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Sample records for short-term approach based

  1. Short-term load forecasting by a neuro-fuzzy based approach

    Energy Technology Data Exchange (ETDEWEB)

    Ruey-Hsun Liang; Ching-Chi Cheng [National Yunlin University of Science and Technology (China). Dept. of Electrical Engineering

    2002-02-01

    An approach based on an artificial neural network (ANN) combined with a fuzzy system is proposed for short-term load forecasting. This approach was developed in order to reach the desired short-term load forecasting in an efficient manner. Over the past few years, ANNs have attained the ability to manage a great deal of system complexity and are now being proposed as powerful computational tools. In order to select the appropriate load as the input for the desired forecasting, the Pearson analysis method is first applied to choose two historical record load patterns that are similar to the forecasted load pattern. These two load patterns and the required weather parameters are then fuzzified and input into a neural network for training or testing the network. The back-propagation (BP) neural network is applied to determine the preliminary forecasted load. In addition, the rule base for the fuzzy inference machine contains important linguistic membership function terms with knowledge in the form of fuzzy IF-THEN rules. This produces the load correction inference from the historical information and past forecasted load errors to obtain an inferred load error. Adding the inferred load error to the preliminary forecasted load, we can obtain the finial forecasted load. The effectiveness of the proposed approach to the short-term load-forecasting problem is demonstrated using practical data from the Taiwan Power Company (TPC). (Author)

  2. A Gaussian process regression based hybrid approach for short-term wind speed prediction

    International Nuclear Information System (INIS)

    Zhang, Chi; Wei, Haikun; Zhao, Xin; Liu, Tianhong; Zhang, Kanjian

    2016-01-01

    Highlights: • A novel hybrid approach is proposed for short-term wind speed prediction. • This method combines the parametric AR model with the non-parametric GPR model. • The relative importance of different inputs is considered. • Different types of covariance functions are considered and combined. • It can provide both accurate point forecasts and satisfactory prediction intervals. - Abstract: This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian process regression (GPR) for probabilistic wind speed forecasting. In the proposed approach, the AR model is employed to capture the overall structure from wind speed series, and the GPR is adopted to extract the local structure. Additionally, automatic relevance determination (ARD) is used to take into account the relative importance of different inputs, and different types of covariance functions are combined to capture the characteristics of the data. The proposed hybrid model is compared with the persistence model, artificial neural network (ANN), and support vector machine (SVM) for one-step ahead forecasting, using wind speed data collected from three wind farms in China. The forecasting results indicate that the proposed method can not only improve point forecasts compared with other methods, but also generate satisfactory prediction intervals.

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

    International Nuclear Information System (INIS)

    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

  4. A PSO based Artificial Neural Network approach for short term unit commitment problem

    Directory of Open Access Journals (Sweden)

    AFTAB AHMAD

    2010-10-01

    Full Text Available Unit commitment (UC is a non-linear, large scale, complex, mixed-integer combinatorial constrained optimization problem. This paper proposes, a new hybrid approach for generating unit commitment schedules using swarm intelligence learning rule based neural network. The training data has been generated using dynamic programming for machines without valve point effects and using genetic algorithm for machines with valve point effects. A set of load patterns as inputs and the corresponding unit generation schedules as outputs are used to train the network. The neural network fine tunes the best results to the desired targets. The proposed approach has been validated for three thermal machines with valve point effects and without valve point effects. The results are compared with the approaches available in the literature. The PSO-ANN trained model gives better results which show the promise of the proposed methodology.

  5. A hybrid wavelet transform based short-term wind speed forecasting approach.

    Science.gov (United States)

    Wang, Jujie

    2014-01-01

    It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

  6. A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Javier Moriano

    2016-01-01

    Full Text Available In recent years, Secondary Substations (SSs are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected.

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

    Directory of Open Access Journals (Sweden)

    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.

  8. Fuzzy approach for short term load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    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)

  9. A novel economy reflecting short-term load forecasting approach

    International Nuclear Information System (INIS)

    Lin, Cheng-Ting; Chou, Li-Der

    2013-01-01

    Highlights: ► We combine MA line of TAIEX and SVR to overcome the load demands over-prediction problems caused by the economic downturn. ► The Taiwan island-wide electricity power system was used as the case study. ► Short- to middle-term MA lines of TAIEX are found to be good economic input variables for load forecasting models. - Abstract: The global economic downturn in 2008 and 2009, which was spurred by the bankruptcy of Lehman Brothers, sharply reduced the demand for electricity load. Conventional load-forecasting approaches were unable to respond to sudden changes in the economy, because these approaches do not consider the effect of economic factors. Therefore, the over-prediction problem occurred. To overcome this problem, this paper proposes a novel, economy-reflecting, short-term load forecasting (STLF) approach based on theories of moving average (MA) line of stock index and machine learning. In this approach, the stock indices decision model is designed to reflect fluctuations in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) series, which is selected as an optimal input variable in support vector regression load forecasting model at an appropriate timing. The Taiwan island-wide hourly electricity load demands from 2008 to 2010 are used as the case study for performance benchmarking. Results show that the proposed approach with a 60-day MA of the TAIEX as economic learning pattern achieves good forecasting performance. It outperforms the conventional approach by 29.16% on average during economic downturn-affected days. Overall, the proposed approach successfully overcomes the over-prediction problems caused by the economic downturn. To the best of our knowledge, this paper is the first attempt to apply MA line theory of stock index on STLF.

