, and medical therapies were examined for all patients. Considering dynamics, we distinguished accidental falls (due to interaction with environmental hazards while in motion) and falls from standing (secondary to syncope, lipothymia, drop attack, or vertigo). The overall prevalence of fractures in our population did not differ significantly with advancing age, though hip fractures were more common in the oldest, and upper limb fractures in the youngest patients. Falls from standing were associated with polypharmacy and with higher ward admission rate despite a lower fractures´ prevalence than accidental falls. The chances of fall-related fractures were more than fourfold as high for accidental dynamics (OR=4.05, 95%CI:3.10-5.29, pfall-related fractures (OR=6.84, 95%CI:5.45-8.58, pfall dynamics. Outcomes of falls in older age depend not only on any fall-related injuries, but also on factors such as polypharmacy, cognitive status and fall dynamics. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    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

  12. Follicular dynamics, interval to ovulation and fertility after AI in short-term progesterone and PGF2α oestrous synchronization protocol in sheep.

    Cox, J F; Allende, R; Lara, E; Leiva, A; Díaz, T; Dorado, J; Saravia, F

    2012-12-01

    The study was aimed to assess the influence that short-term progesterone treatments have on follicular dynamics, oestrus and ovulation in sheep. The treatment was tested thereafter in a field trial to assess its fertility after AI with fresh semen. In a first experiment, 12 ewes without CL were grouped to receive a new (n = 6) or used CIDR (n = 6) for 7 days and blood samples were obtained to follow plasma progesterone profiles. In a second experiment, 39 cycling ewes were synchronized by a 7-day P4+PGF2α protocol using a new (n = 20) or a 7-day used CIDR (n = 19). Half of both groups received 400 IU eCG and half remained untreated as controls. Ultrasound ovarian examination and oestrous detection were used to compare follicular dynamics, oestrus and ovulation in both groups. In a third experiment, 288 ewes in 3 farms were synchronized by the short-term P4+PGF2α+eCG protocol and ewes were AI with fresh semen 24 h after oestrous detection. Lambing performance was used to test the fertility of the treatment. In Experiment 1, ewes with new inserts presented higher P4 concentration than ewes with used inserts throughout the sampling period (p 0.10). However, ewes treated with eCG show shorter interval to oestrus (p = 0.004) and tend to have larger mature CL (p = 0.06). In Experiment 3, oestrous presentation and lambing performance after AI with fresh semen was considered normal compared to published results. Results suggest that the oestrous synchronization protocol based on P4+PGF2α allows little control of follicular dynamics without compromising fertility after AI with fresh semen provided that eCG is added at the end of the treatment. © 2012 Blackwell Verlag GmbH.

  13. Short-term action potential memory and electrical restitution: A cellular computational study on the stability of cardiac repolarization under dynamic pacing.

    Massimiliano Zaniboni

    Full Text Available Electrical restitution (ER is a major determinant of repolarization stability and, under fast pacing rate, it reveals memory properties of the cardiac action potential (AP, whose dynamics have never been fully elucidated, nor their ionic mechanisms. Previous studies have looked at ER mainly in terms of changes in AP duration (APD when the preceding diastolic interval (DI changes and described dynamic conditions where this relationship shows hysteresis which, in turn, has been proposed as a marker of short-term AP memory and repolarization stability. By means of numerical simulations of a non-propagated human ventricular AP, we show here that measuring ER as APD versus the preceding cycle length (CL provides additional information on repolarization dynamics which is not contained in the companion formulation. We focus particularly on fast pacing rate conditions with a beat-to-beat variable CL, where memory properties emerge from APD vs CL and not from APD vs DI and should thus be stored in APD and not in DI. We provide an ion-currents characterization of such conditions under periodic and random CL variability, and show that the memory stored in APD plays a stabilizing role on AP repolarization under pacing rate perturbations. The gating kinetics of L-type calcium current seems to be the main determinant of this safety mechanism. We also show that, at fast pacing rate and under otherwise identical pacing conditions, a periodically beat-to-beat changing CL is more effective than a random one in stabilizing repolarization. In summary, we propose a novel view of short-term AP memory, differentially stored between systole and diastole, which opens a number of methodological and theoretical implications for the understanding of arrhythmia development.

  14. The Major Mobilization of the Unconscious and the Total Removal of Resistance in Davanloo's Intensive Short-term Dynamic Psychotherapy Part II: Treating the Transference Neurosis.

    Hickey, Catherine

    2015-01-01

    Davanloo's Intensive Short-term Dynamic Psychotherapy has been the subject of various reviews. The first article in this series focused on a review of Davanloo's early work as well as a discussion of some of his most recent research findings. A case from the Montreal closed circuit training program was reviewed. This second article will focus on Davanloo's views on the transference neurosis and how its development should be avoided at all costs. There will be further exploration of the case presented in Part I from the Montreal closed circuit training program. There will also be a special focus on detecting the transference neurosis when present and the technical interventions needed to lay the foundations for removing it.

  15. The Major Mobilization of the Unconscious and the Total Removal of Resistance in Davanloo's Intensive Short-term Dynamic Psychotherapy Part I: An Introduction.

    Hickey, Catherine

    2015-01-01

    Davanloo's Intensive Short-term Dynamic Psychotherapy has been the subject of various reviews. Davanloo has published extensively on his early work, but there have been no publications on his most recent work-most notably his Montreal Closed-circuit training program. This program focuses on his most recent discoveries and techniques and is a unique, videotaped supervisory program. It focuses on self-assessment and peer-assessment. It is also a unique format in which to review Davanloo's theoretical conceptions of resistance and the transference component of the resistance. This paper will review the early work of Davanloo as well as his most recent research findings. A case from the Montreal Closed-circuit training program will be reviewed in detail to highlight these findings.

  16. Ocean Dredged Material Disposal Site (ODMDS) Authorization and Short-Term FATE (STFATE) Model Analysis: 2014-2015 Working Group Findings Report

    2016-03-01

    fractions A grain size or sieve analysis typically yields the mass fraction of each particle size class after dispersing all of the material. However...ER D C TR -1 6- 2 Ocean Dredged Material Disposal Site (ODMDS) Authorization and Short-Term FATE (STFATE) Model Analysis 2014 – 2015...Term FATE (STFATE) Model Analysis 2014 – 2015 Working Group Findings Report Jase D. Ousley Coastal and Hydraulics Laboratory U.S. Army Engineer

  17. [Dynamic observation on the short-term change of xerostomia after intensity-modulated radiotherapy for patients with nasopharyngeal carcinoma].

    Li, Yanjie; Zhao, Changqing

    2015-01-01

    To dynamically analyze the change of xerostomia in patients with nasopharyngeal carcinoma after radiotherapy by DW MRI. Twenty-three nasopharyngeal carcinoma patients confirmed by pathology were enrolled. Male/Female: 19/4. The age was from 37 to 69 years. The patients were divided into two groups: G1, Dmeanxerostomia was assessed. SPSS 13.0 and SAS 8.2 software were used to analyze the data. At the end of IMRT, the change tendency of ADC in parotid and submandibular glands value was different in patients with different degree of xerostomia (F = 11.52, P xerostomia could be found in patients within different irradiation dose groups (Z = -3.622, P xerostomia for patients at the end of IMRT (Z value was -0.791, -0.949, 2.488, all P > 0.05). A significant difference of xerostomia degree in patients was found at the various follow-up time after IMRT (χ(2) = 19.59, P xerostomia in patients with nasopharyngeal carcinoma after radiotherapy. The degrees of salivary gland function and dry mouth in patients with nasopharyngeal carcinoma damage evaluate with illuminated dose increases. The function of salivary gland gradually restored and the degree of dry mouth gradually reduce with the extension of time after radiotherapy.

  18. Very short-term rainfall forecasting by effectively using the ensemble outputs of numerical weather prediction models

    Wu, Ming-Chang; Lin, Gwo-Fong; Feng, Lei; Hwang, Gong-Do

    2017-04-01

    In Taiwan, heavy rainfall brought by typhoons often causes serious disasters and leads to loss of life and property. In order to reduce the impact of these disasters, accurate rainfall forecasts are always important for civil protection authorities to prepare proper measures in advance. In this study, a methodology is proposed for providing very short-term (1- to 6-h ahead) rainfall forecasts in a basin-scale area. The proposed methodology is developed based on the use of analogy reasoning approach to effectively integrate the ensemble precipitation forecasts from a numerical weather prediction system in Taiwan. To demonstrate the potential of the proposed methodology, an application to a basin-scale area (the Choshui River basin located in west-central Taiwan) during five typhoons is conducted. The results indicate that the proposed methodology yields more accurate hourly rainfall forecasts, especially the forecasts with a lead time of 1 to 3 hours. On average, improvement of the Nash-Sutcliffe efficiency coefficient is about 14% due to the effective use of the ensemble forecasts through the proposed methodology. The proposed methodology is expected to be useful for providing accurate very short-term rainfall forecasts during typhoons.

  19. Decarbonization scenarios for the EU and MENA power system: Considering spatial distribution and short term dynamics of renewable generation

    Haller, Markus; Ludig, Sylvie; Bauer, Nico

    2012-01-01

    We use the multi-scale power system model LIMES-EU + to explore coordinated long term expansion pathways for Renewable Energy (RE) generation, long distance transmission and storage capacities for the power sector of the Europe and Middle East/North Africa (MENA) regions that lead to a low emission power system. We show that ambitious emission reduction targets can be achieved at moderate costs by a nearly complete switch to RE sources until 2050, if transmission and storage capacities are expanded adequately. Limiting transmission capacities to current levels leads to higher storage requirements, higher curtailments, and to an increase in temporal and spatial electricity price variations. Results show an escalation of electricity prices if emission reductions exceed a critical value. Adequate expansion of transmission and storage capacities shift this threshold from 70% to 90% emission reductions in 2050 relative to 2010. - Highlights: ► We present an EU+MENA power system model that considers long term investments and integration of renewables. ► For low emission targets, renewable integration issues lead to escalating electricity prices. ► The feasibility frontier can be pushed by adequate transmission and storage investments. ► The transformation from wind/fossil to wind/solar regime changes integration requirements. ► Low emission targets can be reached without significant interconnections between EU and MENA regions.

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

    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.

  1. Abnormal presynaptic short-term plasticity and information processing in a mouse model of fragile X syndrome.

    Deng, Pan-Yue; Sojka, David; Klyachko, Vitaly A

    2011-07-27

    Fragile X syndrome (FXS) is the most common inherited form of intellectual disability and the leading genetic cause of autism. It is associated with the lack of fragile X mental retardation protein (FMRP), a regulator of protein synthesis in axons and dendrites. Studies on FXS have extensively focused on the postsynaptic changes underlying dysfunctions in long-term plasticity. In contrast, the presynaptic mechanisms of FXS have garnered relatively little attention and are poorly understood. Activity-dependent presynaptic processes give rise to several forms of short-term plasticity (STP), which is believed to control some of essential neural functions, including information processing, working memory, and decision making. The extent of STP defects and their contributions to the pathophysiology of FXS remain essentially unknown, however. Here we report marked presynaptic abnormalities at excitatory hippocampal synapses in Fmr1 knock-out (KO) mice leading to defects in STP and information processing. Loss of FMRP led to enhanced responses to high-frequency stimulation. Fmr1 KO mice also exhibited abnormal synaptic processing of natural stimulus trains, specifically excessive enhancement during the high-frequency spike discharges associated with hippocampal place fields. Analysis of individual STP components revealed strongly increased augmentation and reduced short-term depression attributable to loss of FMRP. These changes were associated with exaggerated calcium influx in presynaptic neurons during high-frequency stimulation, enhanced synaptic vesicle recycling, and enlarged readily-releasable and reserved vesicle pools. These data suggest that loss of FMRP causes abnormal STP and information processing, which may represent a novel mechanism contributing to cognitive impairments in FXS.

  2. Directed coupling in local field potentials of macaque V4 during visual short-term memory revealed by multivariate autoregressive models

    Gregor M Hoerzer

    2010-05-01

    Full Text Available Processing and storage of sensory information is based on the interaction between different neural populations rather than the isolated activity of single neurons. In order to characterize the dynamic interaction and transient cooperation of sub-circuits within a neural network, multivariate autoregressive (MVAR models have proven to be an important analysis tool. In this study, we apply directed functional coupling based on MVAR models and describe the temporal and spatial changes of functional coupling between simultaneously recorded local field potentials (LFP in extrastriate area V4 during visual memory. Specifically, we compare the strength and directional relations of coupling based on Generalized Partial Directed Coherence (GDPC measures while two rhesus monkeys perform a visual short-term memory task. In both monkeys we find increases in theta power during the memory period that are accompanied by changes in directed coupling. These interactions are most prominent in the low frequency range encompassing the theta band (3-12~Hz and, more importantly, are asymmetric between pairs of recording sites. Furthermore, we find that the degree of interaction decreases as a function of distance between electrode positions, suggesting that these interactions are a predominantly local phenomenon. Taken together, our results show that directed coupling measures based on MVAR models are able to provide important insights into the spatial and temporal formation of local functionally coupled ensembles during visual memory in V4. Moreover, our findings suggest that visual memory is accompanied not only by a temporary increase of oscillatory activity in the theta band, but by a direction-dependent change in theta coupling, which ultimately represents a change in functional connectivity within the neural circuit.

  3. 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

    E. E. Popova

    2006-01-01

    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 primarily because of increased light limitation resulting from deepening of the mixed layer overnight as well as enhanced storm activity, and 25% at moderate and high latitudes primarily due to increased grazing pressure resulting from late winter stratification events. Mini-blooms of phytoplankton and zooplankton occur in the model during these events, leading to zooplankton populations being sufficiently well developed to suppress the progress of phytoplankton blooms. A 10% increase in primary production was predicted in the peripheries of the oligotrophic gyres due to increased storm-induced nutrient supply end enhanced winter production during the short term stratification events that are resolved in the run forced by 6 h meteorological fields. 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.

  4. The Short-Term Dynamics of Peers and Delinquent Behavior: An Analysis of Bi-weekly Changes Within a High School Student Network

    F.M. Weerman (Frank); P. Wilcox (Pamela); C.J. Sullivan (Christopher J.)

    2017-01-01

    markdownabstract_Objectives:_ To analyze short-term changes in peer affiliations, offending behavior and routine activities in order to evaluate three different processes: peer selection, peer socialization and situational peer influences. _Methods:_ The short-term longitudinal TEENS study was

  5. Long-term power generation expansion planning with short-term demand response: Model, algorithms, implementation, and electricity policies

    Lohmann, Timo

    Electric sector models are powerful tools that guide policy makers and stakeholders. Long-term power generation expansion planning models are a prominent example and determine a capacity expansion for an existing power system over a long planning horizon. With the changes in the power industry away from monopolies and regulation, the focus of these models has shifted to competing electric companies maximizing their profit in a deregulated electricity market. In recent years, consumers have started to participate in demand response programs, actively influencing electricity load and price in the power system. We introduce a model that features investment and retirement decisions over a long planning horizon of more than 20 years, as well as an hourly representation of day-ahead electricity markets in which sellers of electricity face buyers. This combination makes our model both unique and challenging to solve. Decomposition algorithms, and especially Benders decomposition, can exploit the model structure. We present a novel method that can be seen as an alternative to generalized Benders decomposition and relies on dynamic linear overestimation. We prove its finite convergence and present computational results, demonstrating its superiority over traditional approaches. In certain special cases of our model, all necessary solution values in the decomposition algorithms can be directly calculated and solving mathematical programming problems becomes entirely obsolete. This leads to highly efficient algorithms that drastically outperform their programming problem-based counterparts. Furthermore, we discuss the implementation of all tailored algorithms and the challenges from a modeling software developer's standpoint, providing an insider's look into the modeling language GAMS. Finally, we apply our model to the Texas power system and design two electricity policies motivated by the U.S. Environment Protection Agency's recently proposed CO2 emissions targets for the

  6. Modeling the Short-Term Effect of Traffic and Meteorology on Air Pollution in Turin with Generalized Additive Models

    Pancrazio Bertaccini

    2012-01-01

    Full Text Available Vehicular traffic plays an important role in atmospheric pollution and can be used as one of the key predictors in air-quality forecasting models. The models that can account for the role of traffic are especially valuable in urban areas, where high pollutant concentrations are often observed during particular times of day (rush hour and year (winter. In this paper, we develop a generalized additive models approach to analyze the behavior of concentrations of nitrogen dioxide (NO2, and particulate matter (PM10, collected at the environmental monitoring stations distributed throughout the city of Turin, Italy, from December 2003 to April 2005. We describe nonlinear relationships between predictors and pollutants, that are adjusted for unobserved time-varying confounders. We examine several functional forms for the traffic variable and find that a simple form can often provide adequate modeling power. Our analysis shows that there is a saturation effect of traffic on NO2, while such saturation is less evident in models linking traffic to PM10 behavior, having adjusted for meteorological covariates. Moreover, we consider the proposed models separately by seasons and highlight similarities and differences in the predictors’ partial effects. Finally, we show how forecasting can help in evaluating traffic regulation policies.

  7. Left TPJ activity in verbal working memory: implications for storage- and sensory-specific models of short term memory.

    Ravizza, Susan M; Hazeltine, Eliot; Ruiz, Sandra; Zhu, David C

    2011-04-15

    Patients with damage to the left temporoparietal junction (TPJ) have a low verbal span without concomitant deficits in speech perception. This pattern of cognitive impairment is taken as evidence for a dedicated phonological buffer that plays little role in perception (storage-specific account). In contrast, other research suggests that items are maintained and perceived in the same regions (sensory-specific account). In an fMRI study, we demonstrate that the left TPJ does not respond in a way predicted of a phonological buffer; that is, activity in this region is not sustained during encoding or maintenance. Instead, a region in the superior temporal gyrus that has been associated with both speech perception and production demonstrated the expected profile of a store: it was more active in the verbal condition than the object condition and was active during both encoding and maintenance. These results support the sensory-specific account of short term memory rather than the storage-specific account. Based on the pattern of activity in the left TPJ, we suggest that the impairment of verbal working memory observed in patients with TPJ damage may be due to diminished attentional processes rather than reduced storage capacity. Copyright © 2010 Elsevier Inc. All rights reserved.

  8. Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems

    Santos, P.J.; Martins, A.G.; Pires, A.J.

    2007-01-01

    The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector. (author)

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

    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

  10. On the time course of short-term forgetting: a human experimental model for the sense of balance.

    Tribukait, Arne; Eiken, Ola

    2016-02-01

    The primary aim of this study was to establish whether the decline of the memory of an angular displacement, detected by the semicircular canals, is best characterized by an exponential function or by a power function. In 27 subjects a conflict was created between the semicircular canals and the graviceptive systems. Subjects were seated, facing forwards, in the gondola of a large centrifuge. The centrifuge was accelerated from stationary to 2.5Gz. While the swing out of the gondola (66°) during acceleration constitutes a frontal plane angular-displacement stimulus to the semicircular canals, the graviceptive systems persistently signal that the subject is upright. During 6 min at 2.5Gz the perceived head and body position was recorded; in darkness the subject repeatedly adjusted the orientation of a luminous line so that it appeared to be horizontal. Acceleration of the centrifuge induced a sensation of tilt which declined with time in a characteristic way. A three-parameter exponential function (Y = Ae(-bt) + C) and a power function (Y = At(-b) + C) were fitted to the data points. The inter-individual variability was considerable. In the vast majority of cases, however, the exponential function provided a better fit (in terms of RMS error) than the power function. The mean exponential function was: y = 27.8e(-0.018t) + 0.5°, where t is time in seconds. Findings are discussed with connection to possible underlying neural mechanisms; in particular, the head-direction system and short-term potentiation and persistent action potential firing in the hippocampus are considered.

  11. Construction of long-term isochronous stress-strain curves by a modeling of short-term creep curves for a Grade 9Cr-1Mo steel

    Kim, Woo-Gon; Yin, Song-Nan; Koo, Gyeong-Hoi

    2009-01-01

    This study dealt with the construction of long-term isochronous stress-strain curves (ISSC) by a modeling of short-term creep curves for a Grade 9Cr-1Mo steel (G91) which is a candidate material for structural applications in the next generation nuclear reactors as well as in fusion reactors. To do this, tensile material data used in the inelastic constitutive equations was obtained by tensile tests at 550degC. Creep curves were obtained by a series of creep tests with different stress levels of 300MPa to 220MPa at an identical controlled temperature of 550degC. On the basis of these experimental data, the creep curves were characterized by Garofalo's creep model. Three parameters of P 1 , P 2 and P 3 in Garofalo's model were properly optimized by a nonlinear least square fitting (NLSF) analysis. The stress dependency of the three parameters was found to be a linear relationship. But, the P 3 parameter representing the steady state creep rate exhibited a two slope behavior with different stress exponents at a transient stress of about 250 MPa. The long-term creep curves of the G91 steel was modeled by Garofalo's model with only a few short-term creep data. Using the modeled creep curves, the long-term isochronous curves up to 10 5 hours were successfully constructed. (author)

  12. Short-Term Therapeutic Efficacy of the Isobar TTL Dynamic Internal Fixation System for the Treatment of Lumbar Degenerative Disc Diseases.