  10. Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

    Energy Technology Data Exchange (ETDEWEB)

    Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

    2011-02-15

    In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

  11. Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Pousinho, H.M.I.; Mendes, V.M.F.

    2011-01-01

    In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

  12. Short-term Power Load Forecasting Based on Balanced KNN

    Science.gov (United States)

    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.

  13. Effects of short term and long term soil warming on ecosystem phenology of a sub-arctic grassland: an NDVI-based approach

    Science.gov (United States)

    Leblans, Niki; Sigurdsson, Bjarni D.; Janssens, Ivan A.

    2014-05-01

    Phenology has been defined as the study of the timing of recurring biological events and the causes of their timing with regard to abiotic and biotic factors. Ecosystem phenology, including the onset of the growing season and its senescence in autumn, plays an important role in the carbon, water and energy exchange between biosphere and atmosphere at higher latitudes. Factors that influence ecosystem phenology can therefore induce important climate-controlling feedback mechanisms. Global surface temperatures have been predicted to increase in the coming decades. Hence, a better understanding of the effect of temperature on ecosystem phenology is essential. Natural geothermal soil temperature gradients in Iceland offer a unique opportunity to study the soil temperature (Ts) dependence of ecosystem phenology and distinguish short-term (transient) warming effects (in recently established Ts gradients) from long-term (permanent) effects (in centuries-old Ts gradients). This research was performed in the framework of an international research project (ForHot; www.forhot.is). ForHot includes two natural grassland areas with gradients in Ts, dominated by Festuca sp., Agrostis sp.. The first warmed area was created in 2008, when an earthquake in S-Iceland caused geothermal systems to be shifted to previously cold soils. The second area is located about 3 km away from this newly warmed grassland. For this area, there are proofs that the natural soil warming has been continuous for at least 300 year. In the present study we focus on Ts elevation gradients of +0 to +10°C. The experiment consists of five transects with five temperature levels (+0,+1,+3,+5 and +10°C) in the two aforementioned grassland ecosystems (n=25 in each grassland). From April until November 2013, weekly measurements of the normalized difference vegetation index (NDVI) were taken. In the short-term warmed grassland, the greening of the vegetation was 36 days advanced at +10°C Ts and the date of 50

  14. Dimension-based attention in visual short-term memory.

    Science.gov (United States)

    Pilling, Michael; Barrett, Doug J K

    2016-07-01

    We investigated how dimension-based attention influences visual short-term memory (VSTM). This was done through examining the effects of cueing a feature dimension in two perceptual comparison tasks (change detection and sameness detection). In both tasks, a memory array and a test array consisting of a number of colored shapes were presented successively, interleaved by a blank interstimulus interval (ISI). In Experiment 1 (change detection), the critical event was a feature change in one item across the memory and test arrays. In Experiment 2 (sameness detection), the critical event was the absence of a feature change in one item across the two arrays. Auditory cues indicated the feature dimension (color or shape) of the critical event with 80 % validity; the cues were presented either prior to the memory array, during the ISI, or simultaneously with the test array. In Experiment 1, the cue validity influenced sensitivity only when the cue was given at the earliest position; in Experiment 2, the cue validity influenced sensitivity at all three cue positions. We attributed the greater effectiveness of top-down guidance by cues in the sameness detection task to the more active nature of the comparison process required to detect sameness events (Hyun, Woodman, Vogel, Hollingworth, & Luck, Journal of Experimental Psychology: Human Perception and Performance, 35; 1140-1160, 2009).

  15. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

    International Nuclear Information System (INIS)

    Pousinho, H.M.I.; Mendes, V.M.F.; Catalao, J.P.S.

    2011-01-01

    The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

  16. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

    Energy Technology Data Exchange (ETDEWEB)

    Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal); Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal)

    2011-01-15

    The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches. (author)

  17. A heuristic approach for short-term operations planning in a catering company

    DEFF Research Database (Denmark)

    Farahani, Poorya; Grunow, Martin; Günther, H.O.

    2009-01-01

    Certain types of food such as catering foods decay very rapidly. This paper investigates how the quality of such foods can be improved by shortening the time interval between production and delivery. To this end, we develop an approach which integrates short-term production and distribution...... planning in a novel iterative scheme. The production scheduling problem is solved through an MILP modeling approach which is based on a block planning formulation complemented by a heuristic simplification procedure. Our investigation was motivated by a catering company located in Denmark. The production...... configuration and the processes assumed in our numerical experiments reflect real settings from this company. First numerical results are reported which demonstrate the applicability of the proposed approach....

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

    Directory of Open Access Journals (Sweden)

    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.

  19. Deep Neural Network Based Demand Side Short Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Seunghyoung Ryu

    2016-12-01

    Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

  20. Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-07-26

    In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.

  1. Short-Term Success versus Long-Term Failure: A Simulation-Based Approach for Understanding the Potential of Zambia’s Fertilizer Subsidy Program in Enhancing Maize Availability

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    Andreas Gerber

    2016-10-01

    Full Text Available In Sub-Saharan Africa, food-related policies such as fertilizer subsidy programs (FSPs have undergone a revival and triggered a controversy about their impact. In this article I applied a simulation-based approach to examine the FSPs’ short- and long-term potential for increasing maize availability in Zambia. The study revealed that FSPs are an effective policy measure to enhance maize availability in the short-term. However, in the long-term, the food system becomes dependent on the government’s annual expenses. The dependency occurs because FSPs fail to build up adequate stock levels of soil organic matter (SOM, which is an important source of resilience and productivity, and thus represents a long-term leverage point in Zambia’s maize production system. For this reason, alternative policies that combine increasing productivity and building up SOM stock levels were analyzed. They were found to be a viable means for enhancing long-term maize availability. The study concludes that gradually reducing investments in FSPs while simultaneously promoting farming practices that build up SOM stock levels is a promising strategy to enhance maize availability sustainably.