    Qian, Jiale; Bao, Zhaohua; Li, Xuefeng; Zou, Jun; Yang, Huilin

    2016-07-01

    At present, posterior interbody fusion surgery with pedicle internal fixation is the gold standard for the treatment of lumbar degenerative disc diseases. However, an increasing number of studies have shown that because fused lumbar vertebrae lose their physiological activity, the compensatory range of motion (ROM) of the adjacent levels increases. To address this issue, dynamic internal fixation systems have been developed. Our goal was to investigate the short-term therapeutic efficacy of the Isobar TTL dynamic internal fixation system for the treatment of lumbar degenerative disc diseases and its effect on the ROM of the surgical segments. Retrospective Evaluation. Tertiary hospital setting in China. Twenty-four lumbar degenerative disc disease patients who underwent posterior lumbar decompression and single-segment Isobar TTL dynamic internal fixation at our hospital between January 2013 and July 2014 were retrospectively analyzed. The preoperative and one month, 3 month, and 12 month postoperative visual analog scale (VAS) pain scores, Japanese Orthopedic Association (JOA) scores, and Oswestry Disability Index (ODI) scores were observed and recorded to assess the clinical therapeutic effect; the lumbar ROM was measured preoperatively and at the last follow-up to evaluate the preservation of functional movement in the dynamically stabilized segment. All patients underwent the operation successfully without complications during hospitalization and were followed for 12 to 27 months, with an average of 18 months. The patients' preoperative and one month, 3 month, and 12 month postoperative VAS scores were 6.42 ± 0.72, 1.71 ± 0.86, 1.38 ± 0.65, and 1.37 ± 0.58, respectively, and their JOA scores were 9.54 ± 1.89, 21.21 ± 1.98, 22.50 ± 1.47, and 23.46 ± 1.32, respectively. The preoperative ODI score was 42.04 ± 2.63; the one month, 3 month, and 12 month postoperative ODI scores were 22.79 ± 1.61, 18.63 ± 1.61, and 15.08 ± 1.21, respectively. These

  13. Onboard Short Term Plan Viewer

    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.

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

    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.

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

    Erik Karl Håkan Clemensson

    Full Text Available 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.

  16. Inhaled nitric oxide improves short term memory and reduces the inflammatory reaction in a mouse model of mild traumatic brain injury.

    Liu, Ping; Li, Yong-Sheng; Quartermain, David; Boutajangout, Allal; Ji, Yong

    2013-07-19

    Although the mechanisms underlying mild traumatic brain injury (mTBI) are becoming well understood, treatment options are still limited. In the present study, mTBI was induced by a weight drop model to produce a closed head injury to mice and the effect of inhaled nitric oxide (INO) was evaluated by a short term memory task (object recognition task) and immunohistochemical staining of glial fibrillary acidic protein (GFAP) and CD45 for the detection of reactive astrocytes and microglia. Results showed that mTBI model did not produce brain edema, skull fracture or sensorimotor coordination dysfunctions. Mice did however exhibit a significant deficit in short term memory (STM) and strong inflammatory reaction in the ipsilateral cortex and hippocampus compared to sham-injured controls 24h after mTBI. Additional groups of untreated mice tested 3 and 7 days later, demonstrated that recognition memory had recovered to normal levels by Day 3. Mice treated with 10ppm INO for 4 or 8h, beginning immediately after TBI demonstrated significantly improved STM at 24h when compared with room air controls (pshort durations of INO prevents this memory loss and also attenuates the inflammatory response. These findings may have relevance for the treatment of patients diagnosed with concussion. Copyright © 2013 Elsevier B.V. All rights reserved.

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

    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.

  18. Modelling of long-term and short-term mechanisms of arterial pressure control in the cardiovascular system: an object-oriented approach.

    Fernandez de Canete, J; Luque, J; Barbancho, J; Munoz, V

    2014-04-01

    A mathematical model that provides an overall description of both the short- and long-term mechanisms of arterial pressure regulation is presented. Short-term control is exerted through the baroreceptor reflex while renal elimination plays a role in long-term control. Both mechanisms operate in an integrated way over the compartmental model of the cardiovascular system. The whole system was modelled in MODELICA, which uses a hierarchical object-oriented modelling strategy, under the DYMOLA simulation environment. The performance of the controlled system was analysed by simulation in light of the existing hypothesis and validation tests previously performed with physiological data, demonstrating the effectiveness of both regulation mechanisms under physiological and pathological conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    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;

  20. Short-term synaptic plasticity and heterogeneity in neural systems

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  1. Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

    Che Jinxing; Wang Jianzhou

    2010-01-01

    In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.

  2. Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

    Claudio Monteiro

    2016-09-01

    Full Text Available This paper presents novel intraday session models for price forecasts (ISMPF models for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL and the analysis of mean absolute percentage errors (MAPEs obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

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

    2013-04-30

    photosynthetically active radiation (PAR) which drives photosynthesis of the ecosystem model, and relevant to the forecast of the underwater light...highly uncertain model parameters. Only P\\ and PI are prognostic variables in (1) and (2). [is] Phytoplankton photosynthesis in the biochemical model...Application to remote sensing and database activities, Mar. Chem., #5(2004), 41-61. Cossarini G.. P. F. J. Lcrmusiaux. and C. Solidoro (2009), Lagoon of

  4. 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

    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

  5. Using targeted short-term field investigations to calibrate and evaluate the structure of a hydrological model

    Hughes, DA

    2013-02-01

    Full Text Available catchments and are applied in a daily version of the model. The results demonstrate the importance of ensuring that field observations are measuring the same hydrological variables as the model simulations. At one study site, there was a mismatch in the soil...

  6. The integration of familiarity and recollection information in short-term recognition: modeling speed-accuracy trade-off functions.

    Göthe, Katrin; Oberauer, Klaus

    2008-05-01

    Dual process models postulate familiarity and recollection as the basis of the recognition process. We investigated the time-course of integration of the two information sources to one recognition judgment in a working memory task. We tested 24 subjects with a response signal variant of the modified Sternberg recognition task (Oberauer, 2001) to isolate the time course of three different probe types indicating different combinations of familiarity and source information. We compared two mathematical models implementing different ways of integrating familiarity and recollection. Within each model, we tested three assumptions about the nature of the familiarity signal, with familiarity having (a) only positive values, indicating similarity of the probe with the memory list, (b) only negative values, indicating novelty, or (c) both positive and negative values. Both models provided good fits to the data. A model combining the outputs of both processes additively (Integration Model) gave an overall better fit to the data than a model based on a continuous familiarity signal and a probabilistic all-or-none recollection process (Dominance Model).

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

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

    2017-01-01

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

  8. Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels

    Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

    2018-01-01

    The share of wind energy in total installed power capacity has grown rapidly in recent years. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build...... spatiotemporal models for wind power generation and obtain full probabilistic forecasts from 15 min to 5 h ahead. Detailed analyses of forecast performances on individual wind farms and aggregated wind power are provided. The predictions from our models are evaluated on a data set from wind farms in western...... Denmark using a sliding window approach, for which estimation is performed using only the last available measurements. The case study shows that it is important to have a spatiotemporal model instead of a temporal one to achieve calibrated aggregated forecasts. Furthermore, spatiotemporal models have...

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

    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

  10. Short-Term Wave Forecasting with AR models in Real-Time Optimal Control of Wave Energy Converters

    Fusco, Francesco; Ringwood, John

    2010-01-01

    Time domain control of wave energy converters requires knowledge of future incident wave elevation in order to approach conditions for optimal energy extraction. Autoregressive models revealed to be a promising approach to the prediction of future values of the wave elevation only from its past history. Results on real wave observations from different ocean locations show that AR models allow to achieve very good predictions for more than one wave period in the future if ...

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

    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.

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

    Hemmati, Reza; Saboori, Hedayat

    2016-01-01

    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. PMID:27222741

  13. Comparison of Multi-shot Models for Short-term Re-identification of People using RGB-D Sensors

    Møgelmose, Andreas; Bahnsen, Chris; Moeslund, Thomas B.

    2015-01-01

    This work explores different types of multi-shot descriptors for re-identification in an on-the-fly enrolled environment using RGB-D sensors. We present a full re-identification pipeline complete with detection, segmentation, feature extraction, and re-identification, which expands on previous work...... by using multi-shot descriptors modeling people over a full camera pass instead of single frames with no temporal linking. We compare two different multi-shot models; mean histogram and histogram series, and test them each in 3 different color spaces. Both histogram descriptors are assisted by a depth...

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

    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

  15. Quantification of submarine groundwater discharge and its short-term dynamics by linking time-variant end-member mixing analysis and isotope mass balancing (222-Rn)

    Petermann, Eric; Knöller, Kay; Stollberg, Reiner; Scholten, Jan; Rocha, Carlos; Weiß, Holger; Schubert, Michael

    2017-04-01

    Submarine groundwater discharge (SGD) plays a crucial role for the water quality of coastal waters due to associated fluxes of nutrients, organic compounds and/or heavy-metals. Thus, the quantification of SGD is essential for evaluating the vulnerability of coastal water bodies with regard to groundwater pollution as well as for understanding the matter cycles of the connected water bodies. Here, we present a scientific approach for quantifying discharge of fresh groundwater (GWf) and recirculated seawater (SWrec), including its short-term temporal dynamics, into the tide-affected Knysna estuary, South Africa. For a time-variant end-member mixing analysis we conducted time-series observations of radon (222Rn) and salinity within the estuary over two tidal cycles in combination with estimates of the related end-members for seawater, river water, GWf and SWrec. The mixing analysis was treated as constrained optimization problem for finding an end-member mixing ratio that simultaneously fits the observed data for radon and salinity best for every time-step. Uncertainty of each mixing ratio was quantified by Monte Carlo simulations of the optimization procedure considering uncertainty in end-member characterization. Results reveal the highest GWf and SWrec fraction in the estuary during peak low tide with averages of 0.8 % and 1.4 %, respectively. Further, we calculated a radon mass balance that revealed a daily radon flux of 4.8 * 108 Bq into the estuary equivalent to a GWf discharge of 29.000 m3/d (9.000-59.000 m3/d for 25th-75th percentile range) and a SWrec discharge of 80.000 m3/d (45.000-130.000 m3/d for 25th-75th percentile range). The uncertainty of SGD reflects the end-member uncertainty, i.e. the spatial heterogeneity of groundwater composition. The presented approach allows the calculation of mixing ratios of multiple uncertain end-members for time-series measurements of multiple parameters. Linking these results with a tracer mass balance allows conversion

  16. A prediction model of short-term ionospheric foF2 based on AdaBoost

    Zhao, Xiukuan; Ning, Baiqi; Liu, Libo; Song, Gangbing

    2014-02-01

    In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years' foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years' data were used as a training dataset and the second eleven years' data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.

  17. Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

    Cohen, Charlie; Robertson, Franklin; Molod, Andrea

    2014-01-01

    The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of

  18. A prediction model of short-term ionospheric foF2 Based on AdaBoost

    Zhao, Xiukuan; Liu, Libo; Ning, Baiqi

    Accurate specifications of spatial and temporal variations of the ionosphere during geomagnetic quiet and disturbed conditions are critical for applications, such as HF communications, satellite positioning and navigation, power grids, pipelines, etc. Therefore, developing empirical models to forecast the ionospheric perturbations is of high priority in real applications. The critical frequency of the F2 layer, foF2, is an important ionospheric parameter, especially for radio wave propagation applications. In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.

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

    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.

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

    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.

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

    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. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  2. A Rodent Model of Night-Shift Work Induces Short-Term and Enduring Sleep and Electroencephalographic Disturbances.

    Grønli, Janne; Meerlo, Peter; Pedersen, Torhild T; Pallesen, Ståle; Skrede, Silje; Marti, Andrea R; Wisor, Jonathan P; Murison, Robert; Henriksen, Tone E G; Rempe, Michael J; Mrdalj, Jelena

    2017-02-01

    Millions of people worldwide are working at times that overlap with the normal time for sleep. Sleep problems related to the work schedule may mediate the well-established relationship between shift work and increased risk for disease, occupational errors and accidents. Yet, our understanding of causality and the underlying mechanisms that explain this relationship is limited. We aimed to assess the consequences of night-shift work for sleep and to examine whether night-shift work-induced sleep disturbances may yield electrophysiological markers of impaired maintenance of the waking brain state. An experimental model developed in rats simulated a 4-day protocol of night-work in humans. Two groups of rats underwent 8-h sessions of enforced ambulation, either at the circadian time when the animal was physiologically primed for wakefulness (active-workers, mimicking day-shift) or for sleep (rest-workers, mimicking night-shift). The 4-day rest-work schedule induced a pronounced redistribution of sleep to the endogenous active phase. Rest-work also led to higher electroencephalogram (EEG) slow-wave (1-4 Hz) energy in quiet wakefulness during work-sessions, suggesting a degraded waking state. After the daily work-sessions, being in their endogenous active phase, rest-workers slept less and had higher gamma (80-90 Hz) activity during wake than active-workers. Finally, rest-work induced an enduring shift in the main sleep period and attenuated the accumulation of slow-wave energy during NREM sleep. A comparison of recovery data from 12:12 LD and constant dark conditions suggests that reduced time in NREM sleep throughout the recorded 7-day recovery phase induced by rest-work may be modulated by circadian factors. Our data in rats show that enforced night-work-like activity during the normal resting phase has pronounced acute and persistent effects on sleep and waking behavior. The study also underscores the potential importance of animal models for future studies on the

  3. 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.

    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.

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

    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.

  5. Solar Activity from 2006 to 2014 and Short-term Forecasts of Solar Proton Events Using the ESPERTA Model

    Alberti, T.; Lepreti, F. [Dipartimento di Fisica, Università della Calabria, Ponte P. Bucci, Cubo 31C, 87036, Rende (CS) (Italy); Laurenza, M.; Storini, M.; Consolini, G. [INAF-IAPS, Via del Fosso del Cavaliere 100, I-00133, Roma (Italy); Cliver, E. W., E-mail: tommaso.alberti@unical.it, E-mail: monica.laurenza@iaps.inaf.it [National Solar Observatory, Boulder, CO (United States)

    2017-03-20

    To evaluate the solar energetic proton (SEP) forecast model of Laurenza et al., here termed ESPERTA, we computed the input parameters (soft X-ray (SXR) fluence and ∼1 MHz radio fluence) for all ≥M2 SXR flares from 2006 to 2014. This database is outside the 1995–2005 interval on which ESPERTA was developed. To assess the difference in the general level of activity between these two intervals, we compared the occurrence frequencies of SXR flares and SEP events for the first six years of cycles 23 (1996 September–2002 September) and 24 (2008 December–2014 December). We found a reduction of SXR flares and SEP events of 40% and 46%, respectively, in the latter period. Moreover, the numbers of ≥M2 flares with high values of SXR and ∼1 MHz fluences (>0.1 J m{sup −2} and >6 × 10{sup 5} sfu × minute, respectively) are both reduced by ∼30%. A somewhat larger percentage decrease of these two parameters (∼40% versus ∼30%) is obtained for the 2006–2014 interval in comparison with 1995–2005. Despite these differences, ESPERTA performance was comparable for the two intervals. For the 2006–2014 interval, ESPERTA had a probability of detection (POD) of 59% (19/32) and a false alarm rate (FAR) of 30% (8/27), versus a POD = 63% (47/75) and an FAR = 42% (34/81) for the original 1995–2005 data set. In addition, for the 2006–2014 interval the median (average) warning time was estimated to be ∼2 hr (∼7 hr), versus ∼6 hr (∼9 hr), for the 1995–2005 data set.

  6. Short-term Memory of Deep RNN

    Gallicchio, Claudio

    2018-01-01

    The extension of deep learning towards temporal data processing is gaining an increasing research interest. In this paper we investigate the properties of state dynamics developed in successive levels of deep recurrent neural networks (RNNs) in terms of short-term memory abilities. Our results reveal interesting insights that shed light on the nature of layering as a factor of RNN design. Noticeably, higher layers in a hierarchically organized RNN architecture results to be inherently biased ...

  7. A Short Term Analogue Memory

    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...

  8. Short-term depression and transient memory in sensory cortex.

    Gillary, Grant; Heydt, Rüdiger von der; Niebur, Ernst

    2017-12-01

    Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive feedback models which decay to baseline lose their persistence when their recurrent connections are subject to short-term depression, a common property of excitatory connections in early sensory areas. Dual time constant network behavior has also been implemented by nonlinear afferents producing a large transient input followed by much smaller steady state input. We show that such networks require unphysiologically large onset transients to produce the rise and decay observed in sensory areas. Our study explores how memory and persistence can be implemented in another model class, derivative feedback networks. We show that these networks can operate with two vastly different time courses, changing their state quickly when new information is coming in but retaining it for a long time, and that these capabilities are robust to short-term depression. Specifically, derivative feedback networks with short-term depression that acts differentially on positive and negative feedback projections are capable of dynamically changing their time constant, thus allowing fast onset and slow decay of responses without requiring unrealistically large input transients.

  9. WRF model sensitivity to land surface model and cumulus parameterization under short-term climate extremes over the southern Great Plains of the United States

    Lisi Pei; Nathan Moore; Shiyuan Zhong; Lifeng Luo; David W. Hyndman; Warren E. Heilman; Zhiqiu. Gao

    2014-01-01

    Extreme weather and climate events, especially short-term excessive drought and wet periods over agricultural areas, have received increased attention. The Southern Great Plains (SGP) is one of the largest agricultural regions in North America and features the underlying Ogallala-High Plains Aquifer system worth great economic value in large part due to production...

  10. Population Dynamics and Cost-Benefit Analysis. An Attempt to Relate Population Dynamics via Lifetime Reproductive Success to Short-Term Decisions

    Tinbergen, J.M.; Balen, J.H. van; Drent, P.J.; Cavé, A.J.; Mertens, J.A.L.; Boer-Hazewinkel, J. den

    1987-01-01

    1. The aim of this article is to explore whether cost-benefit analysis of behaviour may help to understand the population dynamics of a species. The Great Tit is taken as an example. 2. The lifetime reproductive success in different populations of Great Tits amounts from 0.7 (Hoge Veluwe, Wytham) to

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

    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.

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

    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. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  13. Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

    Qiang Shang

    2016-08-01

    Full Text Available Short-term traffic flow prediction is an important part of intelligent transportation systems research and applications. For further improving the accuracy of short-time traffic flow prediction, a novel hybrid prediction model (multivariate phase space reconstruction–combined kernel function-least squares support vector machine based on multivariate phase space reconstruction and combined kernel function-least squares support vector machine is proposed. The C-C method is used to determine the optimal time delay and the optimal embedding dimension of traffic variables’ (flow, speed, and occupancy time series for phase space reconstruction. The G-P method is selected to calculate the correlation dimension of attractor which is an important index for judging chaotic characteristics of the traffic variables’ series. The optimal input form of combined kernel function-least squares support vector machine model is determined by multivariate phase space reconstruction, and the model’s parameters are optimized by particle swarm optimization algorithm. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. The experimental results suggest that the new proposed model yields better predictions compared with similar models (combined kernel function-least squares support vector machine, multivariate phase space reconstruction–generalized kernel function-least squares support vector machine, and phase space reconstruction–combined kernel function-least squares support vector machine, which indicates that the new proposed model exhibits stronger prediction ability and robustness.

  14. A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

    Weide Li

    2017-05-01

    Full Text Available Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM, which combines k-Nearest Neighbor (KNN and Extreme Learning Machine (ELM based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM, Wavelet Denoising-Extreme Learning Machine (WKM and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM, the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

  15. Dynamics of cyanobacterial bloom formation during short-term hydrodynamic fluctuation in a large shallow, eutrophic, and wind-exposed Lake Taihu, China.

    Wu, Tingfeng; Qin, Boqiang; Zhu, Guangwei; Luo, Liancong; Ding, Yanqing; Bian, Geya

    2013-12-01

    Short-term hydrodynamic fluctuations caused by extreme weather events are expected to increase worldwide because of global climate change, and such fluctuations can strongly influence cyanobacterial blooms. In this study, the cyanobacterial bloom disappearance and reappearance in Lake Taihu, China, in response to short-term hydrodynamic fluctuations, was investigated by field sampling, long-term ecological records, high-frequency sensors and MODIS satellite images. The horizontal drift caused by the dominant easterly wind during the phytoplankton growth season was mainly responsible for cyanobacterial biomass accumulation in the western and northern regions of the lake and subsequent bloom formation over relatively long time scales. The cyanobacterial bloom changed slowly under calm or gentle wind conditions. In contrast, the short-term bloom events within a day were mainly caused by entrainment and disentrainment of cyanobacterial colonies by wind-induced hydrodynamics. Observation of a westerly event in Lake Taihu revealed that when the 30 min mean wind speed (flow speed) exceeded the threshold value of 6 m/s (5.7 cm/s), cyanobacteria in colonies were entrained by the wind-induced hydrodynamics. Subsequently, the vertical migration of cyanobacterial colonies was controlled by hydrodynamics, resulting in thorough mixing of algal biomass throughout the water depth and the eventual disappearance of surface blooms. Moreover, the intense mixing can also increase the chance for forming larger and more cyanobacterial colonies, namely, aggregation. Subsequently, when the hydrodynamics became weak, the cyanobacterial colonies continuously float upward without effective buoyancy regulation, and cause cyanobacterial bloom explosive expansion after the westerly. Furthermore, the results of this study indicate that the strong wind happening frequently during April and October can be an important cause of the formation and expansion of cyanobacterial blooms in Lake Taihu.