  2. Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-08-23

    In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

  3. Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

    2017-05-17

    A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severe voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.

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

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    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.

  5. A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

    Directory of Open Access Journals (Sweden)

    Farshid Keynia

    2011-03-01

    Full Text Available Short-term load forecast (STLF is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.

  6. Assessing the short term impact of air pollution on mortality: a matching approach.

    Science.gov (United States)

    Baccini, Michela; Mattei, Alessandra; Mealli, Fabrizia; Bertazzi, Pier Alberto; Carugno, Michele

    2017-02-10

    The opportunity to assess short term impact of air pollution relies on the causal interpretation of the exposure-response association. However, up to now few studies explicitly faced this issue within a causal inference framework. In this paper, we reformulated the problem of assessing the short term impact of air pollution on health using the potential outcome approach to causal inference. We considered the impact of high daily levels of particulate matter ≤10 μm in diameter (PM 10 ) on mortality within two days from the exposure in the metropolitan area of Milan (Italy), during the period 2003-2006. Our research focus was the causal impact of a hypothetical intervention setting daily air pollution levels under a pre-fixed threshold. We applied a matching procedure based on propensity score to estimate the total number of attributable deaths (AD) during the study period. After defining the number of attributable deaths in terms of difference between potential outcomes, we used the estimated propensity score to match each high exposure day, namely each day with a level of exposure higher than 40 μg/m 3 , with a day with similar background characteristics but a level of exposure lower than 40 μg/m 3 . Then, we estimated the impact by comparing mortality between matched days. During the study period daily exposures larger than 40 μg/m 3 were responsible for 1079 deaths (90% CI: 116; 2042). The impact was more evident among the elderly than in the younger age classes. Exposures ≥ 40 μg/m 3 were responsible, among the elderly, for 1102 deaths (90% CI: 388, 1816), of which 797 from cardiovascular causes and 243 from respiratory causes. Clear evidence of an impact on respiratory mortality was found also in the age class 65-74, with 87 AD (90% CI: 11, 163). The propensity score matching turned out to be an appealing method to assess historical impacts in this field, which guarantees that the estimated total number of AD can be derived directly as sum

  7. A New Two-Stage Approach to Short Term Electrical Load Forecasting

    Directory of Open Access Journals (Sweden)

    Dragan Tasić

    2013-04-01

    Full Text Available In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach.

  8. Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

    Directory of Open Access Journals (Sweden)

    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.

  9. Short-term versus long-term approaches to the development of tourism-related policies

    OpenAIRE

    Dredge, Dianne

    2015-01-01

    Tourism policy development is an increasingly complex activity involving multiple public sector agencies, industry and community stakeholders and non-government organisations at different scales. This discussion paper examines the implications for tourism of governments adopting short- term versus long-term approaches to the development of tourism related policies and identifies policy considerations to maximize the growth potential of tourism. The key issue is to understand how governments c...

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

    International Nuclear Information System (INIS)

    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

  11. Short-term electricity prices forecasting in a competitive market: A neural network approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.

    2007-01-01

    This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)

  12. SHORT-TERM SOLAR FLARE LEVEL PREDICTION USING A BAYESIAN NETWORK APPROACH

    International Nuclear Information System (INIS)

    Yu Daren; Huang Xin; Hu Qinghua; Zhou Rui; Wang Huaning; Cui Yanmei

    2010-01-01

    A Bayesian network approach for short-term solar flare level prediction has been proposed based on three sequences of photospheric magnetic field parameters extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. The magnetic measures, the maximum horizontal gradient, the length of neutral line, and the number of singular points do not have determinate relationships with solar flares, so the solar flare level prediction is considered as an uncertainty reasoning process modeled by the Bayesian network. The qualitative network structure which describes conditional independent relationships among magnetic field parameters and the quantitative conditional probability tables which determine the probabilistic values for each variable are learned from the data set. Seven sequential features-the maximum, the mean, the root mean square, the standard deviation, the shape factor, the crest factor, and the pulse factor-are extracted to reduce the dimensions of the raw sequences. Two Bayesian network models are built using raw sequential data (BN R ) and feature extracted data (BN F ), respectively. The explanations of these models are consistent with physical analyses of experts. The performances of the BN R and the BN F appear comparable with other methods. More importantly, the comprehensibility of the Bayesian network models is better than other methods.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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.

  15. Short-Term and Long-Term Educational Mobility of Families: A Two-Sex Approach.

    Science.gov (United States)

    Song, Xi; Mare, Robert D

    2017-02-01

    We use a multigenerational perspective to investigate how families reproduce and pass their educational advantages to succeeding generations. Unlike traditional mobility studies that have typically focused on one-sex influences from fathers to sons, we rely on a two-sex approach that accounts for interactions between males and females-the process in which males and females mate and have children with those of similar educational statuses and jointly determine the educational status attainment of their offspring. Using data from the Panel Study of Income Dynamics, we approach this issue from both a short-term and a long-term perspective. For the short term, grandparents' educational attainments have a direct association with grandchildren's education as well as an indirect association that is mediated by parents' education and demographic behaviors. For the long term, initial educational advantages of families may benefit as many as three subsequent generations, but such advantages are later offset by the lower fertility of highly educated persons. Yet, all families eventually achieve the same educational distribution of descendants because of intermarriages between families of high- and low-education origin.