  16. A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company

    Ahmadi, Abdollah; Charwand, Mansour; Siano, Pierluigi; Nezhad, Ali Esmaeel; Sarno, Debora; Gitizadeh, Mohsen; Raeisi, Fatima

    2016-01-01

    In order to supply the demands of the end users in a competitive market, a distribution company purchases energy from the wholesale market while other options would be in access in the case of possessing distributed generation units and interruptible loads. In this regard, this study presents a two-stage stochastic programming model for a distribution company energy acquisition market model to manage the involvement of different electric energy resources characterized by uncertainties with the minimum cost. In particular, the distribution company operations planning over a day-ahead horizon is modeled as a stochastic mathematical optimization, with the objective of minimizing costs. By this, distribution company decisions on grid purchase, owned distributed generation units and interruptible load scheduling are determined. Then, these decisions are considered as boundary constraints to a second step, which deals with distribution company's operations in the hour-ahead market with the objective of minimizing the short-term cost. The uncertainties in spot market prices and wind speed are modeled by means of probability distribution functions of their forecast errors and the roulette wheel mechanism and lattice Monte Carlo simulation are used to generate scenarios. Numerical results show the capability of the proposed method. - Highlights: • Proposing a new a stochastic-based two-stage operations framework in retail competitive markets. • Proposing a Mixed Integer Non-Linear stochastic programming. • Employing roulette wheel mechanism and Lattice Monte Carlo Simulation.

  17. Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting

    Wendong Yang

    2017-01-01

    Full Text Available Machine learning plays a vital role in several modern economic and industrial fields, and selecting an optimized machine learning method to improve time series’ forecasting accuracy is challenging. Advanced machine learning methods, e.g., the support vector regression (SVR model, are widely employed in forecasting fields, but the individual SVR pays no attention to the significance of data selection, signal processing and optimization, which cannot always satisfy the requirements of time series forecasting. By preprocessing and analyzing the original time series, in this paper, a hybrid SVR model is developed, considering periodicity, trend and randomness, and combined with data selection, signal processing and an optimization algorithm for short-term load forecasting. Case studies of electricity power data from New South Wales and Singapore are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed hybrid method is not only robust but also capable of achieving significant improvement compared with the traditional single models and can be an effective and efficient tool for power load forecasting.

  18. Dynamic analysis of sexual organ weight and serum levels of glucose, glycosyl protein, testosterone in male rats with short-term diabetes

    Zou Donghui; Wang Zhongshan; Zhao Hui; Xu Zongge

    2001-01-01

    Objective: To study the effect of short-term diabetes on the changes of sexual organ weights and serum levels of testosterone in male rats. Methods: All rats were divided into control (C) and diabetes (D). Diabetes group was observed on 1 day, 3 days, 5 days, 7 days, 14 days after injection of streptozotocin. All rats were killed for measurement of serum levels of glucose, glycosyl protein, testosterone and weights of sexual organs (testis, epididymis, seminal vesicle and prostate). Results: The serum levels of glucose, glycosyl protein of diabetes group decreased significantly, compared with those of control group; the serum levels of T lowered remarkably compared with control levels after three days of injection of STZ. The weight of epididymis, seminal vesicle and prostate reduced remarkably, compared with control group. The weights of seminal vesicle, prostate negatively correlated with serum levels of glucose and glycosyl protein, and they positively correlated with serum levels of testosterone. Conclusion: The sexual organs (testis, epididymis, seminal vesicle and prostate) of male rats with short-term diabetes were damaged, and the changes of sexual organs closely related with the serum levels of testosterone besides irregular metabolism in diabetes

  19. 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

    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.

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

    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 (NO 2 ) 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.

  1. A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand

    Wang Jianzhou; Zhu Wenjin; Zhang Wenyu; Sun Donghuai

    2009-01-01

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined ε-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the ε-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.

  2. A trend fixed on firstly and seasonal adjustment model combined with the epsilon-SVR for short-term forecasting of electricity demand

    Wang Jianzhou [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhu Wenjin, E-mail: crying.1@hotmail.co [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

    2009-11-15

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined epsilon-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the epsilon-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.

  3. A trend fixed on firstly and seasonal adjustment model combined with the {epsilon}-SVR for short-term forecasting of electricity demand

    Wang, Jianzhou; Zhu, Wenjin [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang, Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun, Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

    2009-11-15

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined {epsilon}-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the {epsilon}-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved. (author)

  4. A short-term neural network memory

    Morris, R.J.T.; Wong, W.S.

    1988-12-01

    Neural network memories with storage prescriptions based on Hebb's rule are known to collapse as more words are stored. By requiring that the most recently stored word be remembered precisely, a new simple short-term neutral network memory is obtained and its steady state capacity analyzed and simulated. Comparisons are drawn with Hopfield's method, the delta method of Widrow and Hoff, and the revised marginalist model of Mezard, Nadal, and Toulouse.

  5. Differential responses of short-term soil respiration dynamics to the experimental addition of nitrogen and water in the temperate semi-arid steppe of Inner Mongolia, China.

    Qi, Yuchun; Liu, Xinchao; Dong, Yunshe; Peng, Qin; He, Yating; Sun, Liangjie; Jia, Junqiang; Cao, Congcong

    2014-04-01

    We examined the effects of simulated rainfall and increasing N supply of different levels on CO2 pulse emission from typical Inner Mongolian steppe soil using the static opaque chamber technique, respectively in a dry June and a rainy August. The treatments included NH4NO3 additions at rates of 0, 5, 10, and 20 g N/(m(2)·year) with or without water. Immediately after the experimental simulated rainfall events, the CO2 effluxes in the watering plots without N addition (WCK) increased greatly and reached the maximum value at 2 hr. However, the efflux level reverted to the background level within 48 hr. The cumulative CO2 effluxes in the soil rang ed from 5.60 to 6.49 g C/m(2) over 48 hr after a single water application, thus showing an increase of approximately 148.64% and 48.36% in the effluxes during both observation periods. By contrast, the addition of different N levels without water addition did not result in a significant change in soil respiration in the short term. Two-way ANOVA showed that the effects of the interaction between water and N addition were insignificant in short-term soil CO2 effluxes in the soil. The cumulative soil CO2 fluxes of different treatments over 48 hr accounted for approximately 5.34% to 6.91% and 2.36% to 2.93% of annual C emission in both experimental periods. These results stress the need for improving the sampling frequency after rainfall in future studies to ensure more accurate evaluation of the grassland C emission contribution. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

  6. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

    Xike Zhang

    2018-05-01

    Full Text Available Daily land surface temperature (LST forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD coupled with Machine Learning (ML algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs and a single residue item. Then, the Partial Autocorrelation Function (PACF is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE, Mean Absolute Error (MAE, Mean Absolute Percentage Error (MAPE, Root Mean Square Error (RMSE, Pearson Correlation Coefficient (CC and Nash-Sutcliffe Coefficient of Efficiency (NSCE. To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN, LSTM and Empirical Mode Decomposition (EMD coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other

  7. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

    Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

    2018-05-21

    Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five

  8. Physical bases of the generation of short-term earthquake precursors: A complex model of ionization-induced geophysical processes in the lithosphere-atmosphere-ionosphere-magnetosphere system

    Pulinets, S. A.; Ouzounov, D. P.; Karelin, A. V.; Davidenko, D. V.

    2015-07-01

    This paper describes the current understanding of the interaction between geospheres from a complex set of physical and chemical processes under the influence of ionization. The sources of ionization involve the Earth's natural radioactivity and its intensification before earthquakes in seismically active regions, anthropogenic radioactivity caused by nuclear weapon testing and accidents in nuclear power plants and radioactive waste storage, the impact of galactic and solar cosmic rays, and active geophysical experiments using artificial ionization equipment. This approach treats the environment as an open complex system with dissipation, where inherent processes can be considered in the framework of the synergistic approach. We demonstrate the synergy between the evolution of thermal and electromagnetic anomalies in the Earth's atmosphere, ionosphere, and magnetosphere. This makes it possible to determine the direction of the interaction process, which is especially important in applications related to short-term earthquake prediction. That is why the emphasis in this study is on the processes proceeding the final stage of earthquake preparation; the effects of other ionization sources are used to demonstrate that the model is versatile and broadly applicable in geophysics.

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

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

    2002-01-01

    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

  10. Induction of a specific strong polyantigenic cellular immune response after short-term chemotherapy controls bacillary reactivation in murine and guinea pig experimental models of tuberculosis.

    Guirado, Evelyn; Gil, Olga; Cáceres, Neus; Singh, Mahavir; Vilaplana, Cristina; Cardona, Pere-Joan

    2008-08-01

    RUTI is a therapeutic vaccine that is generated from detoxified and liposomed Mycobacterium tuberculosis cell fragments that has demonstrated its efficacy in the control of bacillus reactivation after short-term chemotherapy. The aim of this study was to characterize the cellular immune response generated after the therapeutic administration of RUTI and to corroborate the lack of toxicity of the vaccine. Mouse and guinea pig experimental models were infected with a low-dose M. tuberculosis aerosol. RUTI-treated animals showed the lowest bacillary load in both experimental models. RUTI also decreased the percentage of pulmonary granulomatous infiltration in the mouse and guinea pig models. This was not the case after Mycobacterium bovis BCG treatment. Cellular immunity was studied through the characterization of the intracellular gamma interferon (IFN-gamma)-producing cells after the splenocytes' stimulation with M. tuberculosis-specific structural and growth-related antigens. Our data show that the difference between the therapeutic administration of BCG and RUTI resides mainly in the stronger activation of IFN-gamma(+) CD4(+) cells and CD8(+) cells against tuberculin purified protein derivative, ESAT-6, and Ag85B that RUTI generates. Both vaccines also triggered a specific immune response against the M. tuberculosis structural antigens Ag16kDa and Ag38kDa and a marked mRNA expression of IFN-gamma, tumor necrosis factor, interleukin-12, inducible nitric oxide synthase, and RANTES in the lung. The results show that RUTI's therapeutic effect is linked not only to the induction of a Th1 response but also to the stimulation of a quicker and stronger specific immunity against structural and growth-related antigens that reduces both the bacillary load and the pulmonary pathology.

  11. 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

    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

  12. Towards an integrative model of visual short-term memory maintenance: Evidence from the effects of attentional control, load, decay, and their interactions in childhood.

    Shimi, Andria; Scerif, Gaia

    2017-12-01

    Over the past decades there has been a surge of research aiming to shed light on the nature of capacity limits to visual short-term memory (VSTM). However, an integrative account of this evidence is currently missing. We argue that investigating parameters constraining VSTM in childhood suggests a novel integrative model of VSTM maintenance, and that this in turn informs mechanisms of VSTM maintenance in adulthood. Over 3 experiments with 7-year-olds and young adults (total N=206), we provide evidence for multiple cognitive processes interacting to constrain VSTM performance. While age-related increases in storage capacity are undisputable, we replicate the finding that attentional processes control what information will be encoded and maintained in VSTM in the face of increased competition. Therefore, a central process to the current model is attentional refreshment, a mechanism that it is thought to reactivate and strengthen the signal of the visual representations. Critically, here we also show that attentional influences on VSTM are further constrained by additional factors, traditionally studied to the exclusion of each other, such as memory load and temporal decay. We propose that these processes work synergistically in an elegant manner to capture the adult-end state, whereas their less refined efficiency and modulations in childhood account for the smaller VSTM capacity that 7-year-olds demonstrate compared to older individuals. We conclude that going beyond the investigation of single cognitive mechanisms, to their interactions, holds the promise to understand both developing and fully developed maintenance in VSTM. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    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

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

    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.

  15. Implementation of short-term prediction

    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.

  16. Short-Term Wind Speed Forecasting for Power System Operations

    Zhu, Xinxin; Genton, Marc G.

    2012-01-01

    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

  17. Enhancing global climate policy ambition towards a 1.5 °C stabilization: a short-term multi-model assessment

    Vrontisi, Zoi; Luderer, Gunnar; Saveyn, Bert; Keramidas, Kimon; Reis Lara, Aleluia; Baumstark, Lavinia; Bertram, Christoph; Sytze de Boer, Harmen; Drouet, Laurent; Fragkiadakis, Kostas; Fricko, Oliver; Fujimori, Shinichiro; Guivarch, Celine; Kitous, Alban; Krey, Volker; Kriegler, Elmar; Broin, Eoin Ó.; Paroussos, Leonidas; van Vuuren, Detlef

    2018-04-01

    The Paris Agreement is a milestone in international climate policy as it establishes a global mitigation framework towards 2030 and sets the ground for a potential 1.5 °C climate stabilization. To provide useful insights for the 2018 UNFCCC Talanoa facilitative dialogue, we use eight state-of-the-art climate-energy-economy models to assess the effectiveness of the Intended Nationally Determined Contributions (INDCs) in meeting high probability 1.5 and 2 °C stabilization goals. We estimate that the implementation of conditional INDCs in 2030 leaves an emissions gap from least cost 2 °C and 1.5 °C pathways for year 2030 equal to 15.6 (9.0–20.3) and 24.6 (18.5–29.0) GtCO2eq respectively. The immediate transition to a more efficient and low-carbon energy system is key to achieving the Paris goals. The decarbonization of the power supply sector delivers half of total CO2 emission reductions in all scenarios, primarily through high penetration of renewables and energy efficiency improvements. In combination with an increased electrification of final energy demand, low-carbon power supply is the main short-term abatement option. We find that the global macroeconomic cost of mitigation efforts does not reduce the 2020–2030 annual GDP growth rates in any model more than 0.1 percentage points in the INDC or 0.3 and 0.5 in the 2 °C and 1.5 °C scenarios respectively even without accounting for potential co-benefits and avoided climate damages. Accordingly, the median GDP reductions across all models in 2030 are 0.4%, 1.2% and 3.3% of reference GDP for each respective scenario. Costs go up with increasing mitigation efforts but a fragmented action, as implied by the INDCs, results in higher costs per unit of abated emissions. On a regional level, the cost distribution is different across scenarios while fossil fuel exporters see the highest GDP reductions in all INDC, 2 °C and 1.5 °C scenarios.

  18. Short-term Mobility and Increased Partnership Concurrency among Men in Zimbabwe.

    Susan Cassels

    Full Text Available Migration has long been understood as an underlying factor for HIV transmission, and sexual partner concurrency has been increasingly studied as an important component of HIV transmission dynamics. However, less work has examined the role of short-term mobility in sexual partner concurrency using a network approach. Short-term mobility may be a risk for HIV for the migrant's partner as well either through the partner's risk behaviors while the migrant is away, such as the partner having additional partners, or via exposure to the return migrant.Using data from the 2010-11 Zimbabwe Demographic and Health Survey, weighted generalized linear regression models were used to investigate the associations between short-term mobility and partnership concurrency at the individual and partnership levels.At the individual level, we find strong evidence of an association between short-term mobility and concurrency. Men who traveled were more likely to have concurrent partnerships compared to men who did not travel and the relationship was non-linear: each trip was associated with a 2% higher probability of concurrency, with a diminishing risk at 60 trips (p<0.001. At the partnership level, short-term mobility by the male only or both partners was associated with male concurrency. Couples in which the female only traveled exhibited less male concurrency.Short-term mobility has the ability to impact population-level transmission dynamics by facilitating partnership concurrency and thus onward HIV transmission. Short-term migrants may be an important population to target for HIV testing, treatment, or social and behavioral interventions to prevent the spread of HIV.

  19. Parent-Offspring Conflict over Short-Term Mating Strategies

    Spyroulla Georgiou

    2011-12-01

    Full Text Available Individuals engage in short-term mating strategies that enable them to obtain fitness benefits from casual relationships. These benefits, however, count for less and cost more to their parents. On this basis three hypotheses are tested. First, parents and offspring are likely to disagree over short-term mating strategies, with the former considering these as less acceptable than the latter. Second, parents are more likely to disapprove of the short-term mating strategies of their daughters than of their sons. Finally, mothers and fathers are expected to agree on how much they disagree over the short-term mating strategies of their children. Evidence from a sample of 148 Greek-Cypriot families (140 mothers, 105 fathers, 119 daughters, 77 sons provides support for the first two hypotheses and partial support for the third hypothesis. The implications of these findings for understanding family dynamics are further discussed.

  20. Short-term energy outlook, January 1999

    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.

  1. Short-Term Antiretroviral Treatment Recommendations Based on Sensitivity Analysis of a Mathematical Model for HIV Infection of CD₄⁺Τ Cells.

    Croicu, Ana-Maria; Jarrett, Angela M; Cogan, N G; Hussaini, M Yousuff

    2017-11-01

    HIV infection is one of the most difficult infections to control and manage. The most recent recommendations to control this infection vary according to the guidelines used (US, European, WHO) and are not patient-specific. Unfortunately, no two individuals respond to infection and treatment quite the same way. The purpose of this paper is to make use of the uncertainty and sensitivity analysis to investigate possible short-term treatment options that are patient-specific. We are able to identify the most significant parameters that are responsible for ART outcome and to formulate some insights into the ART success.

  2. Evaluation of Short Term Memory Span Function In Children

    Barış ERGÜL

    2016-12-01

    Full Text Available Although details of the information encoded in the short-term memory where it is stored temporarily be recorded in the working memory in the next stage. Repeating the information mentally makes it remain in memory for a long time. Studies investigating the relationship between short-term memory and reading skills that are carried out to examine the relationship between short-term memory processes and reading comprehension. In this study information coming to short-term memory and the factors affecting operation of short term memory are investigated with regression model. The aim of the research is to examine the factors (age, IQ and reading skills that are expected the have an effect on short-term memory in children through regression analysis. One of the assumptions of regression analysis is to examine which has constant variance and normal distribution of the error term. In this study, because the error term is not normally distributed, robust regression techniques were applied. Also, for each technique; coefficient of determination is determined. According to the findings, the increase in age, IQ and reading skills caused the increase in short term memory in children. After applying robust regression techniques, the Winsorized Least Squares (WLS technique gives the highest coefficient of determination.

  3. Short Term Airing by Natural Ventilation

    Heiselberg, Per; Perino, M.

    2010-01-01

    The need to improve the energy efficiency of buildings requires new and more efficient ventilation systems. It has been demonstrated that innovative operating concepts that make use of natural ventilation seem to be more appreciated by occupants. Among the available 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 for temperature and Indoor Air Quality (IAQ) control. However, to promote a wider distribution of these systems an improvement in the knowledge of their working...... 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...

  4. Enhanced long-term and impaired short-term spatial memory in GluA1 AMPA receptor subunit knockout mice: evidence for a dual-process memory model.

    Sanderson, David J; Good, Mark A; Skelton, Kathryn; Sprengel, Rolf; Seeburg, Peter H; Rawlins, J Nicholas P; Bannerman, David M

    2009-06-01

    The GluA1 AMPA receptor subunit is a key mediator of hippocampal synaptic plasticity and is especially important for a rapidly-induced, short-lasting form of potentiation. GluA1 gene deletion impairs hippocampus-dependent, spatial working memory, but spares hippocampus-dependent spatial reference memory. These findings may reflect the necessity of GluA1-dependent synaptic plasticity for short-term memory of recently visited places, but not for the ability to form long-term associations between a particular spatial location and an outcome. This hypothesis is in concordance with the theory that short-term and long-term memory depend on dissociable psychological processes. In this study we tested GluA1-/- mice on both short-term and long-term spatial memory using a simple novelty preference task. Mice were given a series of repeated exposures to a particular spatial location (the arm of a Y-maze) before their preference for a novel spatial location (the unvisited arm of the maze) over the familiar spatial location was assessed. GluA1-/- mice were impaired if the interval between the trials was short (1 min), but showed enhanced spatial memory if the interval between the trials was long (24 h). This enhancement was caused by the interval between the exposure trials rather than the interval prior to the test, thus demonstrating enhanced learning and not simply enhanced performance or expression of memory. This seemingly paradoxical enhancement of hippocampus-dependent spatial learning may be caused by GluA1 gene deletion reducing the detrimental effects of short-term memory on subsequent long-term learning. Thus, these results support a dual-process model of memory in which short-term and long-term memory are separate and sometimes competitive processes.