  16. Prepectoral Implant-Based Breast Reconstruction and Postmastectomy Radiotherapy: Short-Term Outcomes

    Directory of Open Access Journals (Sweden)

    Steven Sigalove, MD

    2017-12-01

    Conclusions:. Immediate implant-based prepectoral breast reconstruction followed by PMRT appears to be well tolerated, with no excess risk of adverse outcomes, at least in the short term. Longer follow-up is needed to better understand the risk of PMRT in prepectorally reconstructed breasts.

  17. Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

    Directory of Open Access Journals (Sweden)

    Xuejun Chen

    2014-01-01

    Full Text Available As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H weighted average smoothing method, ensemble empirical mode decomposition (EEMD algorithm, and nonlinear autoregressive (NAR neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems.

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

    International Nuclear Information System (INIS)

    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

  19. Short-term versus long-term approaches to the development of tourism-related policies

    DEFF Research Database (Denmark)

    Dredge, Dianne

    Tourism policy development is an increasingly complex activity involving multiple public sector agencies, industry and community stakeholders and non-government organisations at different scales. This discussion paper examines the implications for tourism of governments adopting short- term versus...... long-term approaches to the development of tourism related policies and identifies policy considerations to maximize the growth potential of tourism. The key issue is to understand how governments can strengthen their support for tourism growth and development by taking an integrated cross......-sector policy approach. The discussion paper commences by examining the unique character of tourism policyscape and recognises that it involves a wide variety of inter-linked policy sectors that often operate and develop policies in separate policy processes. Little attention is placed on cross-sector policy...

  20. Short-term wind power combined forecasting based on error forecast correction

    International Nuclear Information System (INIS)

    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

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

    International Nuclear Information System (INIS)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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)

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

    International Nuclear Information System (INIS)

    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 predictability of crude oil markets: A detrended fluctuation analysis approach

    International Nuclear Information System (INIS)

    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)

  5. A neutral network based technique for short-term forecasting of anomalous load periods

    Energy Technology Data Exchange (ETDEWEB)

    Sforna, M [ENEL, s.p.a, Italian Power Company (Italy); Lamedica, R; Prudenzi, A [Rome Univ. ` La Sapienza` , Rome (Italy); Caciotta, M; Orsolini Cencelli, V [Rome Univ. III, Rome (Italy)

    1995-01-01

    The paper illustrates a part of the research activity conducted by authors in the field of electric Short Term Load Forecasting (STLF) based on Artificial Neural Network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architecture provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to `anomalous` load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen`s Self Organizing Map (SOM). The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer percept ron with a back propagation learning algorithm similar to the ones above mentioned. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations.

  6. Feature-based and object-based attention orientation during short-term memory maintenance.

    Science.gov (United States)

    Ku, Yixuan

    2015-12-01

    Top-down attention biases the short-term memory (STM) processing at multiple stages. Orienting attention during the maintenance period of STM by a retrospective cue (retro-cue) strengthens the representation of the cued item and improves the subsequent STM performance. In a recent article, Backer et al. (Backer KC, Binns MA, Alain C. J Neurosci 35: 1307-1318, 2015) extended these findings from the visual to the auditory domain and combined electroencephalography to dissociate neural mechanisms underlying feature-based and object-based attention orientation. Both event-related potentials and neural oscillations explained the behavioral benefits of retro-cues and favored the theory that feature-based and object-based attention orientation were independent. Copyright © 2015 the American Physiological Society.

  7. Short-Term Intercultural Psychotherapy: Ethnographic Inquiry

    Science.gov (United States)

    Seeley, Karen M.

    2004-01-01

    This article examines the challenges specific to short-term intercultural treatments and recently developed approaches to intercultural treatments based on notions of cultural knowledge and cultural competence. The article introduces alternative approaches to short-term intercultural treatments based on ethnographic inquiry adapted for clinical…

  8. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

    OpenAIRE

    Wen-Yeau Chang

    2013-01-01

    High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...

  9. Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

    OpenAIRE

    Li, Xiangang; Wu, Xihong

    2014-01-01

    Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed an...

  10. Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

    Science.gov (United States)

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

  11. Short-term PV/T module temperature prediction based on PCA-RBF neural network

    Science.gov (United States)

    Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng

    2018-02-01

    Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.

  12. Feature-Based Visual Short-Term Memory Is Widely Distributed and Hierarchically Organized.

    Science.gov (United States)

    Dotson, Nicholas M; Hoffman, Steven J; Goodell, Baldwin; Gray, Charles M

    2018-06-15

    Feature-based visual short-term memory is known to engage both sensory and association cortices. However, the extent of the participating circuit and the neural mechanisms underlying memory maintenance is still a matter of vigorous debate. To address these questions, we recorded neuronal activity from 42 cortical areas in monkeys performing a feature-based visual short-term memory task and an interleaved fixation task. We find that task-dependent differences in firing rates are widely distributed throughout the cortex, while stimulus-specific changes in firing rates are more restricted and hierarchically organized. We also show that microsaccades during the memory delay encode the stimuli held in memory and that units modulated by microsaccades are more likely to exhibit stimulus specificity, suggesting that eye movements contribute to visual short-term memory processes. These results support a framework in which most cortical areas, within a modality, contribute to mnemonic representations at timescales that increase along the cortical hierarchy. Copyright © 2018 Elsevier Inc. All rights reserved.

  13. Persistent spatial information in the frontal eye field during object-based short-term memory.

    Science.gov (United States)

    Clark, Kelsey L; Noudoost, Behrad; Moore, Tirin

    2012-08-08

    Spatial attention is known to gate entry into visual short-term memory, and some evidence suggests that spatial signals may also play a role in binding features or protecting object representations during memory maintenance. To examine the persistence of spatial signals during object short-term memory, the activity of neurons in the frontal eye field (FEF) of macaque monkeys was recorded during an object-based delayed match-to-sample task. In this task, monkeys were trained to remember an object image over a brief delay, regardless of the locations of the sample or target presentation. FEF neurons exhibited visual, delay, and target period activity, including selectivity for sample location and target location. Delay period activity represented the sample location throughout the delay, despite the irrelevance of spatial information for successful task completion. Furthermore, neurons continued to encode sample position in a variant of the task in which the matching stimulus never appeared in their response field, confirming that FEF maintains sample location independent of subsequent behavioral relevance. FEF neurons also exhibited target-position-dependent anticipatory activity immediately before target onset, suggesting that monkeys predicted target position within blocks. These results show that FEF neurons maintain spatial information during short-term memory, even when that information is irrelevant for task performance.

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

    Directory of Open Access Journals (Sweden)

    Haixiang Zang

    2016-01-01

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

  15. A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-08-25

    This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.

  16. A simulation approach for analysis of short-term security of natural gas supply in Colombia

    International Nuclear Information System (INIS)

    Villada, Juan; Olaya, Yris

    2013-01-01

    Achieving security of gas supply implies diversifying gas sources, while having enough supply, transportation, and storage capacity to meet demand peaks and supply interruptions. Devising a strategy for securing gas supply is not straightforward because gas supply depends on complex interactions of production, demand and infrastructure, and it is exposed to economic, regulatory, political, environmental and technical risks. To address this complexity, we propose a simulation approach that replicates the structure of the gas supply chain, including transportation constraints and demand fluctuations. We build and calibrate a computer model for the Colombian gas sector, and run the model to assess the impact of expanding transportation capacity and increasing market flexibility on the security of supply. Our analysis focuses on the operation and planned and proposed expansions of the transportation infrastructure because adequate regulation and development of this infrastructure can contribute to increase the security of supply in the gas sector. We find that proposed import facilities, specifically LNG import terminals at Buenaventura, increase system's security under the current market structure. - Highlights: ► We build a simulation model for analyzing natural gas trade in Colombia. ► The model captures the structure of the gas network and on market rules. ► We simulate investment decisions to increase short-term security of supply. ► Securing supply would need LNG imports and expansion of pipeline capacity.

  17. Performance of fuzzy approach in Malaysia short-term electricity load forecasting

    Science.gov (United States)

    Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee

    2014-12-01

    Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in forecasting electricity load when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.

  18. Response of stream invertebrates to short-term salinization: A mesocosm approach

    International Nuclear Information System (INIS)

    Cañedo-Argüelles, Miguel; Grantham, Theodore E.; Perrée, Isabelle; Rieradevall, Maria; Céspedes-Sánchez, Raquel; Prat, Narcís

    2012-01-01

    Salinization is a major and growing threat to freshwater ecosystems, yet its effects on aquatic invertebrates have been poorly described at a community-level. Here we use a controlled experimental setting to evaluate short-term stream community responses to salinization, under conditions designed to replicate the duration (72 h) and intensity (up to 5 mS cm −1 ) of salinity pulses common to Mediterranean rivers subjected to mining pollution during runoff events. There was a significant overall effect, but differences between individual treatments and the control were only significant for the highest salinity treatment. The community response to salinization was characterized by a decline in total invertebrate density, taxon richness and diversity, an increase in invertebrate drift and loss of the most sensitive taxa. The findings indicate that short-term salinity increases have a significant impact on the stream invertebrate community, but concentrations of 5 mS cm −1 are needed to produce a significant ecological response. - Highlights: ► Short-term salinization has a significant impact on the aquatic invertebrates. ► A significant short-term ecological response is registered at 5 mS cm −1 . ► Salinization causes a decline in invertebrate density, richness and diversity. ► Biotic quality indices decline with increasing salinity and exposure time. - Short-term salinization in a stream mesocosm caused a significant response in the aquatic invertebrate community and led to declines in biological quality indices.

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

    Directory of Open Access Journals (Sweden)

    Francesca Gagliardi

    2017-07-01

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

  20. A least squares approach for efficient and reliable short-term versus long-term optimization

    DEFF Research Database (Denmark)

    Christiansen, Lasse Hjuler; Capolei, Andrea; Jørgensen, John Bagterp

    2017-01-01

    The uncertainties related to long-term forecasts of oil prices impose significant financial risk on ventures of oil production. To minimize risk, oil companies are inclined to maximize profit over short-term horizons ranging from months to a few years. In contrast, conventional production...... optimization maximizes long-term profits over horizons that span more than a decade. To address this challenge, the oil literature has introduced short-term versus long-term optimization. Ideally, this problem is solved by a posteriori multi-objective optimization methods that generate an approximation...... the balance between the objectives, leaving an unfulfilled potential to increase profits. To promote efficient and reliable short-term versus long-term optimization, this paper introduces a natural way to characterize desirable Pareto points and proposes a novel least squares (LS) method. Unlike hierarchical...