  5. Short-Term Effects of Low Intensity Thinning on the Fine Root Dynamics of Pinus massoniana Plantations in the Three Gorges Reservoir Area, China

    Yafei Shen

    2017-11-01

    Full Text Available Fine roots play an important role in plant growth as well as carbon (C and nutrient cycling in terrestrial ecosystems. Fine roots are important for understanding the contribution of forests to the global C cycle. Knowledge about this topic is still limited, especially regarding the effects of different forest management practices. This study investigated the seasonal dynamics of fine roots (<2 mm in masson pine (P. massoniana plantations for one year after low intensity thinning by using a sequential soil coring method. The fine roots showed pronounced seasonal dynamics, with a peak of fine root biomass (FRB occurring in September. Significant differences were noted in the seasonal dynamics of FRB for the different diameter size sub-classes (≤0.5 mm, 0.5–1 mm and 1–2 mm; also FRB was inversely related to soil depth. Moreover, the FRB (≤0.5 mm and 0.5–1 mm except 1–2 mm in the thinning plots was greater than that in the control only in the upper soil layer (0–10 cm. Furthermore, the FRB varied significantly with soil temperature, moisture and nutrients depended on the diameter sub-class considered. Significant differences in the soil temperature and moisture levels were noted between low-intensity thinned and control plots. Soil nutrient levels slightly decreased after low-intensity thinning. In addition, there was a more sensitive relationship between the very fine roots (diameter < 0.5 mm and soil nutrients. Our results showed an influence of low-intensity thinning on the fine root dynamics with a different magnitude according to fine root diameter sub-classes. These results provide a theoretical basis to promote the benefits of C cycling in the management of P. massoniana forests.

  6. Short-term facilitation may stabilize parametric working memory trace

    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.

  7. Prospective testing of Coulomb short-term earthquake forecasts

    Jackson, D. D.; Kagan, Y. Y.; Schorlemmer, D.; Zechar, J. D.; Wang, Q.; Wong, K.

    2009-12-01

    Earthquake induced Coulomb stresses, whether static or dynamic, suddenly change the probability of future earthquakes. Models to estimate stress and the resulting seismicity changes could help to illuminate earthquake physics and guide appropriate precautionary response. But do these models have improved forecasting power compared to empirical statistical models? The best answer lies in prospective testing in which a fully specified model, with no subsequent parameter adjustments, is evaluated against future earthquakes. The Center of Study of Earthquake Predictability (CSEP) facilitates such prospective testing of earthquake forecasts, including several short term forecasts. Formulating Coulomb stress models for formal testing involves several practical problems, mostly shared with other short-term models. First, earthquake probabilities must be calculated after each “perpetrator” earthquake but before the triggered earthquakes, or “victims”. The time interval between a perpetrator and its victims may be very short, as characterized by the Omori law for aftershocks. CSEP evaluates short term models daily, and allows daily updates of the models. However, lots can happen in a day. An alternative is to test and update models on the occurrence of each earthquake over a certain magnitude. To make such updates rapidly enough and to qualify as prospective, earthquake focal mechanisms, slip distributions, stress patterns, and earthquake probabilities would have to be made by computer without human intervention. This scheme would be more appropriate for evaluating scientific ideas, but it may be less useful for practical applications than daily updates. Second, triggered earthquakes are imperfectly recorded following larger events because their seismic waves are buried in the coda of the earlier event. To solve this problem, testing methods need to allow for “censoring” of early aftershock data, and a quantitative model for detection threshold as a function of

  8. Short-term energy outlook, July 1998

    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.

  9. Measuring Short-term Energy Security

    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 Brochure provides and overview of the analysis and results. Readers interested in an in-depth discussion of methodology are referred to the MOSES Working Paper.

  10. A Conceptual Model for Calculating the Return of Costs Invested in the Creation of an Economic Security Service, During a Short-Term Period

    Melikhova Tetiana O.

    2018-02-01

    Full Text Available The article is aimed at suggesting methods for calculating the short-term period of return of costs invested in creation of an economic security service. The article considers approaches to calculation of the period of return of costs, advanced at the level of enterprise, which build the methodical basis for definition of such period. At the level of structural subdivisions of enterprise, which do not produce products, it is suggested to use conditional money flow as a source of financing advanced costs. The calculation of the short-term return on investment at the enterprise level provides for: allocation of expenses for the permanent and the replacement parts during the year; determination of the production of money flow and the money flow accumulated during the year. Annual depreciation payments are the basis of fixed costs. Methods of determination of the gross, net, valid, and specified periods of return of costs, advanced during the year for introduction of an economic security service at enterprise, have been suggested.

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

    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. Copyright © 2011 Elsevier Ltd. All rights reserved.

  12. Prediction of short-term and long-term VOC emissions from SBR bitumen-backed carpet under different temperatures

    Yang, X.; Chen, Q.; Bluyssen, P.M.

    1998-01-01

    This paper presents two models for volatile organic compound (VOC) emissions from carpet. One is a numerical model using the computational fluid dynamics (CFD) tech-nique for short-term predictions, the other an analytical model for long-term predictions. The numerical model can (1) deal with

  13. Short-term forecasting of internal migration.

    Frees, E W

    1993-11-01

    A new methodological approach to the forecasting of short-term trends in internal migration in the United States is introduced. "Panel-data (or longitudinal-data) models are used to represent the relationship between destination-specific out-migration and several explanatory variables. The introduction of this methodology into the migration literature is possible because of some new and improved databases developed by the U.S. Bureau of the Census.... Data from the Bureau of Economic Analysis are used to investigate the incorporation of exogenous factors as variables in the model." The exogenous factors considered include employment and unemployment, income, population size of state, and distance between states. The author concludes that "when one...includes additional parameters that are estimable in longitudinal-data models, it turns out that there is little additional information in the exogenous factors that is useful for forecasting." excerpt

  14. Effects of short-term two weeks low intensity plyometrics combined with dynamic stretching training in improving vertical jump height and agility on trained basketball players.

    Ramachandran, Selvam; Pradhan, Binita

    2014-01-01

    Sport specific training in basketball players should focus on vertical jump height and agility in consistent with demands of the sport. Since plyometrics training improves vertical jump height and agility, it can be useful training strategy to improve the performance of basketball players. A convenience sample of thirty professional basketball players were recruited. Following pre-intervention assessment, interventions using plyometrics training and dynamic stretching protocol was administered on the basketball players. The outcome measures were assessed before the intervention and at the end of first and second week. Statistically significant improvements in vertical jump height (31.68 ± 11.64 to 37.57 ± 16.74; P basketball players.

  15. Dynamics of Short-Term Phosphorus Uptake by Intact Mycorrhizal and Non-mycorrhizal Maize Plants Grown in a Circulatory Semi-Hydroponic Cultivation System.

    Garcés-Ruiz, Mónica; Calonne-Salmon, Maryline; Plouznikoff, Katia; Misson, Coralie; Navarrete-Mier, Micaela; Cranenbrouck, Sylvie; Declerck, Stéphane

    2017-01-01

    A non-destructive cultivation system was developed to study the dynamics of phosphorus (Pi) uptake by mycorrhizal and non-mycorrhizal maize plantlets. The system consisted of a plant container connected via silicon tubes to a glass bottle containing a nutrient solution supplemented with Pi. The nutrient solution is pumped with a peristaltic pump to the upper part of the container via the silicon tubes and the solution percolate through the plantlet container back into the glass bottle. Pi is sampled from the glass bottle at regular intervals and concentration evaluated. Maize plantlets were colonized by the AMF Rhizophagus irregularis MUCL 41833 and Pi uptake quantified at fixed intervals (9, 21, and 42 h) from the depletion of the Pi in the nutrient solution flowing through the plantlets containers. Plants and fungus grew well in the perlite substrate. The concentration of Pi in the bottles followed an almost linear decrease over time, demonstrating a depletion of Pi in the circulating solution and a concomitant uptake/immobilization by the plantlet-AMF associates in the containers. The Pi uptake rate was significantly increased in the AMF-colonized plantlets (at 9 and 21 h) as compared to non-colonized plantlets, although no correlation was noticed with plant growth or P accumulation in shoots. The circulatory semi-hydroponic cultivation system developed was adequate for measuring Pi depletion in a nutrient solution and by corollary Pi uptake/immobilization by the plant-AMF associates. The measurements were non-destructive so that the time course of Pi uptake could be monitored without disturbing the growth of the plant and its fungal associate. The system further opens the door to study the dynamics of other micro and macro-nutrients as well as their uptake under stressed growth conditions such as salinity, pollution by hydrocarbon contaminants or potential toxic elements.

  16. Dynamics of Short-Term Phosphorus Uptake by Intact Mycorrhizal and Non-mycorrhizal Maize Plants Grown in a Circulatory Semi-Hydroponic Cultivation System

    Mónica Garcés-Ruiz

    2017-08-01

    Full Text Available A non-destructive cultivation system was developed to study the dynamics of phosphorus (Pi uptake by mycorrhizal and non-mycorrhizal maize plantlets. The system consisted of a plant container connected via silicon tubes to a glass bottle containing a nutrient solution supplemented with Pi. The nutrient solution is pumped with a peristaltic pump to the upper part of the container via the silicon tubes and the solution percolate through the plantlet container back into the glass bottle. Pi is sampled from the glass bottle at regular intervals and concentration evaluated. Maize plantlets were colonized by the AMF Rhizophagus irregularis MUCL 41833 and Pi uptake quantified at fixed intervals (9, 21, and 42 h from the depletion of the Pi in the nutrient solution flowing through the plantlets containers. Plants and fungus grew well in the perlite substrate. The concentration of Pi in the bottles followed an almost linear decrease over time, demonstrating a depletion of Pi in the circulating solution and a concomitant uptake/immobilization by the plantlet-AMF associates in the containers. The Pi uptake rate was significantly increased in the AMF-colonized plantlets (at 9 and 21 h as compared to non-colonized plantlets, although no correlation was noticed with plant growth or P accumulation in shoots. The circulatory semi-hydroponic cultivation system developed was adequate for measuring Pi depletion in a nutrient solution and by corollary Pi uptake/immobilization by the plant-AMF associates. The measurements were non-destructive so that the time course of Pi uptake could be monitored without disturbing the growth of the plant and its fungal associate. The system further opens the door to study the dynamics of other micro and macro-nutrients as well as their uptake under stressed growth conditions such as salinity, pollution by hydrocarbon contaminants or potential toxic elements.

  17. The Demonstration of Short-Term Consolidation.

    Jolicoeur, Pierre; Dell'Acqua, Roberto

    1998-01-01

    Results of seven experiments involving 112 college students or staff using a dual-task approach provide evidence that encoding information into short-term memory involves a distinct process termed short-term consolidation (STC). Results suggest that STC has limited capacity and that it requires central processing mechanisms. (SLD)

  18. Short-Term Intercultural Psychotherapy: Ethnographic Inquiry

    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…

  19. The Conceptual Model of Calculating the Return Period of the Costs for Creation of the Enterprise’s Economic Security Service in the Short-Term Period

    Melikhova Tetiana O.

    2018-03-01

    Full Text Available Determination of the return period of the costs, advanced for the creation of economic security service of enterprise during a year, involves consideration of interaction of the conditional money flow, accumulated for a certain number of months, and the constant costs. The main component of the constant costs are the annual depreciation deductions. The return period is considered as gross, net, valid, and specified. The gross (net, valid, and specified return period is the time, wherein the gross conditional money flow, equal to the advanced costs, will be accumulated. The gross return period, taking account of the effect of time factor, is proposed to be defined as the ratio of annual depreciation deductions increased by the annual compounding coefficient to the conditional average monthly gross money flow, increased by the average monthly inflation index. As for the short-term period, a relationship between the gross, net, valid, and specified return periods of the costs, advanced to the creation of the economic security service, has been identified. The net (valid, specified return period is equal to the gross period adjusted to the coefficient of excess of the gross conditional money flow, accumulated in the gross period, over the net (valid, specified conditional cash flow.

  20. Panorama 2012 - Short-term trends in the gas industry

    Lecarpentier, Armelle

    2011-12-01

    Against the background of an energy market beset by the Fukushima crisis, the Arab spring and economic uncertainty, 2011 saw dynamic growth in demand for natural gas, although developments varied widely from region to region. New trends are emerging in the gas market, and these will have both short-term and longer-term impacts on how the industry develops. (author)

  1. Short term memory in echo state networks

    Jaeger, H.

    2001-01-01

    The report investigates the short-term memory capacity of echo state recurrent neural networks. A quantitative measure MC of short-term memory capacity is introduced. The main result is that MC 5 N for networks with linear Output units and i.i.d. input, where N is network size. Conditions under which these maximal memory capacities are realized are described. Several theoretical and practical examples demonstrate how the short-term memory capacities of echo state networks can be exploited for...

  2. Nuisance forecasting. Univariate modelling and very-short-term forecasting of winter smog episodes; Immissionsprognose. Univariate Modellierung und Kuerzestfristvorhersage von Wintersmogsituationen

    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 natural gas consumption forecasting

    Potocnik, P.; Govekar, E.; Grabec, I.

    2007-01-01

    Energy forecasting requirements for Slovenia's natural gas market were investigated along with the cycles of natural gas consumption. This paper presented a short-term natural gas forecasting approach where the daily, weekly and yearly gas consumption were analyzed and the information obtained was incorporated into the forecasting model for hourly forecasting for the next day. The natural gas market depends on forecasting in order to optimize the leasing of storage capacities. As such, natural gas distribution companies have an economic incentive to accurately forecast their future gas consumption. The authors proposed a forecasting model with the following properties: two submodels for the winter and summer seasons; input variables including past consumption data, weather data, weather forecasts and basic cycle indexes; and, a hierarchical forecasting structure in which a daily model was used as the basis, with the hourly forecast obtained by modeling the relative daily profile. This proposed method was illustrated by a forecasting example for Slovenia's natural gas market. 11 refs., 11 figs

  4. Fast Weight Long Short-Term Memory

    Keller, T. Anderson; Sridhar, Sharath Nittur; Wang, Xin

    2018-01-01

    Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs). As recent studies introduced fast weights only to regular RNNs, it is unknown whether fast weight memory is beneficial to gated RNNs. In this work, we report a significant synergy between long short-term memory (LSTM) networks and fast weight associative memories. We show that this combination, in learning associative retrie...

  5. Short-term energy outlook, April 1999

    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. Development of Dynamic Environmental Effect Calculation Model

    Jeong, Chang Joon; Ko, Won Il

    2010-01-01

    The short-term, long-term decay heat, and radioactivity are considered as main environmental parameters of SF and HLA. In this study, the dynamic calculation models for radioactivity, short-term decay heat, and long-term heat load of the SF are developed and incorporated into the Doneness code. The spent fuel accumulation has become a major issue for sustainable operation of nuclear power plants. If a once-through fuel cycle is selected, the SF will be disposed into the repository. Otherwise, in case of fast reactor or reuse cycle, the SF will be reprocessed and the high level waste will be disposed

  7. A Randomized Double-Blind Controlled Trial Comparing Davanloo Intensive Short-Term Dynamic Psychotherapy as Internet-Delivered Vs Treatment as Usual for Medically Unexplained Pain: A 6-Month Pilot Study.

    Chavooshi, Behzad; Mohammadkhani, Parvaneh; Dolatshahi, Behrouz

    2016-01-01

    Research has shown that Intensive Short-Term Dynamic Psychotherapy (ISTDP) can effectively decrease pain intensity and improve quality of life in patients with medically unexplained pain. Understanding that not all patients with medically unexplained pain have access to in-person ISTDP, this study aims to investigate the efficacy of an Internet-delivered ISTDP for individuals with medically unexplained pain using Skype in comparison with treatment as usual. In this randomized controlled trial, 100 patients were randomly allocated into Internet-delivered ISTDP (n = 50) and treatment-as- usual (n = 50) groups. Treatment intervention consisted of 16 weekly, hour-long therapy sessions. The primary outcome was perceived pain assessed using the Numeric Pain Rating Scale. The secondary outcome included Depression Anxiety Stress Scale-21, Emotion Regulation Questionnaire, Mindful Attention Awareness Scale, and Quality-of-Life Inventory. Blind assessments were conducted at the baseline, posttreatment, and at a 6-month follow-up. In the intention-to-treat analysis, pain symptoms in the intervention group were significantly reduced (p Skype can significantly improve pain intensity and clinical symptoms of medically unexplained pain. Copyright © 2016 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.

  8. Holding Multiple Items in Short Term Memory: A Neural Mechanism

    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

  9. LANGUAGE REPETITION AND SHORT-TERM MEMORY: AN INTEGRATIVE FRAMEWORK

    Steve eMajerus

    2013-07-01

    Full Text Available Short-term maintenance of verbal information is a core factor of language repetition, especially when reproducing multiple or unfamiliar stimuli. Many models of language processing locate the verbal short-term maintenance function in the left posterior superior temporo-parietal area and its connections with the inferior frontal gyrus. However, research in the field of short-term memory has implicated bilateral fronto-parietal networks, involved in attention and serial order processing, as being critical for the maintenance and reproduction of verbal sequences. We present here an integrative framework aimed at bridging research in the language processing and short-term memory fields. This framework considers verbal short-term maintenance as an emergent function resulting from synchronized and integrated activation in dorsal and ventral language processing networks as well as fronto-parietal attention and serial order processing networks. To-be-maintained item representations are temporarily activated in the dorsal and ventral language processing networks, novel phoneme and word serial order information is proposed to be maintained via a right fronto-parietal serial order processing network, and activation in these different networks is proposed to be coordinated and maintained via a left fronto-parietal attention processing network. This framework provides new perspectives for our understanding of information maintenance at the nonword-, word- and sentence-level as well as of verbal maintenance deficits in case of brain injury.

  10. Language repetition and short-term memory: an integrative framework.

    Majerus, Steve

    2013-01-01

    Short-term maintenance of verbal information is a core factor of language repetition, especially when reproducing multiple or unfamiliar stimuli. Many models of language processing locate the verbal short-term maintenance function in the left posterior superior temporo-parietal area and its connections with the inferior frontal gyrus. However, research in the field of short-term memory has implicated bilateral fronto-parietal networks, involved in attention and serial order processing, as being critical for the maintenance and reproduction of verbal sequences. We present here an integrative framework aimed at bridging research in the language processing and short-term memory fields. This framework considers verbal short-term maintenance as an emergent function resulting from synchronized and integrated activation in dorsal and ventral language processing networks as well as fronto-parietal attention and serial order processing networks. To-be-maintained item representations are temporarily activated in the dorsal and ventral language processing networks, novel phoneme and word serial order information is proposed to be maintained via a right fronto-parietal serial order processing network, and activation in these different networks is proposed to be coordinated and maintained via a left fronto-parietal attention processing network. This framework provides new perspectives for our understanding of information maintenance at the non-word-, word- and sentence-level as well as of verbal maintenance deficits in case of brain injury.

  11. Holding multiple items in short term memory: a neural mechanism.

    Edmund T Rolls

    Full Text Available 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.

  12. Holding multiple items in short term memory: a neural mechanism.

    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.

  13. Short-term memory across eye blinks.

    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.

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

    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

  15. Short-Term Memory, Executive Control, and Children's Route Learning

    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. Scalable data-driven short-term traffic prediction

    Friso, K.; Wismans, L. J.J.; Tijink, M. B.

    2017-01-01

    Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models-which do not scale very well to large networks, computationally-or on data-driven methods for freeways, leaving out urban arterials completely. Urban

  17. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

    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.

  18. Visual Short-Term Memory Complexity

    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. Smart Demand for Improving Short-term Voltage Control on Distribution Networks

    Garcia-Valle, Rodrigo; P. Da Silva, Luiz C.; Xu, Zhao

    2009-01-01

    customer integration to aid power system performance is almost inevitable. This study introduces a new type of smart demand side technology, denoted demand as voltage controlled reserve (DVR), to improve short-term voltage control, where customers are expected to play a more dynamic role to improve voltage...... control. The technology can be provided by thermostatically controlled loads as well as other types of load. This technology is proven to be effective in case of distribution systems with a large composition of induction motors, where the voltage presents a slow recovery characteristic due to deceleration...... of the motors during faults. This study presents detailed models, discussion and simulation tests to demonstrate the technical viability and effectiveness of the DVR technology for short-term voltage control....

  20. FORECASTING ENERGY CONSUMPTION IN SHORT-TERM AND LONG-TERM PERIOD BY USING ARIMAX MODEL IN THE CONSTRUCTION AND MATERIALS SECTOR IN THAILAND

    Pruethsan Sutthichaimethee

    2017-07-01

    Full Text Available This study aims to analyze the forecasting of energy consumption in the Construction and Materials sectors. The scope of the study covers the forecasting periods of energy consumption for the next 10 years, 2017-2026, 20 years, 2017-2036, and 30 years, 2017-2046, by using ARIMAX Model. The prediction results show that these models are effective in the forecast measured by RMSE, MAE, and MAPE. The results show that from the first model (2,1,1, which predicted the duration of 10 years, 2017-2026, indicates that Thailand has increased an energy consumption rate with the average of 18.09%, while the second model (2,1,2 with the prediction of 20 years, 2017-2036, Thailand arises its energy consumption up to 37.32%. In addition, the third model (2,1,3 predicted the duration of 30 years from 2017 to 2046, and it has found that Thailand increases its energy consumption up to 49.72%.