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

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

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

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

    Science.gov (United States)

    Fiebig, Florian

    2017-01-01

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

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

    Science.gov (United States)

    Fiebig, Florian; Lansner, Anders

    2017-01-04

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

  4. A new cascade NN based method to short-term load forecast in deregulated electricity market

    International Nuclear Information System (INIS)

    Kouhi, Sajjad; Keynia, Farshid

    2013-01-01

    Highlights: • We are proposed a new hybrid cascaded NN based method and WT to short-term load forecast in deregulated electricity market. • An efficient preprocessor consist of normalization and shuffling of signals is presented. • In order to select the best inputs, a two-stage feature selection is presented. • A new cascaded structure consist of three cascaded NNs is used as forecaster. - Abstract: Short-term load forecasting (STLF) is a major discussion in efficient operation of power systems. The electricity load is a nonlinear signal with time dependent behavior. The area of electricity load forecasting has still essential need for more accurate and stable load forecast algorithm. To improve the accuracy of prediction, a new hybrid forecast strategy based on cascaded neural network is proposed for STLF. This method is consists of wavelet transform, an intelligent two-stage feature selection, and cascaded neural network. The feature selection is used to remove the irrelevant and redundant inputs. The forecast engine is composed of three cascaded neural network (CNN) structure. This cascaded structure can be efficiently extract input/output mapping function of the nonlinear electricity load data. Adjustable parameters of the intelligent feature selection and CNN is fine-tuned by a kind of cross-validation technique. The proposed STLF is tested on PJM and New York electricity markets. It is concluded from the result, the proposed algorithm is a robust forecast method

  5. An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments

    International Nuclear Information System (INIS)

    Azadeh, A.; Asadzadeh, S.M.; Ghanbari, A.

    2010-01-01

    Accurate short-term natural gas (NG) demand estimation and forecasting is vital for policy and decision-making process in energy sector. Moreover, conventional methods may not provide accurate results. This paper presents an adaptive network-based fuzzy inference system (ANFIS) for estimation of NG demand. Standard input variables are used which are day of the week, demand of the same day in previous year, demand of a day before and demand of 2 days before. The proposed ANFIS approach is equipped with pre-processing and post-processing concepts. Moreover, input data are pre-processed (scaled) and finally output data are post-processed (returned to its original scale). The superiority and applicability of the ANFIS approach is shown for Iranian NG consumption from 22/12/2007 to 30/6/2008. Results show that ANFIS provides more accurate results than artificial neural network (ANN) and conventional time series approach. The results of this study provide policy makers with an appropriate tool to make more accurate predictions on future short-term NG demand. This is because the proposed approach is capable of handling non-linearity, complexity as well as uncertainty that may exist in actual data sets due to erratic responses and measurement errors.

  6. Short-term data forecasting based on wavelet transformation and chaos theory

    Science.gov (United States)

    Wang, Yi; Li, Cunbin; Zhang, Liang

    2017-09-01

    A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of “data nail” on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.

  7. Fragile visual short-term memory is an object-based and location-specific store

    NARCIS (Netherlands)

    Pinto, Y.; Sligte, I.G.; Shapiro, K.L.; Lamme, V.A.F.

    2013-01-01

    Fragile visual short-term memory (FM) is a recently discovered form of visual short-term memory. Evidence suggests that it provides rich and high-capacity storage, like iconic memory, yet it exists, without interference, almost as long as visual working memory. In the present study, we sought to

  8. The neural bases of the short-term storage of verbal information are anatomically variable across individuals.

    Science.gov (United States)

    Feredoes, Eva; Tononi, Giulio; Postle, Bradley R

    2007-10-10

    What are the precise brain regions supporting the short-term retention of verbal information? A previous functional magnetic resonance imaging (fMRI) study suggested that they may be topographically variable across individuals, occurring, in most, in regions posterior to prefrontal cortex (PFC), and that detection of these regions may be best suited to a single-subject (SS) approach to fMRI analysis (Feredoes and Postle, 2007). In contrast, other studies using spatially normalized group-averaged (SNGA) analyses have localized storage-related activity to PFC. To evaluate the necessity of the regions identified by these two methods, we applied repetitive transcranial magnetic stimulation (rTMS) to SS- and SNGA-identified regions throughout the retention period of a delayed letter-recognition task. Results indicated that rTMS targeting SS analysis-identified regions of left perisylvian and sensorimotor cortex impaired performance, whereas rTMS targeting the SNGA-identified region of left caudal PFC had no effect on performance. Our results support the view that the short-term retention of verbal information can be supported by regions associated with acoustic, lexical, phonological, and speech-based representation of information. They also suggest that the brain bases of some cognitive functions may be better detected by SS than by SNGA approaches to fMRI data analysis.

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

    International Nuclear Information System (INIS)

    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. GSM base stations: short-term effects on well-being.

    Science.gov (United States)

    Augner, Christoph; Florian, Matthias; Pauser, Gernot; Oberfeld, Gerd; Hacker, Gerhard W

    2009-01-01

    The purpose of this study was to examine the effects of short-term GSM (Global System for Mobile Communications) cellular phone base station RF-EMF (radiofrequency electromagnetic fields) exposure on psychological symptoms (good mood, alertness, calmness) as measured by a standardized well-being questionnaire. Fifty-seven participants were selected and randomly assigned to one of three different exposure scenarios. Each of those scenarios subjected participants to five 50-min exposure sessions, with only the first four relevant for the study of psychological symptoms. Three exposure levels were created by shielding devices in a field laboratory, which could be installed or removed during the breaks between sessions such that double-blinded conditions prevailed. The overall median power flux densities were 5.2 microW/m(2) during "low," 153.6 microW/m(2) during "medium," and 2126.8 microW/m(2) during "high" exposure sessions. For scenario HM and MH, the first and third sessions were "low" exposure. The second session was "high" and the fourth was "medium" in scenario HM; and vice versa for scenario MH. Scenario LL had four successive "low" exposure sessions constituting the reference condition. Participants in scenarios HM and MH (high and medium exposure) were significantly calmer during those sessions than participants in scenario LL (low exposure throughout) (P = 0.042). However, no significant differences between exposure scenarios in the "good mood" or "alertness" factors were obtained. We conclude that short-term exposure to GSM base station signals may have an impact on well-being by reducing psychological arousal. (c) 2008 Wiley-Liss, Inc.

  11. Response of rat lung tissue to short-term hyperoxia: a proteomic approach.

    Science.gov (United States)

    Spelten, Oliver; Wetsch, Wolfgang A; Wrettos, Georg; Kalenka, Armin; Hinkelbein, Jochen

    2013-11-01

    An inspiratory oxygen fraction of 1.0 is often required to avoid hypoxia both in many pre- and in-hospital situations. On the other hand, hyperoxia may lead to deleterious consequences (cell growth inhibition, inflammation, and apoptosis) for numerous tissues including the lung. Whereas clinical effects of hyperoxic lung injury are well known, its impact on the expression of lung proteins has not yet been evaluated sufficiently. The aim of this study was to analyze time-dependent alterations of protein expression in rat lung tissue after short-term normobaric hyperoxia (NH). After approval of the local ethics committee for animal research, N = 36 Wistar rats were randomized into six different groups: three groups with NH with exposure to 100 % oxygen for 3 h and three groups with normobaric normoxia (NN) with exposure to room air (21 % oxygen). After the end of the experiments, lungs were removed immediately (NH0 and NN0), after 3 days (NH3 and NN3) and after 7 days (NH7 and NN7). Lung lysates were analyzed by two-dimensional gel electrophoresis (2D-GE) followed by peptide mass fingerprinting using mass spectrometry. Statistical analysis was performed with Delta 2D (DECODON GmbH, Greifswald, Germany; ANOVA, Bonferroni correction, p pO2 was significantly higher in NH-groups compared to NN-groups (581 ± 28 vs. 98 ± 12 mmHg; p < 0.01), all other physiological parameters did not differ. Expression of 14 proteins were significantly altered: two proteins were up-regulated and 12 proteins were down-regulated. Even though NH was comparatively short termed, significant alterations in lung protein expression could be demonstrated up to 7 days after hyperoxia. The identified proteins indicate an association with cell growth inhibition, regulation of apoptosis, and approval of structural cell integrity.

  12. Supervised guiding long-short term memory for image caption generation based on object classes

    Science.gov (United States)

    Wang, Jian; Cao, Zhiguo; Xiao, Yang; Qi, Xinyuan

    2018-03-01

    The present models of image caption generation have the problems of image visual semantic information attenuation and errors in guidance information. In order to solve these problems, we propose a supervised guiding Long Short Term Memory model based on object classes, named S-gLSTM for short. It uses the object detection results from R-FCN as supervisory information with high confidence, and updates the guidance word set by judging whether the last output matches the supervisory information. S-gLSTM learns how to extract the current interested information from the image visual se-mantic information based on guidance word set. The interested information is fed into the S-gLSTM at each iteration as guidance information, to guide the caption generation. To acquire the text-related visual semantic information, the S-gLSTM fine-tunes the weights of the network through the back-propagation of the guiding loss. Complementing guidance information at each iteration solves the problem of visual semantic information attenuation in the traditional LSTM model. Besides, the supervised guidance information in our model can reduce the impact of the mismatched words on the caption generation. We test our model on MSCOCO2014 dataset, and obtain better performance than the state-of-the- art models.

  13. Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations

    Directory of Open Access Journals (Sweden)

    Mingjun Deng

    2016-01-01

    Full Text Available There are many short-term road travel time forecasting studies based on time series, but indeed, road travel time not only relies on the historical travel time series, but also depends on the road and its adjacent sections history flow. However, few studies have considered that. This paper is based on the correlation of flow spatial distribution and the road travel time series, applying nearest neighbor and nonparametric regression method to build a forecasting model. In aspect of spatial nearest neighbor search, three different space distances are defined. In addition, two forecasting functions are introduced: one combines the forecasting value by mean weight and the other uses the reciprocal of nearest neighbors distance as combined weight. Three different distances are applied in nearest neighbor search, which apply to the two forecasting functions. For travel time series, the nearest neighbor and nonparametric regression are applied too. Then minimizing forecast error variance is utilized as an objective to establish the combination model. The empirical results show that the combination model can improve the forecast performance obviously. Besides, the experimental results of the evaluation for the computational complexity show that the proposed method can satisfy the real-time requirement.