  1. Short term benefits for laparoscopic colorectal resection.

    Schwenk, W; Haase, O; Neudecker, J; Müller, J M

    2005-07-20

    Colorectal resections are common surgical procedures all over the world. Laparoscopic colorectal surgery is technically feasible in a considerable amount of patients under elective conditions. Several short-term benefits of the laparoscopic approach to colorectal resection (less pain, less morbidity, improved reconvalescence and better quality of life) have been proposed. This review compares laparoscopic and conventional colorectal resection with regards to possible benefits of the laparoscopic method in the short-term postoperative period (up to 3 months post surgery). We searched MEDLINE, EMBASE, CancerLit, and the Cochrane Central Register of Controlled Trials for the years 1991 to 2004. We also handsearched the following journals from 1991 to 2004: British Journal of Surgery, Archives of Surgery, Annals of Surgery, Surgery, World Journal of Surgery, Disease of Colon and Rectum, Surgical Endoscopy, International Journal of Colorectal Disease, Langenbeck's Archives of Surgery, Der Chirurg, Zentralblatt für Chirurgie, Aktuelle Chirurgie/Viszeralchirurgie. Handsearch of abstracts from the following society meetings from 1991 to 2004: American College of Surgeons, American Society of Colorectal Surgeons, Royal Society of Surgeons, British Assocation of Coloproctology, Surgical Association of Endoscopic Surgeons, European Association of Endoscopic Surgeons, Asian Society of Endoscopic Surgeons. All randomised-controlled trial were included regardless of the language of publication. No- or pseudorandomised trials as well as studies that followed patient's preferences towards one of the two interventions were excluded, but listed separately. RCT presented as only an abstract were excluded. Results were extracted from papers by three observers independently on a predefined data sheet. Disagreements were solved by discussion. 'REVMAN 4.2' was used for statistical analysis. Mean differences (95% confidence intervals) were used for analysing continuous variables. If

  2. Long-term manure carbon sequestration in soil simulated with the Daisy model on the basis of short-term incubation study

    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...... application of the two manures (70 kg organic manure N ha-1 plus 90 kg mineral N ha-1) and compared with a mineral N reference (120 kg N ha-1 yr-1). Carbon retention in soil was related to the initial C in non-digested manure, and after 52 years of repeated manure application extra C retention was equivalent...... to 41% for non-digested and 35% for digested manure in the loamy sand. In the sandy soil corresponding C retention was 37 and 29%. The higher C retention from non-digested compared to digested manure differed from the incubation study and was mainly due to the model response to the optimized parameters...

  3. Short-term adaptations as a response to travel time: results of a stated adaptation experimentincreases

    Psarra, I.; Arentze, T.A.; Timmermans, H.J.P.

    2016-01-01

    This study focused on short-term dynamics of activity-travel behavior as a response to travel time increases. It is assumed that short-term changes are triggered by stress, which is defined as the deviation between an individual’s aspirations and his or her daily experiences. When stress exceeds a

  4. The Use of Indirect Estimates of Soil Moisture to Initialize Coupled Models and its Impact on Short-Term and Seasonal Simulations

    Lapenta, William M.; Crosson, William; Dembek, Scott; Lakhtakia, Mercedes

    1998-01-01

    It is well known that soil moisture is a characteristic of the land surface that strongly affects the partitioning of outgoing radiation into sensible and latent heat which significantly impacts both weather and climate. Detailed land surface schemes are now being coupled to mesoscale atmospheric models in order to represent the effect of soil moisture upon atmospheric simulations. However, there is little direct soil moisture data available to initialize these models on regional to continental scales. As a result, a Soil Hydrology Model (SHM) is currently being used to generate an indirect estimate of the soil moisture conditions over the continental United States at a grid resolution of 36 Km on a daily basis since 8 May 1995. The SHM is forced by analyses of atmospheric observations including precipitation and contains detailed information on slope soil and landcover characteristics.The purpose of this paper is to evaluate the utility of initializing a detailed coupled model with the soil moisture data produced by SHM.

  5. Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

    Stewart, Ian B.; Arendt, Dustin L.; Bell, Eric B.; Volkova, Svitlana

    2017-05-17

    Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. This work addresses several important tasks of visualizing and predicting short term text representation shift, i.e. the change in a word’s contextual semantics. We study the relationship between short-term concept drift and representation shift on a large social media corpus – VKontakte collected during the Russia-Ukraine crisis in 2014 – 2015. We visualize short-term representation shift for example keywords and build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift. We show that short-term representation shift can be accurately predicted up to several weeks in advance and that visualization provides insight into meaning change. Our approach can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event forecasting in social media.

  6. Retrieval-Induced Inhibition in Short-Term Memory.

    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.

  7. Short-term energy outlook annual supplement, 1993

    1993-01-01

    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 third quarter of 1993 through the fourth quarter of 1994. Values for the second 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. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding

  8. Short-Term Energy Outlook: Quarterly projections. Fourth quarter 1993

    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.

  9. Short-term energy outlook: Quarterly projections, Third quarter 1992

    1992-08-01

    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 principal users of the Outlook are managers and energy analysts in private industry and government. The forecast period for this issue of the Outlook extends from the third quarter of 1992 through the fourth quarter of 1993. Values for the second quarter of 1992, 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. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding

  10. Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008-2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion.

    Liao, Jiaqiang; Yu, Shicheng; Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

    2016-01-01

    Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008-2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse "V" shape and "V" shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across

  11. Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008–2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion

    Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

    2016-01-01

    Background Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. Methods The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008–2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. Results The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse “V” shape and “V” shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. Conclusion We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic

  12. Short-term forecasting of the chloride content in the mineral waters of the Ustroń Health Resort using SARIMA and Holt-Winters models

    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.

  13. [Short-term crisis psychotherapy in children with posttraumatic stress disorder in the frames of the "Dobryakov-Nikol'skaya" rehabilitation model].

    Dobriakov, I V; Nikol'skaia, I M

    2009-01-01

    Peculiarities of the model of medical-psychological help elaborated by the authors are presented on the example of the psychotherapeutic work with children, victims of the terror act in Beslan. A complex of methods which includes "debriefing", and using of tails and games in the combination with the technique of serial drawings and stories was applied in accordance to rules of crisis psychotherapy. Results demonstrate that the methods described allow to get into contact to the child, discover and remove his/her emotional experience related to the traumatic situation.

  14. Cascades in model steels: The effect of cementite (Fe3C) and Cr23C6 particles on short-term crystal damage

    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.

  15. Proteomic analysis of a model unicellular green alga, Chlamydomonas reinhardtii, during short-term exposure to irradiance stress reveals significant down regulation of several heat-shock proteins.

    Mahong, Bancha; Roytrakul, Suttiruk; Phaonaklop, Narumon; Wongratana, Janewit; Yokthongwattana, Kittisak

    2012-03-01

    Oxygenic photosynthetic organisms often suffer from excessive irradiance, which cause harmful effects to the chloroplast proteins and lipids. Photoprotection and the photosystem II repair processes are the mechanisms that plants deploy to counteract the drastic effects from irradiance stress. Although the protective and repair mechanisms seemed to be similar in most plants, many species do confer different level of tolerance toward high light. Such diversity may originate from differences at the molecular level, i.e., perception of the light stress, signal transduction and expression of stress responsive genes. Comprehensive analysis of overall changes in the total pool of proteins in an organism can be performed using a proteomic approach. In this study, we employed 2-DE/LC-MS/MS-based comparative proteomic approach to analyze total proteins of the light sensitive model unicellular green alga Chlamydomonas reinhardtii in response to excessive irradiance. Results showed that among all the differentially expressed proteins, several heat-shock proteins and molecular chaperones were surprisingly down-regulated after 3-6 h of high light exposure. Discussions were made on the possible involvement of such down regulation and the light sensitive nature of this model alga.

  16. Short-term exposure to dim light at night disrupts rhythmic behaviors and causes neurodegeneration in fly models of tauopathy and Alzheimer's disease.

    Kim, Mari; Subramanian, Manivannan; Cho, Yun-Ho; Kim, Gye-Hyeong; Lee, Eunil; Park, Joong-Jean

    2018-01-08

    The accumulation and aggregation of phosphorylated tau proteins in the brain are the hallmarks for the onset of Alzheimer's disease (AD). In addition, disruptions in circadian rhythms (CRs) with altered sleep-wake cycles, dysregulation of locomotion, and increased memory defects have been reported in patients with AD. Drosophila flies that have an overexpression of human tau protein in neurons exhibit most of the symptoms of human patients with AD, including locomotion defects and neurodegeneration. Using the fly model for tauopathy/AD, we investigated the effects of an exposure to dim light at night on AD symptoms. We used a light intensity of 10 lux, which is considered the lower limit of light pollution in many countries. After the tauopathy flies were exposed to the dim light at night for 3 days, the flies showed disrupted CRs, altered sleep-wake cycles due to increased pTau proteins and neurodegeneration, in the brains of the AD flies. The results indicate that the nighttime exposure of tauopathy/AD model Drosophila flies to dim light disrupted CR and sleep-wake behavior and promoted neurodegeneration. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Effects of short-term variability of meteorological variables on soil temperature in permafrost regions

    Beer, Christian; Porada, Philipp; Ekici, Altug; Brakebusch, Matthias

    2018-03-01

    Effects of the short-term temporal variability of meteorological variables on soil temperature in northern high-latitude regions have been investigated. For this, a process-oriented land surface model has been driven using an artificially manipulated climate dataset. Short-term climate variability mainly impacts snow depth, and the thermal diffusivity of lichens and bryophytes. These impacts of climate variability on insulating surface layers together substantially alter the heat exchange between atmosphere and soil. As a result, soil temperature is 0.1 to 0.8 °C higher when climate variability is reduced. Earth system models project warming of the Arctic region but also increasing variability of meteorological variables and more often extreme meteorological events. Therefore, our results show that projected future increases in permafrost temperature and active-layer thickness in response to climate change will be lower (i) when taking into account future changes in short-term variability of meteorological variables and (ii) when representing dynamic snow and lichen and bryophyte functions in land surface models.

  18. Short-Term Research Experiences with Teachers in Earth and Planetary Sciences and a Model for Integrating Research into Classroom Inquiry

    Morgan, P.; Bloom, J. W.

    2006-12-01

    For the past three summers, we have worked with in-service teachers on image processing, planetary geology, and earthquake and volcano content modules using inquiry methods that ended with mini-research experiences. Although almost all were science teachers, very few could give a reasonable definition of science at the start of the modules, and very few had a basic grasp of the processes of scientific research and could not include substantive scientific inquiry into their lessons. To build research understanding and confidence, an instructor-student interaction model was used in the modules. Studies have shown that children who participate in classrooms as learning and inquiry communities develop more complex understandings. The same patterns of complex understandings have resulted in similarly structured professional communities of teachers. The model is based on professional communities, emphasizing from the beginning that inquiry is a form of research. Although the actual "research" component of the modules was short, the teachers were identified as professionals and researchers from the start. Research/inquiry participation is therefore an excellent example by which to allow their teachers to learn. Initially the teachers were very reluctant to pose questions. As they were encouraged to share, collaborate, and support each other, the role of the instructor became less of a leader and more of a facilitator, and the confidence of the teachers as professionals and researchers grew. One teacher even remarked, "This is how we should be teaching our kids!' Towards the end of the modules the teachers were ready for their mini- research projects and collaborated in teams of 2-4. They selected their own research topics, but were guided toward research questions that required data collection (from existing studies), some data manipulation, interpretation, and drawing conclusions with respect to the original question. The teachers were enthusiastic about all of their

  19. A model-independent comparison of the rates of uptake and short term retention of 47Ca and 85Sr by the skeleton.

    Reeve, J; Hesp, R

    1976-12-22

    1. A method has been devised for comparing the impulse response functions of the skeleton for two or more boneseeking tracers, and for estimating the contribution made by measurement errors to the differences between any pair of impulse response functions. 2. Comparisons were made between the calculated impulse response functions for 47Ca and 85Sr obtained in simultaneous double tracer studies in sixteen subjects. Collectively the differences between the 47Ca and 85Sr functions could be accounted for entirely by measurement errors. 3. Because the calculation of an impulse response function requires fewer a priori assumptions than other forms of mathematical analysis, and automatically corrects for differences induced by recycling of tracer and non-identical rates of excretory plasma clearance of tracer, it is concluded that differences shown in previous in vivo studies between the fluxes of Ca and Sr into bone can be fully accounted for by undetermined oversimplifications in the various mathematical models used to analyse the results of those studies. 85Sr is therefore an adequate tracer for bone calcium in most in vivo studies.

  20. Short-term effect of zoledronic acid upon fracture resistance of the mandibular condyle and femoral head in an animal model.

    Camacho-Alonso, Fabio; López-Jornet, Pía; Vicente-Hernández, Ascensión

    2013-05-01

    The aim of this study was to compare the effects in terms of resistance to fracture of the mandibular condyle and femoral head following different doses of zoledronic acid in an animal model. A total of 80 adult male Sprague-Dawley rats were included in a prospective randomized study. The animals were randomly divided into four groups of 20 rats each. Group 1 (control) received sterile saline solution, while groups 2, 3 and 4 received a accumulated dose of 0.2 mg, 0.4 mg and 0.6 mg of zoledronic acid, respectively. The animals were sacrificed 28 days after the last dose, and the right hemimandible and the right femur were removed. The fracture strength was measured (in Newtons) with a universal test machine using a 1 kN load connected to a metal rod with one end angled at 30 degrees. The cross-head speed was 1 mm/min. Later, the specimens were observed under a scanning electron microscope with backscattered electron imaging (SEM-BSE). At last, chemical analysis and elemental mapping of the mineral bone composition were generated using a microanalytical system based on energy-dispersive and X-ray spectrometry (EDX). A total of 160 fracture tests were performed. The fracture resistance increased in mandible and femur with a higher accumulated dose of zoledronic acid. Statistically significant differences were recorded versus the controls with all the studies groups. The chemical analysis in mandible showed a significantly increased of calcium and phosphorous to compare the control with all of the study groups; however, in femur no statistically significant differences between the four study groups were observed. The administration of bisphosphonates increases the fracture resistance in mandible and femur.

  1. Short-term LNG-markets

    Eldegard, Tom; Lund, Arne-Christian; Miltersen, Kristian; Rud, Linda

    2005-01-01

    The global Liquefied Natural Gas (LNG) industry has experienced substantial growth in the past decades. In the traditional trade patterns of LNG the product has typically been handled within a dedicated chain of plants and vessels fully committed by long term contracts or common ownership, providing risk sharing of large investments in a non-liquid market. Increasing gas prices and substantial cost reductions in all parts of the LNG chain have made LNG projects viable even if only part of the capacity is secured by long-term contracts, opening for more flexible trade of the remainder. Increasing gas demand, especially in power generation, combined with cost reductions in the cost of LNG terminals, open new markets for LNG. For the LNG supplier, the flexibility of shifting volumes between regions represents an additional value. International trade in LNG has been increasing, now accounting for more than one fifth of the world's cross-border gas trade. Despite traditional vertical chain bonds, increased flexibility has contributed in fact to an increasing LNG spot trade, representing 8% of global trade in 2002. The focus of this paper is on the development of global short-term LNG markets, and their role with respect to efficiency and security of supply in European gas markets. Arbitrage opportunities arising from price differences between regional markets (such as North America versus Europe) are important impetuses for flexible short-term trade. However, the short-term LNG trade may suffer from problems related to market access, e.g. limited access to terminals and regulatory issues, as well as rigidities connected to vertical binding within the LNG chain. Important issues related to the role of short-term LNG-trade in the European gas market are: Competition, flexibility in meeting peak demand, security of supply and consequences of differences in pricing policies (oil-linked prices in Europe and spot market prices in North America). (Author)

  2. Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding

    Zhibin Yu

    2017-08-01

    Full Text Available Understanding of human intention by observing a series of human actions has been a challenging task. In order to do so, we need to analyze longer sequences of human actions related with intentions and extract the context from the dynamic features. The multiple timescales recurrent neural network (MTRNN model, which is believed to be a kind of solution, is a useful tool for recording and regenerating a continuous signal for dynamic tasks. However, the conventional MTRNN suffers from the vanishing gradient problem which renders it impossible to be used for longer sequence understanding. To address this problem, we propose a new model named Continuous Timescale Long-Short Term Memory (CTLSTM in which we inherit the multiple timescales concept into the Long-Short Term Memory (LSTM recurrent neural network (RNN that addresses the vanishing gradient problem. We design an additional recurrent connection in the LSTM cell outputs to produce a time-delay in order to capture the slow context. Our experiments show that the proposed model exhibits better context modeling ability and captures the dynamic features on multiple large dataset classification tasks. The results illustrate that the multiple timescales concept enhances the ability of our model to handle longer sequences related with human intentions and hence proving to be more suitable for complex tasks, such as intention recognition.

  3. Short-term uranium price formation: a methodology

    Hsieh, L.Y.; de Graffenried, C.L.

    1987-01-01

    One of the major problems in analyzing the short-term uranium market is the lack of a well-defined spot market price. The two primary sources of price data covering the US uranium market are the series published by the US Dept. of Energy (DOE) and by the Nuclear Exchange Corporation (NUEXCO), a private brokerage firm. Because of the differences in both definition and coverage, these two series are not directly comparable. In this study, an econometric model was developed for analyzing the interrelationship between short-term uranium price (NUEXCO exchange value), supply, demand, and future price expectations formed by market participants. The validity of this model has been demonstrated by the fact that all simulation statistics derived are highly significant. Three forecasting scenarios were developed in this study

  4. Short-Term Wind Speed Forecasting for Power System Operations

    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.

  5. Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

    Chen, Kuilin; Yu, Jie

    2014-01-01

    Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations

  6. Conversion of short-term to long-term memory in the novel object recognition paradigm.

    Moore, Shannon J; Deshpande, Kaivalya; Stinnett, Gwen S; Seasholtz, Audrey F; Murphy, Geoffrey G

    2013-10-01

    It is well-known that stress can significantly impact learning; however, whether this effect facilitates or impairs the resultant memory depends on the characteristics of the stressor. Investigation of these dynamics can be confounded by the role of the stressor in motivating performance in a task. Positing a cohesive model of the effect of stress on learning and memory necessitates elucidating the consequences of stressful stimuli independently from task-specific functions. Therefore, the goal of this study was to examine the effect of manipulating a task-independent stressor (elevated light level) on short-term and long-term memory in the novel object recognition paradigm. Short-term memory was elicited in both low light and high light conditions, but long-term memory specifically required high light conditions during the acquisition phase (familiarization trial) and was independent of the light level during retrieval (test trial). Additionally, long-term memory appeared to be independent of stress-mediated glucocorticoid release, as both low and high light produced similar levels of plasma corticosterone, which further did not correlate with subsequent memory performance. Finally, both short-term and long-term memory showed no savings between repeated experiments suggesting that this novel object recognition paradigm may be useful for longitudinal studies, particularly when investigating treatments to stabilize or enhance weak memories in neurodegenerative diseases or during age-related cognitive decline. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Short-term Forecasting Tools for Agricultural Nutrient Management.

    Easton, Zachary M; Kleinman, Peter J A; Buda, Anthony R; Goering, Dustin; Emberston, Nichole; Reed, Seann; Drohan, Patrick J; Walter, M Todd; Guinan, Pat; Lory, John A; Sommerlot, Andrew R; Sharpley, Andrew

    2017-11-01

    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 or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

  8. Robust Short-Term Memory without Synaptic Learning

    Johnson, Samuel; Marro, J.; Torres, Joaquin 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 inf...

  9. Neural circuit mechanisms of short-term memory

    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.

  10. Comparison of costs and outcomes of dapagliflozin with other glucose-lowering therapy classes added to metformin using a short-term cost-effectiveness model in the US setting.

    Chakravarty, Abhiroop; Rastogi, Mohini; Dhankhar, Praveen; Bell, Kelly F

    2018-05-01

    To compare 1-year costs and benefits of dapagliflozin (DAPA), a sodium-glucose cotransporter-2 (SGLT-2) inhibitor, with those of other treatments for type 2 diabetes (T2D), such as glucagon-like peptide-1 receptor agonists (GLP-1RAs), sulfonylureas (SUs), thiazolidinediones (TZDs), and dipeptidyl peptidase-4 inhibitors (DPP-4i), all combined with metformin. A short-term decision-analytic model with a 1-year time horizon was developed from a payer's perspective in the United States setting. Costs and benefits associated with four clinical end-points (glycated hemoglobin [A1C], body weight, systolic blood pressure [SBP], and risk of hypoglycemia) were evaluated in the analysis. The impact of DAPA and other glucose-lowering therapy classes on these clinical end-points was estimated from a network meta-analysis (NMA). Data for costs and quality-adjusted life-years (QALYs) associated with a per-unit change in these clinical end-points were taken from published literature. Drug prices were taken from an annual wholesale price list. All costs were inflation-adjusted to December 2016 costs using the medical care component of the consumer price index. Total costs (both medical and drug costs), total QALYs, and incremental cost-effectiveness ratios (ICERs) were estimated. Sensitivity analyses (SA) were performed to explore uncertainty in the inputs. To assess face validity, results from the short-term model were compared with long-term models published for these drugs. The total annual medical cost for DAPA was less than that for GLP-1RA ($186 less), DPP-4i ($1,142 less), SU ($2,474 less), and TZD ($1,640 less). Treatment with DAPA resulted in an average QALY gain of 0.0107, 0.0587, 0.1137, and 0.0715 per treated patient when compared with GLP-1RA, DPP-4i, SU, and TZD, respectively. ICERs for DAPA vs SU and TZD were $19,005 and $25,835, respectively. DAPA was a cost-saving option when compared with GLP-1RAs and DPP-4is. Among all four clinical end-points, change in weight

  11. Is visual short-term memory depthful?

    Reeves, Adam; Lei, Quan

    2014-03-01

    Does visual short-term memory (VSTM) depend on depth, as it might be if information was stored in more than one depth layer? Depth is critical in natural viewing and might be expected to affect retention, but whether this is so is currently unknown. Cued partial reports of letter arrays (Sperling, 1960) were measured up to 700 ms after display termination. Adding stereoscopic depth hardly affected VSTM capacity or decay inferred from total errors. The pattern of transposition errors (letters reported from an uncued row) was almost independent of depth and cue delay. We conclude that VSTM is effectively two-dimensional. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Short-term energy outlook. Quarterly projections, first quarter 1995

    1995-02-01

    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). The forecast period for this issue of the Outlook extends from the first quarter of 1995 through the fourth quarter of 1996. Values for the fourth quarter of 1994, however, are preliminary EIA estimates 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, compiled into the first quarter 1995 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 database is archived quarterly and is available from the National Technical Information Service. The cases are produced using the Short-Term Integrated Forecasting System (STIFS). 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. The EIA model is available on computer tape from the National Technical Information Service

  13. Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

    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.

  14. Very short-term probabilistic forecasting of wind power with generalized logit-Normal distributions

    Pinson, Pierre

    2012-01-01

    and probability masses at the bounds. Both auto-regressive and conditional parametric auto-regressive models are considered for the dynamics of their location and scale parameters. Estimation is performed in a recursive least squares framework with exponential forgetting. The superiority of this proposal over......Very-short-term probabilistic forecasts, which are essential for an optimal management of wind generation, ought to account for the non-linear and double-bounded nature of that stochastic process. They take here the form of discrete–continuous mixtures of generalized logit–normal distributions...

  15. Auditory short-term memory behaves like visual short-term memory.

    Kristina M Visscher

    2007-03-01

    Full Text Available Are the information processing steps that support short-term sensory memory common to all the senses? Systematic, psychophysical comparison requires identical experimental paradigms and comparable stimuli, which can be challenging to obtain across modalities. Participants performed a recognition memory task with auditory and visual stimuli that were comparable in complexity and in their neural representations at early stages of cortical processing. The visual stimuli were static and moving Gaussian-windowed, oriented, sinusoidal gratings (Gabor patches; the auditory stimuli were broadband sounds whose frequency content varied sinusoidally over time (moving ripples. Parallel effects on recognition memory were seen for number of items to be remembered, retention interval, and serial position. Further, regardless of modality, predicting an item's recognizability requires taking account of (1 the probe's similarity to the remembered list items (summed similarity, and (2 the similarity between the items in memory (inter-item homogeneity. A model incorporating both these factors gives a good fit to recognition memory data for auditory as well as visual stimuli. In addition, we present the first demonstration of the orthogonality of summed similarity and inter-item homogeneity effects. These data imply that auditory and visual representations undergo very similar transformations while they are encoded and retrieved from memory.

  16. Auditory short-term memory behaves like visual short-term memory.

    Visscher, Kristina M; Kaplan, Elina; Kahana, Michael J; Sekuler, Robert

    2007-03-01

    Are the information processing steps that support short-term sensory memory common to all the senses? Systematic, psychophysical comparison requires identical experimental paradigms and comparable stimuli, which can be challenging to obtain across modalities. Participants performed a recognition memory task with auditory and visual stimuli that were comparable in complexity and in their neural representations at early stages of cortical processing. The visual stimuli were static and moving Gaussian-windowed, oriented, sinusoidal gratings (Gabor patches); the auditory stimuli were broadband sounds whose frequency content varied sinusoidally over time (moving ripples). Parallel effects on recognition memory were seen for number of items to be remembered, retention interval, and serial position. Further, regardless of modality, predicting an item's recognizability requires taking account of (1) the probe's similarity to the remembered list items (summed similarity), and (2) the similarity between the items in memory (inter-item homogeneity). A model incorporating both these factors gives a good fit to recognition memory data for auditory as well as visual stimuli. In addition, we present the first demonstration of the orthogonality of summed similarity and inter-item homogeneity effects. These data imply that auditory and visual representations undergo very similar transformations while they are encoded and retrieved from memory.

  17. Short-term energy outlook: Quarterly projections, fourth quarter 1997

    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.

  18. Cardioprotective Signature of Short-Term Caloric Restriction.

    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.

  19. FFT transformed quantitative EEG analysis of short term memory load.

    Singh, Yogesh; Singh, Jayvardhan; Sharma, Ratna; Talwar, Anjana

    2015-07-01

    The EEG is considered as building block of functional signaling in the brain. The role of EEG oscillations in human information processing has been intensively investigated. To study the quantitative EEG correlates of short term memory load as assessed through Sternberg memory test. The study was conducted on 34 healthy male student volunteers. The intervention consisted of Sternberg memory test, which runs on a version of the Sternberg memory scanning paradigm software on a computer. Electroencephalography (EEG) was recorded from 19 scalp locations according to 10-20 international system of electrode placement. EEG signals were analyzed offline. To overcome the problems of fixed band system, individual alpha frequency (IAF) based frequency band selection method was adopted. The outcome measures were FFT transformed absolute powers in the six bands at 19 electrode positions. Sternberg memory test served as model of short term memory load. Correlation analysis of EEG during memory task was reflected as decreased absolute power in Upper alpha band in nearly all the electrode positions; increased power in Theta band at Fronto-Temporal region and Lower 1 alpha band at Fronto-Central region. Lower 2 alpha, Beta and Gamma band power remained unchanged. Short term memory load has distinct electroencephalographic correlates resembling the mentally stressed state. This is evident from decreased power in Upper alpha band (corresponding to Alpha band of traditional EEG system) which is representative band of relaxed mental state. Fronto-temporal Theta power changes may reflect the encoding and execution of memory task.

  20. Economics of solar energy: Short term costing

    Klee, H.

    The solar economics based on life cycle costs are refuted as both imaginary and irrelevant. It is argued that predicting rates of inflation and fuel escalation, expected life, maintenance costs, and legislation over the next ten to twenty years is pure guesswork. Furthermore, given the high mobility level of the U.S. population, the average consumer is skeptical of long run arguments which will pay returns only to the next owners. In the short term cost analysis, the house is sold prior to the end of the expected life of the system. The cash flow of the seller and buyer are considered. All the relevant factors, including the federal tax credit and the added value of the house because of the solar system are included.

  1. Short-term plasticity in auditory cognition.

    Jääskeläinen, Iiro P; Ahveninen, Jyrki; Belliveau, John W; Raij, Tommi; Sams, Mikko

    2007-12-01

    Converging lines of evidence suggest that auditory system short-term plasticity can enable several perceptual and cognitive functions that have been previously considered as relatively distinct phenomena. Here we review recent findings suggesting that auditory stimulation, auditory selective attention and cross-modal effects of visual stimulation each cause transient excitatory and (surround) inhibitory modulations in the auditory cortex. These modulations might adaptively tune hierarchically organized sound feature maps of the auditory cortex (e.g. tonotopy), thus filtering relevant sounds during rapidly changing environmental and task demands. This could support auditory sensory memory, pre-attentive detection of sound novelty, enhanced perception during selective attention, influence of visual processing on auditory perception and longer-term plastic changes associated with perceptual learning.

  2. Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach

    Kucukali, Serhat; Baris, Kemal

    2010-01-01

    This paper aims to forecast Turkey's short-term gross annual electricity demand by applying fuzzy logic methodology while general information on economical, political and electricity market conditions of the country is also given. Unlike most of the other forecast models about Turkey's electricity demand, which usually uses more than one parameter, gross domestic product (GDP) based on purchasing power parity was the only parameter used in the model. Proposed model made good predictions and captured the system dynamic behavior covering the years of 1970-2014. The model yielded average absolute relative errors of 3.9%. Furthermore, the model estimates a 4.5% decrease in electricity demand of Turkey in 2009 and the electricity demand growth rates are projected to be about 4% between 2010 and 2014. It is concluded that forecasting the Turkey's short-term gross electricity demand with the country's economic performance will provide more reliable projections. Forecasting the annual electricity consumption of a country could be made by any designer with the help of the fuzzy logic procedure described in this paper. The advantage of this model lies on the ability to mimic the human thinking and reasoning.

  3. Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach

    Kucukali, Serhat [Civil Engineering Department, Zonguldak Karaelmas University, Incivez 67100, Zonguldak (Turkey); Baris, Kemal [Mining Engineering Department, Zonguldak Karaelmas University, Incivez 67100, Zonguldak (Turkey)

    2010-05-15

    This paper aims to forecast Turkey's short-term gross annual electricity demand by applying fuzzy logic methodology while general information on economical, political and electricity market conditions of the country is also given. Unlike most of the other forecast models about Turkey's electricity demand, which usually uses more than one parameter, gross domestic product (GDP) based on purchasing power parity was the only parameter used in the model. Proposed model made good predictions and captured the system dynamic behavior covering the years of 1970-2014. The model yielded average absolute relative errors of 3.9%. Furthermore, the model estimates a 4.5% decrease in electricity demand of Turkey in 2009 and the electricity demand growth rates are projected to be about 4% between 2010 and 2014. It is concluded that forecasting the Turkey's short-term gross electricity demand with the country's economic performance will provide more reliable projections. Forecasting the annual electricity consumption of a country could be made by any designer with the help of the fuzzy logic procedure described in this paper. The advantage of this model lies on the ability to mimic the human thinking and reasoning. (author)

  4. Attentional priorities and access to short-term memory

    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...

  5. Qualitative similarities in the visual short-term memory of pigeons and people.

    Gibson, Brett; Wasserman, Edward; Luck, Steven J

    2011-10-01

    Visual short-term memory plays a key role in guiding behavior, and individual differences in visual short-term memory capacity are strongly predictive of higher cognitive abilities. To provide a broader evolutionary context for understanding this memory system, we directly compared the behavior of pigeons and humans on a change detection task. Although pigeons had a lower storage capacity and a higher lapse rate than humans, both species stored multiple items in short-term memory and conformed to the same basic performance model. Thus, despite their very different evolutionary histories and neural architectures, pigeons and humans have functionally similar visual short-term memory systems, suggesting that the functional properties of visual short-term memory are subject to similar selective pressures across these distant species.

  6. Evaluation of Short Term Memory Span Function In Children

    Barış ERGÜL; Arzu ALTIN YAVUZ; Ebru GÜNDOĞAN AŞIK

    2016-01-01

    Although details of the information encoded in the short-term memory where it is stored temporarily be recorded in the working memory in the next stage. Repeating the information mentally makes it remain in memory for a long time. Studies investigating the relationship between short-term memory and reading skills that are carried out to examine the relationship between short-term memory processes and reading comprehension. In this study information coming to short-term memory and the factors ...

  7. In Search of Decay in Verbal Short-Term Memory

    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…

  8. Insensitivity of visual short-term memory to irrelevant visual information

    Andrade, Jackie; Kemps, Eva; Werniers, Yves; May, Jon; Szmalec, Arnaud

    2002-01-01

    Several authors have hypothesised that visuo-spatial working memory is functionally analogous to verbal working memory. Irrelevant background speech impairs verbal short-term memory. We investigated whether irrelevant visual information has an analogous effect on visual short-term memory, using a dynamic visual noise (DVN) technique known to disrupt visual imagery (Quinn & McConnell, 1996a). Experiment 1 replicated the effect of DVN on pegword imagery. Experiments 2 and 3 showed no effect of ...

  9. A processing architecture for associative short-term memory in electronic noses

    Pioggia, G.; Ferro, M.; Di Francesco, F.; DeRossi, D.

    2006-11-01

    Electronic nose (e-nose) architectures usually consist of several modules that process various tasks such as control, data acquisition, data filtering, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, and fuzzy rules used to implement such tasks may lead to issues concerning module interconnection and cooperation. Moreover, a new learning phase is mandatory once new measurements have been added to the dataset, thus causing changes in the previously derived model. Consequently, if a loss in the previous learning occurs (catastrophic interference), real-time applications of e-noses are limited. To overcome these problems this paper presents an architecture for dynamic and efficient management of multi-transducer data processing techniques and for saving an associative short-term memory of the previously learned model. The architecture implements an artificial model of a hippocampus-based working memory, enabling the system to be ready for real-time applications. Starting from the base models available in the architecture core, dedicated models for neurons, maps and connections were tailored to an artificial olfactory system devoted to analysing olive oil. In order to verify the ability of the processing architecture in associative and short-term memory, a paired-associate learning test was applied. The avoidance of catastrophic interference was observed.

  10. Short-Term Saved Leave Scheme

    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...

  11. Short-Term Saved Leave Scheme

    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 ...

  12. Continuity of Landsat observations: Short term considerations

    Wulder, Michael A.; White, Joanne C.; Masek, Jeffery G.; Dwyer, John L.; Roy, David 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.

  13. Fuzzy approach for short term load forecasting

    Chenthur Pandian, S.; Duraiswamy, K.; Kanagaraj, N. [Electrical and Electronics Engg., K.S. Rangasamy College of Technology, Tiruchengode 637209, Tamil Nadu (India); Christober Asir Rajan, C. [Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Pondicherry (India)

    2006-04-15

    The main objective of short term load forecasting (STLF) is to provide load predictions for generation scheduling, economic load dispatch and security assessment at any time. The STLF is needed to supply necessary information for the system management of day-to-day operations and unit commitment. In this paper, the 'time' and 'temperature' of the day are taken as inputs for the fuzzy logic controller and the 'forecasted load' is the output. The input variable 'time' has been divided into eight triangular membership functions. The membership functions are Mid Night, Dawn, Morning, Fore Noon, After Noon, Evening, Dusk and Night. Another input variable 'temperature' has been divided into four triangular membership functions. They are Below Normal, Normal, Above Normal and High. The 'forecasted load' as output has been divided into eight triangular membership functions. They are Very Low, Low, Sub Normal, Moderate Normal, Normal, Above Normal, High and Very High. Case studies have been carried out for the Neyveli Thermal Power Station Unit-II (NTPS-II) in India. The fuzzy forecasted load values are compared with the conventional forecasted values. The forecasted load closely matches the actual one within +/-3%. (author)

  14. Short-term memory, executive control, and children's route learning

    Purser, H. R.; Farran, E. K.; Courbois, Y.; Lemahieu, A.; Mellier, D.; Sockeel, P.; Blades, M.

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11years 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 visuospatial long-term memory; the route-learning task was conducted using a maze in a virtual environment. In contrast to previous research, correlation...

  15. MHz gravitational waves from short-term anisotropic inflation

    Ito, Asuka; Soda, Jiro

    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 10 MHz∼100 MHz. If we could detect such gravitational waves in future, we would be able to probe higher dimensional fundamental theory.

  16. A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting

    Shifen Cheng

    2018-06-01

    Full Text Available Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and

  17. Why do short term workers have high mortality?

    Kolstad, Henrik; Olsen, Jørn

    1999-01-01

    or violence, the rate ratios for short term employment were 2.30 (95% Cl 1.74-3.06) and 1.86 (95% Cl 1.35-2.56), respectively. An unhealthy lifestyle may also be a determinant of short term employment. While it is possible in principle to adjust for lifestyle factors if proper data are collected, the health......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...

  18. Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

    Yimei Wang

    2018-04-01

    Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.

  19. Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

    Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

    2018-05-01

    We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

  20. In vitro short-term exposure to air pollution PM{sub 2.5-0.3} induced cell cycle alterations and genetic instability in a human lung cell coculture model

    Abbas, Imane [Université de Lille, Lille (France); EA4492-UCEIV, Université du Littoral-Côte d’Opale, Dunkerque (France); Lebanese Atomic Energy Commission – CNRS, Beirut (Lebanon); Verdin, Anthony [Université de Lille, Lille (France); EA4492-UCEIV, Université du Littoral-Côte d’Opale, Dunkerque (France); Escande, Fabienne [Centre de Biologie Pathologie, Centre Hospitalier Régional et Universitaire, Lille (France); Saint-Georges, Françoise [Université de Lille, Lille (France); Groupement Hospitalier de l’Institut Catholique de Lille, Lille (France); Cazier, Fabrice [Université de Lille, Lille (France); Centre Commun de Mesures, Université du Littoral-Côte d’Opale, Dunkerque (France); Mulliez, Philippe [Université de Lille, Lille (France); Groupement Hospitalier de l’Institut Catholique de Lille, Lille (France); Courcot, Dominique; Shirali, Pirouz [Université de Lille, Lille (France); EA4492-UCEIV, Université du Littoral-Côte d’Opale, Dunkerque (France); Gosset, Pierre [Université de Lille, Lille (France); Groupement Hospitalier de l’Institut Catholique de Lille, Lille (France); and others

    2016-05-15

    Although its adverse health effects of air pollution particulate matter (PM2.5) are well-documented and often related to oxidative stress and pro-inflammatory response, recent evidence support the role of the remodeling of the airway epithelium involving the regulation of cell death processes. Hence, the overarching goals of the present study were to use an in vitro coculture model, based on human AM and L132 cells to study the possible alteration of TP53-RB gene signaling pathways (i.e. cell cycle phases, gene expression of TP53, BCL2, BAX, P21, CCND1, and RB, and protein concentrations of their active forms), and genetic instability (i.e. LOH and/or MSI) in the PM{sub 2.5-0.3}-exposed coculture model. PM{sub 2.5-0.3} exposure of human AM from the coculture model induced marked cell cycle alterations after 24 h, as shown by increased numbers of L132 cells in subG1 and S+G2 cell cycle phases, indicating apoptosis and proliferation. Accordingly, activation of the TP53-RB gene signaling pathways after the coculture model exposure to PM{sub 2.5-0.3} was reported in the L132 cells. Exposure of human AM from the coculture model to PM{sub 2.5-0.3} resulted in MS alterations in 3p chromosome multiple critical regions in L132 cell population. Hence, in vitro short-term exposure of the coculture model to PM{sub 2.5-0.3} induced cell cycle alterations relying on the sequential occurrence of molecular abnormalities from TP53-RB gene signaling pathway activation and genetic instability. - Highlights: • Better knowledge on health adverse effects of air pollution PM{sub 2.5}. • Human alveolar macrophage and normal human epithelial lung cell coculture. • Molecular abnormalities from TP53-RB gene signaling pathway. • Loss of heterozygosity and microsatellite instability. • Pathologic changes in morphology and number of cells in relation to airway remodeling.

  1. The application of knemometry to measure childhood short-term growth among the indigenous Shuar of Ecuador.

    Urlacher, Samuel S; Snodgrass, J Josh; Liebert, Melissa A; Cepon-Robins, Tara J; Gildner, Theresa E; Sugiyama, Lawrence S

    2016-06-01

    Knemometry, the precise measurement of lower leg (LL) length, suggests that childhood short-term (e.g., weekly) growth is a dynamic, nonlinear process. However, owing to the large size and complexity of the traditional knemometer device, previous study of short-term growth among children has been restricted predominantly to clinical settings in industrialized Western nations. The aim of the present study is to address this limitation and promote broader understandings of global variation in childhood development by: (1) describing a custom-built portable knemometer and assessing its performance in the field; and (2) demonstrating the potential application of such a device by characterizing childhood short-term LL growth among the indigenous Shuar of Amazonian Ecuador. Mixed-longitudinal LL length data were collected weekly from 336 Shuar children age 5-12 years old using the custom portable knemometer (n = 1,145 total observations). Device performance and Shuar short-term LL growth were explored using linear mixed effects models and descriptive statistics. The portable knemometer performed well across a range of participant characteristics and possesses a low technical error of measurement of 0.18 mm. Shuar childhood LL growth averages 0.47 mm/week (SD = 0.75 mm/week), but exhibits large between- and within-individual variation. Knemometry can be reliably performed in the field, providing a means for evaluating childhood short-term growth among genetically and ecologically diverse populations. Preliminary findings suggest that Shuar weekly LL growth is comparable in mean magnitude but likely more variable than reported for healthy Western children. Future work will further explore these patterns. Am J Phys Anthropol 160:353-357, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  2. Short-term effect of two education methods on oral health among hearing impairment children

    Shiva Pouradeli

    2016-12-01

    CONCLUSION: Both video and dental model effectively improve the oral health of children with HI in short term. Continuous school-based oral health education programs, particularly for HI children, need to be considered.