  14. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

    International Nuclear Information System (INIS)

    Zameer, Aneela; Arshad, Junaid; Khan, Asifullah; Raja, Muhammad Asif Zahoor

    2017-01-01

    Highlights: • Genetic programming based ensemble of neural networks is employed for short term wind power prediction. • Proposed predictor shows resilience against abrupt changes in weather. • Genetic programming evolves nonlinear mapping between meteorological measures and wind-power. • Proposed approach gives mathematical expressions of wind power to its independent variables. • Proposed model shows relatively accurate and steady wind-power prediction performance. - Abstract: The inherent instability of wind power production leads to critical problems for smooth power generation from wind turbines, which then requires an accurate forecast of wind power. In this study, an effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming. In contrast to existing series based combination of wind power predictors, whereby the error or variation in the leading predictor is propagated down the stream to the next predictors, the proposed intelligent ensemble predictor avoids this shortcoming by introducing Genetical Programming based semi-stochastic combination of neural networks. It is observed that the decision of the individual base regressors may vary due to the frequent and inherent fluctuations in the atmospheric conditions and thus meteorological properties. The novelty of the reported work lies in creating ensemble to generate an intelligent, collective and robust decision space and thereby avoiding large errors due to the sensitivity of the individual wind predictors. The proposed ensemble based regressor, Genetic Programming based ensemble of Artificial Neural Networks, has been implemented and tested on data taken from five different wind farms located in Europe. Obtained numerical results of the proposed model in terms of various error measures are compared with the recent artificial intelligence based strategies to demonstrate the

  15. Fisher Information Based Meteorological Factors Introduction and Features Selection for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Shuping Cai

    2018-03-01

    Full Text Available Weather information is an important factor in short-term load forecasting (STLF. However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introduction and variable selection in STLF. Fisher information computation for one-dimensional and multidimensional weather variables is first described, and then the introduction of meteorological factors and variables selection for STLF models are discussed in detail. On this basis, different forecasting models with the proposed methodology are established. The proposed methodology is implemented on real data obtained from Electric Power Utility of Zhenjiang, Jiangsu Province, in southeast China. The results show the advantages of the proposed methodology in comparison with other traditional ones regarding prediction accuracy, and it has very good practical significance. Therefore, it can be used as a unified method for introducing weather variables into STLF models, and selecting their features.

  16. Functional and Structural Neuroplasticity Induced by Short-Term Tactile Training Based on Braille Reading.

    Science.gov (United States)

    Debowska, Weronika; Wolak, Tomasz; Nowicka, Anna; Kozak, Anna; Szwed, Marcin; Kossut, Malgorzata

    2016-01-01

    Neuroplastic changes induced by sensory learning have been recognized within the cortices of specific modalities as well as within higher ordered multimodal areas. The interplay between these areas is not fully understood, particularly in the case of somatosensory learning. Here we examined functional and structural changes induced by short-term tactile training based of Braille reading, a task that requires both significant tactile expertise and mapping of tactile input onto multimodal representations. Subjects with normal vision were trained for 3 weeks to read Braille exclusively by touch and scanned before and after training, while performing a same-different discrimination task on Braille characters and meaningless characters. Functional and diffusion-weighted magnetic resonance imaging sequences were used to assess resulting changes. The strongest training-induced effect was found in the primary somatosensory cortex (SI), where we observed bilateral augmentation in activity accompanied by an increase in fractional anisotropy (FA) within the contralateral SI. Increases of white matter fractional anisotropy were also observed in the secondary somatosensory area (SII) and the thalamus. Outside of somatosensory system, changes in both structure and function were found in i.e., the fusiform gyrus, the medial frontal gyri and the inferior parietal lobule. Our results provide evidence for functional remodeling of the somatosensory pathway and higher ordered multimodal brain areas occurring as a result of short-lasting tactile learning, and add to them a novel picture of extensive white matter plasticity.

  17. Culture belief based multi-objective hybrid differential evolutionary algorithm in short term hydrothermal scheduling

    International Nuclear Information System (INIS)

    Zhang Huifeng; Zhou Jianzhong; Zhang Yongchuan; Lu Youlin; Wang Yongqiang

    2013-01-01

    Highlights: ► Culture belief is integrated into multi-objective differential evolution. ► Chaotic sequence is imported to improve evolutionary population diversity. ► The priority of convergence rate is proved in solving hydrothermal problem. ► The results show the quality and potential of proposed algorithm. - Abstract: A culture belief based multi-objective hybrid differential evolution (CB-MOHDE) is presented to solve short term hydrothermal optimal scheduling with economic emission (SHOSEE) problem. This problem is formulated for compromising thermal cost and emission issue while considering its complicated non-linear constraints with non-smooth and non-convex characteristics. The proposed algorithm integrates a modified multi-objective differential evolutionary algorithm into the computation model of culture algorithm (CA) as well as some communication protocols between population space and belief space, three knowledge structures in belief space are redefined according to these problem-solving characteristics, and in the differential evolution a chaotic factor is embedded into mutation operator for avoiding the premature convergence by enlarging the search scale when the search trajectory reaches local optima. Furthermore, a new heuristic constraint-handling technique is utilized to handle those complex equality and inequality constraints of SHOSEE problem. After the application on hydrothermal scheduling system, the efficiency and stability of the proposed CB-MOHDE is verified by its more desirable results in comparison to other method established recently, and the simulation results also reveal that CB-MOHDE can be a promising alternative for solving SHOSEE.

  18. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

    Directory of Open Access Journals (Sweden)

    Wen-Yeau Chang

    2013-09-01

    Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

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