  3. Robust Short-Term Memory without Synaptic Learning

    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

  4. Robust short-term memory without synaptic learning.

    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.

  5. Robust short-term memory without synaptic learning.

    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.

  6. A method for short term electricity spot price forecasting

    Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V.; Laitinen, E.; Haekli, J.; Antila, E.

    1998-01-01

    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

  7. A method for short term electricity spot price forecasting

    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

  8. Short-term predictability of crude oil markets: A detrended fluctuation analysis approach

    Alvarez-Ramirez, Jose; Alvarez, Jesus; Rodriguez, Eduardo

    2008-01-01

    This paper analyzes the auto-correlations of international crude oil prices on the basis of the estimation of the Hurst exponent dynamics for returns over the period from 1987 to 2007. In doing so, a model-free statistical approach - detrended fluctuation analysis - that reduces the effects of non-stationary market trends and focuses on the intrinsic auto-correlation structure of market fluctuations over different time horizons, is used. Tests for time variations of the Hurst exponent indicate that over long horizons the crude oil market is consistent with the efficient market hypothesis. However, meaningful auto-correlations cannot be excluded for time horizons smaller than one month where the Hurst exponent manifests cyclic, non-periodic dynamics. This means that the market exhibits a time-varying short-term inefficient behavior that becomes efficient in the long term. The proposed methodology and its findings are put in perspective with previous studies and results. (author)

  9. Hydroxychloroquine retinopathy after short-term therapy.

    Phillips, Brandon N; Chun, Dal W

    2014-01-01

    To report an unusual case of hydroxychloroquine toxicity after short-term therapy. Observational case report. A 56-year-old woman presented to the Ophthalmology Clinic at Walter Reed Army Medical Center (WRAMC) with a 6-month history of gradually decreasing vision in both eyes. The patient had been taking hydroxychloroquine for the preceding 48 months for the treatment of rheumatoid arthritis. Examination of the posterior segment revealed bilateral "bull's eye" macular lesions. Fundus autofluorescence revealed hyperfluorescence of well-defined bull's eye lesions in both eyes. Optical coherence tomography revealed corresponding parafoveal atrophy with a loss of the retinal inner segment/outer segment junction. Humphrey visual field 10-2 white showed significant central and paracentral defects with a generalized depression. The patient was on a standard dose of 400 mg daily, which was above her ideal dose. The patient had no history of kidney or liver dysfunction. There were no known risk factors but there were several possible confounding factors. The patient was started on high-dose nabumetone, a nonsteroidal antiinflammatory drug, at the same time she was started on hydroxychloroquine. She also reported taking occasional ibuprofen. Retinal toxicity from chloroquine has been recognized for decades with later reports showing retinopathy from long-term hydroxychloroquine (Plaquenil) use for the treatment of antiinflammatory diseases. Hydroxychloroquine is now widely used and retinal toxicity is relatively uncommon. However, it can cause serious vision loss and is usually irreversible. The risk of hydroxychloroquine toxicity rises to nearly 1% with a total cumulative dose of 1,000 g, which is ∼5 years to 7 years of normal use. Toxicity is rare under this dose. For this reason, the American Academy of Ophthalmology has revised its recommendations such that annual screenings begin 5 years after therapy with hydroxychloroquine has begun unless there are known risk

  10. Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data.

    Bateman, Brooke L; Pidgeon, Anna M; Radeloff, Volker C; Flather, Curtis H; VanDerWal, Jeremy; Akçakaya, H Resit; Thogmartin, Wayne E; Albright, Thomas P; Vavrus, Stephen J; Heglund, Patricia J

    2016-12-01

    Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous USA and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated area-under-the-curve AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate

  11. Short term exposure to cooking fumes and pulmonary function

    Qvenild Torgunn

    2009-05-01

    Full Text Available Abstract Background Exposure to cooking fumes may have different deleterious effects on the respiratory system. The aim of this study was to look at possible effects from inhalation of cooking fumes on pulmonary function. Methods Two groups of 12 healthy volunteers (A and B stayed in a model kitchen for two and four hours respectively, and were monitored with spirometry four times during twenty four hours, on one occasion without any exposure, and on another with exposure to controlled levels of cooking fumes. Results The change in spirometric values during the day with exposure to cooking fumes, were not statistically significantly different from the changes during the day without exposure, with the exception of forced expiratory time (FET. The change in FET from entering the kitchen until six hours later, was significantly prolonged between the exposed and the unexposed day with a 15.7% increase on the exposed day, compared to a 3.2% decrease during the unexposed day (p-value = 0.03. The same tendency could be seen for FET measurements done immediately after the exposure and on the next morning, but this was not statistically significant. Conclusion In our experimental setting, there seems to be minor short term spirometric effects, mainly affecting FET, from short term exposure to cooking fumes.

  12. Short-term effects of playing computer games on attention.

    Tahiroglu, Aysegul Yolga; Celik, Gonca Gul; Avci, Ayse; Seydaoglu, Gulsah; Uzel, Mehtap; Altunbas, Handan

    2010-05-01

    The main aim of the present study is to investigate the short-term cognitive effects of computer games in children with different psychiatric disorders and normal controls. One hundred one children are recruited for the study (aged between 9 and 12 years). All participants played a motor-racing game on the computer for 1 hour. The TBAG form of the Stroop task was administered to all participants twice, before playing and immediately after playing the game. Participants with improved posttest scores, compared to their pretest scores, used the computer on average 0.67 +/- 1.1 hr/day, while the average administered was measured at 1.6 +/- 1.4 hr/day and 1.3 +/- 0.9 hr/day computer use for participants with worse or unaltered scores, respectively. According to the regression model, male gender, younger ages, duration of daily computer use, and ADHD inattention type were found to be independent risk factors for worsened posttest scores. Time spent playing computer games can exert a short-term effect on attention as measured by the Stroop test.

  13. The Role of Short-term Consolidation in Memory Persistence

    Timothy J. Ricker

    2015-01-01

    Short-term memory, often described as working memory, is one of the most fundamental information processing systems of the human brain. Short-term memory function is necessary for language, spatial navigation, problem solving, and many other daily activities. Given its importance to cognitive function, understanding the architecture of short-term memory is of crucial importance to understanding human behavior. Recent work from several laboratories investigating the entry of information into s...

  14. Short-term Power Load Forecasting Based on Balanced KNN

    Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei

    2018-03-01

    To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

  15. Insensitivity of visual short-term memory to irrelevant visual information.

    Andrade, Jackie; Kemps, Eva; Werniers, Yves; May, Jon; Szmalec, Arnaud

    2002-07-01

    Several authors have hypothesized that visuo-spatial working memory is functionally analogous to verbal working memory. Irrelevant background speech impairs verbal short-term memory. We investigated whether irrelevant visual information has an analogous effect on visual short-term memory, using a dynamic visual noise (DVN) technique known to disrupt visual imagery (Quinn & McConnell, 1996b). Experiment I replicated the effect of DVN on pegword imagery. Experiments 2 and 3 showed no effect of DVN on recall of static matrix patterns, despite a significant effect of a concurrent spatial tapping task. Experiment 4 showed no effect of DVN on encoding or maintenance of arrays of matrix patterns, despite testing memory by a recognition procedure to encourage visual rather than spatial processing. Serial position curves showed a one-item recency effect typical of visual short-term memory. Experiment 5 showed no effect of DVN on short-term recognition of Chinese characters, despite effects of visual similarity and a concurrent colour memory task that confirmed visual processing of the characters. We conclude that irrelevant visual noise does not impair visual short-term memory. Visual working memory may not be functionally analogous to verbal working memory, and different cognitive processes may underlie visual short-term memory and visual imagery.

  16. The Mind and Brain of Short-Term Memory

    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, w...

  17. Short-term plasticity as a neural mechanism supporting memory and attentional functions

    Jääskeläinen, Iiro P.; Ahveninen, Jyrki; Andermann, Mark L.; Belliveau, John W.; Raij, Tommi; Sams, Mikko

    2011-01-01

    Based on behavioral studies, several relatively distinct perceptual and cognitive functions have been defined in cognitive psychology such as sensory memory, short-term memory, and selective attention. Here, we review evidence suggesting that some of these functions may be supported by shared underlying neuronal mechanisms. Specifically, we present, based on an integrative review of the literature, a hypothetical model wherein short-term plasticity, in the form of transient center-excitatory ...

  18. Mid-latitude summer response of the middle atmosphere to short-term solar UV changes

    P. Keckhut

    1995-06-01

    Full Text Available Temperature and wind data obtained with Rayleigh lidar since 1979 and Russian rockets since 1964 are analyzed to deduce the summer response of the middle atmosphere to short-term solar UV changes. The equivalent width of the 1083 nm He I line is used as a proxy to monitor the short-term UV flux changes. Spectral analyses are performed on 108-day windows to extract the 27-day component from temperature, wind and solar data sets. Linear regressions between these spectral harmonics show some significant correlations around 45 km at mid-latitudes. For large 27-day solar cycles, amplitudes of 2 K and 6 m s-1 are calculated for temperature data series over the south of France (44°N, and on wind data series over Volgograd (49°N, respectively. Cross-spectrum analyses have indicated correlations between these atmospheric parameters and the solar proxy with a phase lag of less than 2 days. These statistically correlative results, which provide good qualitative agreement with numerical simulations, are both obtained at mid-latitude. However, the observed amplitudes are larger than expected, with numerical models suggesting that dynamical processes such as equatorial or gravity waves may be responsible.

  19. Mid-latitude summer response of the middle atmosphere to short-term solar UV changes

    P. Keckhut

    Full Text Available Temperature and wind data obtained with Rayleigh lidar since 1979 and Russian rockets since 1964 are analyzed to deduce the summer response of the middle atmosphere to short-term solar UV changes. The equivalent width of the 1083 nm He I line is used as a proxy to monitor the short-term UV flux changes. Spectral analyses are performed on 108-day windows to extract the 27-day component from temperature, wind and solar data sets. Linear regressions between these spectral harmonics show some significant correlations around 45 km at mid-latitudes. For large 27-day solar cycles, amplitudes of 2 K and 6 m s-1 are calculated for temperature data series over the south of France (44°N, and on wind data series over Volgograd (49°N, respectively. Cross-spectrum analyses have indicated correlations between these atmospheric parameters and the solar proxy with a phase lag of less than 2 days. These statistically correlative results, which provide good qualitative agreement with numerical simulations, are both obtained at mid-latitude. However, the observed amplitudes are larger than expected, with numerical models suggesting that dynamical processes such as equatorial or gravity waves may be responsible.

  20. 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

    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

  1. Short-term wind power forecasting: probabilistic and space-time aspects

    Tastu, Julija

    work deals with the proposal and evaluation of new mathematical models and forecasting methods for short-term wind power forecasting, accounting for space-time dynamics based on geographically distributed information. Different forms of power predictions are considered, starting from traditional point...... into the corresponding models are analysed. As a final step, emphasis is placed on generating space-time trajectories: this calls for the prediction of joint multivariate predictive densities describing wind power generation at a number of distributed locations and for a number of successive lead times. In addition......Optimal integration of wind energy into power systems calls for high quality wind power predictions. State-of-the-art forecasting systems typically provide forecasts for every location individually, without taking into account information coming from the neighbouring territories. It is however...

  2. Analysis of short-term reactor cavity transient

    Cheng, T.C.; Fischer, S.R.

    1981-01-01

    Following the transient of a hypothetical loss-of-coolant accident (LOCA) in a nuclear reactor, peak pressures are reached within the first 0.03 s at different locations inside the reactor cavity. Due to the complicated multidimensional nature of the reactor cavity, the short-term analysis of the LOCA transient cannot be performed by using traditional containment codes, such as CONTEMPT. The advanced containment code, BEACON/MOD3, developed at the Idaho National Engineering Laboratory (INEL), can be adapted for such analysis. This code provides Eulerian, one and two-dimensional, nonhomogeneous, nonequilibrium flow modeling as well as lumped parameter, homogeneous, equilibrium flow modeling for the solution of two-component, two-phase flow problems. The purpose of this paper is to demonstrate the capability of the BEACON code to analyze complex containment geometry such as a reactor cavity

  3. Short-Term Planning of Hybrid Power System

    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.

  4. Operations Management in Short Term Power Markets

    Heide-Jørgensen, Ditte Mølgård

    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 wit...

  5. Short-term variability of Cyg X-1

    Oda, M.; Doi, K.; Ogawara, Y.

    1976-01-01

    The short-term X-ray variability distinguishes Cyg X-1, which is the most likely candidate for a black hole, from other X-ray sources. The present status of our knowledge on this short-term variation, mainly from the UHURU, the MIT and the GSFC observations, is reviewed. The nature of impulsive variations which compose the time variation exceeding the statistical fluctuation is discussed. There are indications that the energy spectrum of large pulses is harder than the average spectrum, or that the large pulses are the characteristics of the hard component of the spectrum if it is composed of two, soft and hard, components. Features of the variations may be partly simulated by the superposition of random shot-noise pulses with a fraction of a second duration. However, the autocorrelation analysis and the dynamic spectrum analysis indicate that the correlation lasts for several seconds and in the variation are buried some regularities which exhibit power concentrations in several frequency bands; 0.2-0.3, 0.4-0.5, 0.8, 1.2-1.5 Hz. There are several possible interpretations of these results in terms of; e.g. (a) a mixture of shot-noise pulses with two or more constant durations, (b) the shape of the basic shot-noise pulse, (c) bunching of the pulses, (d) superposition of wave-packets or temporal oscillations. But we have not yet reached any definite understandings in the nature of the variabilities. The substructure of the fluctuations on a time scale of milliseconds suggested by two investigations is also discussed. (Auth.)

  6. Short-term variability of CYG X-1

    Oda, M.; Doi, K.; Ogawara, Y.; Takagishi, K.; Wada, M.

    1975-01-01

    The short-term X-ray variability distinguishes Cyg X-1, which is the most likely candidate of the black hole, from other X-ray sources. Present status of our knowledge on this short-term variation mainly from the Uhuru, the MIT and the GSFC observations is reviewed. The nature of impulsive variations which compose the time variation exceeding the statistical fluctuation is discussed. There are indications that the energy spectrum of large pulses is harder than the average spectrum or the large pulses are the characteristics of the hard component of the spectrum if it is composed of two, soft and hard, components. Features of the variations may be partly simulated by the superposition of random short-noise pulses with a fraction of a second duration. However, the autocorrelation analysis and the dynamic spectrum analysis indicate that the correlation lasts for several seconds and in the variation buried are some regularities which exhibit power concentrations in several frequency bands; 0.2 -- 0.3, 0.4 -- 0.5, 0.8, 1.2 -- 1.5 Hz. There are several possible interpretation of these results in terms of: e.g. a) a mixture of short-noise pulses with two or more constant durations, b) the shape of the basic shot-noise pulse, c) bunching of the pulses, d) superposition of wave-packets or temporal oscillations. But we have not yet reached any definite understandings in the nature of the variabilities. The sub-structure of the fluctuations on a time scale of milli-second suggested by two investigations is also discussed. (auth.)

  7. Neuroticism and conscientiousness respectively constrain and facilitate short-term plasticity within the working memory neural network.

    Dima, Danai; Friston, Karl J; Stephan, Klaas E; Frangou, Sophia

    2015-10-01

    Individual differences in cognitive efficiency, particularly in relation to working memory (WM), have been associated both with personality dimensions that reflect enduring regularities in brain configuration, and with short-term neural plasticity, that reflects task-related changes in brain connectivity. To elucidate the relationship of these two divergent mechanisms, we tested the hypothesis that personality dimensions, which reflect enduring aspects of brain configuration, inform about the neurobiological framework within which short-term, task-related plasticity, as measured by effective connectivity, can be facilitated or constrained. As WM consistently engages the dorsolateral prefrontal (DLPFC), parietal (PAR), and anterior cingulate cortex (ACC), we specified a WM network model with bidirectional, ipsilateral, and contralateral connections between these regions from a functional magnetic resonance imaging dataset obtained from 40 healthy adults while performing the 3-back WM task. Task-related effective connectivity changes within this network were estimated using Dynamic Causal Modelling. Personality was evaluated along the major dimensions of Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. Only two dimensions were relevant to task-dependent effective connectivity. Neuroticism and Conscientiousness respectively constrained and facilitated neuroplastic responses within the WM network. These results suggest individual differences in cognitive efficiency arise from the interplay between enduring and short-term plasticity in brain configuration. © 2015 Wiley Periodicals, Inc.

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

    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.

  9. Short-term plasticity as a neural mechanism supporting memory and attentional functions.

    Jääskeläinen, Iiro P; Ahveninen, Jyrki; Andermann, Mark L; Belliveau, John W; Raij, Tommi; Sams, Mikko

    2011-11-08

    Based on behavioral studies, several relatively distinct perceptual and cognitive functions have been defined in cognitive psychology such as sensory memory, short-term memory, and selective attention. Here, we review evidence suggesting that some of these functions may be supported by shared underlying neuronal mechanisms. Specifically, we present, based on an integrative review of the literature, a hypothetical model wherein short-term plasticity, in the form of transient center-excitatory and surround-inhibitory modulations, constitutes a generic processing principle that supports sensory memory, short-term memory, involuntary attention, selective attention, and perceptual learning. In our model, the size and complexity of receptive fields/level of abstraction of neural representations, as well as the length of temporal receptive windows, increases as one steps up the cortical hierarchy. Consequently, the type of input (bottom-up vs. top down) and the level of cortical hierarchy that the inputs target, determine whether short-term plasticity supports purely sensory vs. semantic short-term memory or attentional functions. Furthermore, we suggest that rather than discrete memory systems, there are continuums of memory representations from short-lived sensory ones to more abstract longer-duration representations, such as those tapped by behavioral studies of short-term memory. Copyright © 2011 Elsevier B.V. All rights reserved.

  10. Human short-term spatial memory: precision predicts capacity.

    Banta Lavenex, Pamela; Boujon, Valérie; Ndarugendamwo, Angélique; Lavenex, Pierre

    2015-03-01

    Here, we aimed to determine the capacity of human short-term memory for allocentric spatial information in a real-world setting. Young adults were tested on their ability to learn, on a trial-unique basis, and remember over a 1-min interval the location(s) of 1, 3, 5, or 7 illuminating pads, among 23 pads distributed in a 4m×4m arena surrounded by curtains on three sides. Participants had to walk to and touch the pads with their foot to illuminate the goal locations. In contrast to the predictions from classical slot models of working memory capacity limited to a fixed number of items, i.e., Miller's magical number 7 or Cowan's magical number 4, we found that the number of visited locations to find the goals was consistently about 1.6 times the number of goals, whereas the number of correct choices before erring and the number of errorless trials varied with memory load even when memory load was below the hypothetical memory capacity. In contrast to resource models of visual working memory, we found no evidence that memory resources were evenly distributed among unlimited numbers of items to be remembered. Instead, we found that memory for even one individual location was imprecise, and that memory performance for one location could be used to predict memory performance for multiple locations. Our findings are consistent with a theoretical model suggesting that the precision of the memory for individual locations might determine the capacity of human short-term memory for spatial information. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Short-term memories with a stochastic perturbation

    Pontes, Jose C.A. de; Batista, Antonio M.; Viana, Ricardo L.; Lopes, Sergio R.

    2005-01-01

    We investigate short-term memories in linear and weakly nonlinear coupled map lattices with a periodic external input. We use locally coupled maps to present numerical results about short-term memory formation adding a stochastic perturbation in the maps and in the external input

  12. Short-term energy outlook, annual supplement 1994

    1994-08-01

    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

  13. Pediatric polytrauma : Short-term and long-term outcomes

    vanderSluis, CK; Kingma, J; Eisma, WH; tenDuis, HJ

    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

  14. Comparison of Sugammadex and Neostigmine in Short Term Surgery

    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 Group Treatment for Adult Children of Alcoholics.

    Cooper, Alvin; McCormack, WIlliam A.

    1992-01-01

    Adult children of alcoholics (n=24) were tested on measures of loneliness, anxiety, hostility, depression, and interpersonal dependency before and after participation in short-term group therapy. Highly significant test score changes supported effectiveness of individual therapy in short-term groups. (Author/NB)

  16. Short-term energy outlook annual supplement, 1993

    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.

  17. Short-Term Robustness of Production Management Systems : New Methodology

    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

  18. Short-Term Reciprocity in Late Parent-Child Relationships

    Leopold, Thomas; Raab, Marcel

    2011-01-01

    Long-term concepts of parent-child reciprocity assume that the amount of support given and received is only balanced in a generalized fashion over the life course. We argue that reciprocity in parent-child relationships also operates in the short term. Our analysis of short-term reciprocity focuses on concurrent exchange in its main upward and…

  19. Short-Term Market Risks Implied by Weekly Options

    Andersen, Torben Gustav; Fusari, Nicola; Todorov, Viktor

    a direct way to study volatility and jump risks. Unlike longer-dated options, they are largely insensitive to the risk of intertemporal shifts in the economic environment. Adopting a novel semi-nonparametric approach, we uncover variation in the negative jump tail risk which is not spanned by market......We study short-term market risks implied by weekly S&P 500 index options. The introduction of weekly options has dramatically shifted the maturity profile of traded options over the last five years, with a substantial proportion now having expiry within one week. Such short-dated options provide......" by the level of market volatility and elude standard asset pricing models....

  20. The Delicate Analysis of Short-Term Load Forecasting

    Song, Changwei; Zheng, Yuan

    2017-05-01

    This paper proposes a new method for short-term load forecasting based on the similar day method, correlation coefficient and Fast Fourier Transform (FFT) to achieve the precision analysis of load variation from three aspects (typical day, correlation coefficient, spectral analysis) and three dimensions (time dimension, industry dimensions, the main factors influencing the load characteristic such as national policies, regional economic, holidays, electricity and so on). First, the branch algorithm one-class-SVM is adopted to selection the typical day. Second, correlation coefficient method is used to obtain the direction and strength of the linear relationship between two random variables, which can reflect the influence caused by the customer macro policy and the scale of production to the electricity price. Third, Fourier transform residual error correction model is proposed to reflect the nature of load extracting from the residual error. Finally, simulation result indicates the validity and engineering practicability of the proposed method.

  1. Attention restores discrete items to visual short-term memory.

    Murray, Alexandra M; Nobre, Anna C; Clark, Ian A; Cravo, André M; Stokes, Mark G

    2013-04-01

    When a memory is forgotten, is it lost forever? Our study shows that selective attention can restore forgotten items to visual short-term memory (VSTM). In our two experiments, all stimuli presented in a memory array were designed to be equally task relevant during encoding. During the retention interval, however, participants were sometimes given a cue predicting which of the memory items would be probed at the end of the delay. This shift in task relevance improved recall for that item. We found that this type of cuing improved recall for items that otherwise would have been irretrievable, providing critical evidence that attention can restore forgotten information to VSTM. Psychophysical modeling of memory performance has confirmed that restoration of information in VSTM increases the probability that the cued item is available for recall but does not improve the representational quality of the memory. We further suggest that attention can restore discrete items to VSTM.

  2. SHORT-TERM FORECASTING OF MORTGAGE LENDING

    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.

  3. Short-term predictions in forex trading

    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.

  4. Development of Dynamic Spent Nuclear Fuel Environmental Effect Analysis Model

    Jeong, Chang Joon; Ko, Won Il; Lee, Ho Hee; Cho, Dong Keun; Park, Chang Je

    2010-07-01

    The dynamic environmental effect evaluation model for spent nuclear fuel has been developed and incorporated into the system dynamic DANESS code. First, the spent nuclear fuel isotope decay model was modeled. Then, the environmental effects were modeled through short-term decay heat model, short-term radioactivity model, and long-term heat load model. By using the developed model, the Korean once-through nuclear fuel cycles was analyzed. The once-through fuel cycle analysis was modeled based on the Korean 'National Energy Basic Plan' up to 2030 and a postulated nuclear demand growth rate until 2150. From the once-through results, it is shown that the nuclear power demand would be ∼70 GWe and the total amount of the spent fuel accumulated by 2150 would be ∼168000 t. If the disposal starts from 2060, the short-term decay heat of Cs-137 and Sr-90 isotopes are W and 1.8x10 6 W in 2100. Also, the total long-term heat load in 2100 will be 4415 MW-y. From the calculation results, it was found that the developed model is very convenient and simple for evaluation of the environmental effect of the spent nuclear fuel

  5. Short-term economics of virtual power plants

    Kok, J.K.

    2009-08-01

    The Virtual Power Plant (VPP) has gained an increasing interest over the last few years. A VPP is a flexible representation of a portfolio of Distributed Energy Resources (DER: distributed generation, demand response and electricity storage). One of the key activities of a VPP is the delivery of (near-)real-time balancing services. In order to operate such a (near-)real-time coordination activity optimally, the VPP needs to maintain a dynamic merit-order list of all DER participating in the VPP. In order to make optimal decisions based on this list, the merit order needs to be based on the true marginal cost (or marginal benefit in case of demand response) of the individual DER units. The marginal electricity costs of most types of DER are highly dependent on local context and, hence, change over time. From analysis of the short-term bid strategies of various DER units, the existence of a bid strategy spectrum becomes clear. On one end of the spectrum, bidding strategies are based straightforwardly on true marginal cost or benefit. Further along the spectrum, optimal bidding strategies become less dependent on marginal cost levels and more on the price dynamics in the (VPP) market context. These results are relevant for VPP operations both from business and technical perspectives.

  6. Verbal Short-Term Memory Span in Speech-Disordered Children: Implications for Articulatory Coding in Short-Term Memory.

    Raine, Adrian; And Others

    1991-01-01

    Children with speech disorders had lower short-term memory capacity and smaller word length effect than control children. Children with speech disorders also had reduced speech-motor activity during rehearsal. Results suggest that speech rate may be a causal determinant of verbal short-term memory capacity. (BC)

  7. Short-term wind power prediction

    Joensen, Alfred K.

    2003-01-01

    , 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......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...

  8. Short term solar radiation forecasting: Island versus continental sites

    Boland, John; David, Mathieu; Lauret, Philippe

    2016-01-01

    Due its intermittency, the large-scale integration of solar energy into electricity grids is an issue and more specifically in an insular context. Thus, forecasting the output of solar energy is a key feature to efficiently manage the supply-demand balance. In this paper, three short term forecasting procedures are applied to island locations in order to see how they perform in situations that are potentially more volatile than continental locations. Two continental locations, one coastal and one inland are chosen for comparison. At the two time scales studied, ten minute and hourly, the island locations prove to be more difficult to forecast, as shown by larger forecast errors. It is found that the three methods, one purely statistical combining Fourier series plus linear ARMA models, one combining clear sky index models plus neural net models, and a third using a clear sky index plus ARMA, give similar forecasting results. It is also suggested that there is great potential of merging modelling approaches on different horizons. - Highlights: • Solar energy forecasting is more difficult for insular than continental sites. • Fourier series plus linear ARMA models are one forecasting method tested. • Clear sky index models plus neural net models are also tested. • Clear sky index models plus linear ARMA is also an option. • All three approaches have similar skill.

  9. Short term forecasting of petroleum product demand in France

    Cadren, M.

    1998-01-01

    The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It's filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter's. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach's and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author)

  10. Online short-term solar power forecasting

    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......-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....... 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...

  11. Attention Problems, Phonological Short-Term Memory, and Visuospatial Short-Term Memory: Differential Effects on Near- and Long-Term Scholastic Achievement

    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…

  12. Audit of long-term and short-term liabilities

    Korinko M.D.

    2017-03-01

    Full Text Available The article determines the importance of long-term and short-term liabilities for the management of financial and material resources of an enterprise. It reviews the aim, objects and information generators for realization of audit of short-term and long-term obligations. The organizing and methodical providing of audit of long-term and short-term liabilities of an enterprise are generalized. The authors distinguish the stages of realization of audit of long-term and short-term liabilities, the aim of audit on each of the presented stages, and recommend methodical techniques. It is fixed that it is necessary to conduct the estimation of the systems of internal control and record-keeping of an enterprise by implementation of public accountant procedures for determination of volume and maintenance of selection realization. After estimating the indicated systems, a public accountant determines the methodology for realization of public accountant verification of long-term and short-term liabilities. The analytical procedures that public accountants are expedient to use for realization of audit of short-term and long-term obligations are determined. The authors suggest the classification of the educed defects on the results of the conducted public accountant verification of short-term and long-term obligations.

  13. Short-term memory in the service of executive control functions

    Farshad Alizadeh Mansouri

    2015-12-01

    Full Text Available Short-term memory is a crucial cognitive function for supporting on-going and upcoming behaviours, allowing storage of information across delay periods. The content of this memory may typically include tangible information about features such as the shape, colour or texture of an object, its location and motion relative to the body, or phonological information. The neural correlate of these short-term memories has been found in different brain areas involved in organizing perceptual or motor functions. In particular, neuronal activity in different prefrontal areas encodes task-related information corresponding to short-term memory across delay periods, and lesions in the prefrontal cortex severely affect the ability to hold this type of memory. Recent studies have further expanded the scope and possible role of short-term memory by showing that information of abstract entities such as a behaviour-guiding rule, or the occurrence of a conflict in information processing; can also be maintained in short-term memory and used for adjusting the allocation of executive control in dynamic environments. It has also been shown that neuronal activity in the dorsolateral prefrontal and orbitofrontal cortices encodes information about such abstract entities. These findings suggest that the prefrontal cortex plays crucial roles in organizing goal-directed behaviour by supporting various mnemonic processes that maintain a wide range of information in the service of executive control of on-going or upcoming behaviour.

  14. Slave systems in verbal short-term memory.

    Caplan, David; Waters, Gloria; Howard, David

    2012-01-01

    The model of performance in short-term memory (STM) tasks that has been most influential in cognitive neuropsychological work on deficits of STM is the "working memory" model mainly associated with the work of Alan Baddeley and his colleagues. This paper reviews the model. We examine the development of this theory in studies that account for STM performances in normal (non-brain-damaged) individuals, and then review the application of this theory to neuropsychological cases and specifications, modifications, and extensions of the theory that have been suggested on the basis of these cases. Our approach is to identify the major phenomena that have been discussed and to examine selected papers dealing with those phenomena in some detail. The main contribution is a review of the WM model that includes both normative and neuropsychological data. We conclude that the WM model has many inconsistencies and empirical inadequacies, and that cognitive neuropsychologists might benefit from considering other models when they attempt to describe and explain patients' performances on STM tasks.

  15. Slave systems in verbal short-term memory

    Caplan, David; Waters, Gloria; Howard, David

    2013-01-01

    Background The model of performance in short-term memory (STM) tasks that has been most influential in cognitive neuropsychological work on deficits of STM is the “working memory” model mainly associated with the work of Alan Baddeley and his colleagues. Aim This paper reviews the model. We examine the development of this theory in studies that account for STM performances in normal (non-brain-damaged) individuals, and then review the application of this theory to neuropsychological cases and specifications, modifications, and extensions of the theory that have been suggested on the basis of these cases. Our approach is to identify the major phenomena that have been discussed and to examine selected papers dealing with those phenomena in some detail. Main Contribution The main contribution is a review of the WM model that includes both normative and neuropsychological data. Conclusions We conclude that the WM model has many inconsistencies and empirical inadequacies, and that cognitive neuropsychologists might benefit from considering other models when they attempt to describe and explain patients’ performances on STM tasks. PMID:24347786

  16. Short-term regulation of hydro powerplants. Studies on the environmental effects

    Sinisalmi, T.; Riihimaeki, J.; Vehanen, T.; Yrjaenae, T.

    1997-01-01

    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

  17. Short-term regulation of hydro powerplants. Studies on the environmental effects

    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.

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

    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…

  19. Short-term airing by natural ventilation

    Perino, Marco; Heiselberg, Per

    2009-01-01

    The need to improve the energy efficiency of buildings requires new and more efficient ventilation systems. It has been demonstrated that innovative operating concepts that make use of natural ventilation seem to be more appreciated by occupants. This kind of system frequently integrates traditio......The need to improve the energy efficiency of buildings requires new and more efficient ventilation systems. It has been demonstrated that innovative operating concepts that make use of natural ventilation seem to be more appreciated by occupants. This kind of system frequently integrates...... 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....

  20. Reconciling long-term cultural diversity and short-term collective social behavior.

    Valori, Luca; Picciolo, Francesco; Allansdottir, Agnes; Garlaschelli, Diego

    2012-01-24

    An outstanding open problem is whether collective social phenomena occurring over short timescales can systematically reduce cultural heterogeneity in the long run, and whether offline and online human interactions contribute differently to the process. Theoretical models suggest that short-term collective behavior and long-term cultural diversity are mutually excluding, since they require very different levels of social influence. The latter jointly depends on two factors: the topology of the underlying social network and the overlap between individuals in multidimensional cultural space. However, while the empirical properties of social networks are intensively studied, little is known about the large-scale organization of real societies in cultural space, so that random input specifications are necessarily used in models. Here we use a large dataset to perform a high-dimensional analysis of the scientific beliefs of thousands of Europeans. We find that interopinion correlations determine a nontrivial ultrametric hierarchy of individuals in cultural space. When empirical data are used as inputs in models, ultrametricity has strong and counterintuitive effects. On short timescales, it facilitates a symmetry-breaking phase transition triggering coordinated social behavior. On long timescales, it suppresses cultural convergence by restricting it within disjoint groups. Moreover, ultrametricity implies that these results are surprisingly robust to modifications of the dynamical rules considered. Thus the empirical distribution of individuals in cultural space appears to systematically optimize the coexistence of short-term collective behavior and long-term cultural diversity, which can be realized simultaneously for the same moderate level of mutual influence in a diverse range of online and offline settings.

  1. A New Strategy for Short-Term Load Forecasting

    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. Short-term ensemble radar rainfall forecasts for hydrological applications

    Codo de Oliveira, M.; Rico-Ramirez, M. A.

    2016-12-01

    Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

  3. Statistical short-term earthquake prediction.

    Kagan, Y Y; Knopoff, L

    1987-06-19

    A statistical procedure, derived from a theoretical model of fracture growth, is used to identify a foreshock sequence while it is in progress. As a predictor, the procedure reduces the average uncertainty in the rate of occurrence for a future strong earthquake by a factor of more than 1000 when compared with the Poisson rate of occurrence. About one-third of all main shocks with local magnitude greater than or equal to 4.0 in central California can be predicted in this way, starting from a 7-year database that has a lower magnitude cut off of 1.5. The time scale of such predictions is of the order of a few hours to a few days for foreshocks in the magnitude range from 2.0 to 5.0.

  4. Short-term effects of simultaneous cardiovascular workout and ...

    PMD), has become a growing public health concern, as it may potentially result in the development of hearing difficulties. Objectives: The aim of the study was to determine the differential impact and short-term effects of simultaneous ...

  5. Short-term treatment outcomes of children starting antiretroviral ...

    Short-term treatment outcomes of children starting antiretroviral therapy in the intensive care unit, general medical wards and outpatient HIV clinics at Red Cross War Memorial Children's Hospital, Cape Town, South Africa: A retrospective cohort study.

  6. Short-Term Memory in Habituation and Dishabituation

    Whitlow, Jesse William, Jr.

    1975-01-01

    The present research evaluated the refractorylike response decrement, as found in habituation of auditory evoked peripheral vasoconstriction in rabbits, to determine whether or not it represents a short-term habituation process distinct from effector fatigue or sensory adaptation. (Editor)

  7. Short-term outcome of patients with closed comminuted femoral ...

    Short-term outcome of patients with closed comminuted femoral shaft fracture treated with locking intramedullary sign nail at Muhimbili Orthopaedic Institute in Tanzania. Billy T. Haonga, Felix S. Mrita, Edmundo E. Ndalama, Jackline E. Makupa ...

  8. Short term variations in particulate matter in Mahi river estuary

    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...

  9. The nature of forgetting from short-term memory

    Muter, Paul

    2001-01-01

    Memory and forgetting are inextricably intertwined. Any account of short-term memory (STM) should address the following question: If three, four, or five chunks are being held in STM, what happens after attention is diverted?

  10. An ethics curriculum for short-term global health trainees

    DeCamp, Matthew; Rodriguez, Joce; Hecht, Shelby; Barry, Michele; Sugarman, Jeremy

    2013-01-01

    Background Interest in short-term global health training and service programs continues to grow, yet they can be associated with a variety of ethical issues for which trainees or others with limited global health experience may not be prepared to address. Therefore, there is a clear need for educational interventions concerning these ethical issues. Methods We developed and evaluated an introductory curriculum, ?Ethical Challenges in Short-term Global Health Training.? The curriculum was deve...

  11. Short-term incentive schemes for hospital managers

    Lucas Malambe

    2013-10-01

    Full Text Available Orientation: Short-term incentives, considered to be an extrinsic motivation, are commonly used to motivate performance. This study explored hospital managers’ perceptions of short term incentives in maximising performance and retention. Research purpose: The study explored the experiences, views and perceptions of private hospital managers in South Africa regarding the use of short-term incentives to maximise performance and retention, as well as the applicability of the findings to public hospitals. Motivation for the study: Whilst there is an established link between performance reward schemes and organisational performance, there is little understanding of the effects of short term incentives on the performance and retention of hospital managers within the South African context. Research design, approach, and method: The study used a qualitative research design: interviews were conducted with a purposive sample of 19 hospital managers, and a thematic content analysis was performed. Main findings: Short-term incentives may not be the primary motivator for hospital managers, but they do play a critical role in sustaining motivation. Participants indicated that these schemes could also be applicable to public hospitals. Practical/managerial implications: Hospital managers are inclined to be more motivated by intrinsic than extrinsic factors. However, hospital managers (as middle managers also seem to be motivated by short-term incentives. A combination of intrinsic and extrinsic motivators should thus be used to maximise performance and retention. Contribution/value-add: Whilst the study sought to explore hospital managers’ perceptions of short-term incentives, it also found that an adequate balance between internal and external motivators is key to implementing an effective short-term incentive scheme.

  12. Frequency-specific insight into short-term memory capacity

    Feurra, Matteo; Galli, Giulia; Pavone, Enea Francesco; Rossi, Alessandro; Rossi, Simone

    2016-01-01

    We provided novel evidence of a frequency-specific effect by transcranial alternating current stimulation (tACS) of the left posterior parietal cortex on short-term memory, during a digit span task. the effect was prominent with stimulation at beta frequency for young and not for middle-aged adults and correlated with age. Our findings highlighted a short-term memory capacity improvement by tACS application.

  13. Short-term memory for scenes with affective content

    Maljkovic, Vera; Martini, Paolo

    2005-01-01

    The emotional content of visual images can be parameterized along two dimensions: valence (pleasantness) and arousal (intensity of emotion). In this study we ask how these distinct emotional dimensions affect the short-term memory of human observers viewing a rapid stream of images and trying to remember their content. We show that valence and arousal modulate short-term memory as independent factors. Arousal influences dramatically the average speed of data accumulation in memory: Higher aro...

  14. Narcissism and the Strategic Pursuit of Short-Term Mating

    Schmitt, David P.; Alcalay, Lidia; Allik, Jüri

    2017-01-01

    Previous studies have documented links between sub-clinical narcissism and the active pursuit of short-term mating strategies (e.g., unrestricted sociosexuality, marital infidelity, mate poaching). Nearly all of these investigations have relied solely on samples from Western cultures. In the curr...... limitations of these cross-culturally universal findings and presents suggestions for future research into revealing the precise psychological features of narcissism that facilitate the strategic pursuit of short-term mating....

  15. Short-term memory and dual task performance

    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.

  16. Short-term power plant operation scheduling in thermal systems with long-term boundary conditions

    Wolter, H.

    1990-01-01

    For the first time, the modeling of long-term quantitative conditions within the short-term planning of the application of power stations is made via their shadow prices. It corresponds to a decomposition of the quantitative conditions by means of the method of the Langrange relaxation. The shadow prices determined by the planning for energy application regarding long- term quantitative conditions pass into the short-term planning for power station application and subsidize or rather punish the application of limited amounts as for as they are not claimed for sufficiently or excessively. The clear advantage of this modeling is that the short-term planning of power station application can deviate from the envisioned energy application regarding the total optimum, because the shadow prices contain all information about the cost effect of the energy shifts in the residual total period, which become necessary due to the deviations in the short-term period to be planned in the current short-term period. (orig./DG) [de

  17. Short-term energy outlook, quarterly projections, first quarter 1998

    NONE

    1998-01-01

    The forecast period for this issue of the Outlook extends from the first quarter of 1998 through the fourth quarter of 1999. 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 first quarter 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. 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 adjusted by EIA to reflect EIA assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 24 figs., 19 tabs.

  18. Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

    Limongi, Roberto; Silva, Angélica M

    2016-11-01

    The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

  19. A review on the young history of the wind power short-term prediction

    Costa, A.; Crespo, A.; Navarro, J.

    2008-01-01

    This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art oil models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought...... on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly...

  20. Short-term wind power combined forecasting based on error forecast correction

    Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

    2016-01-01

    Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed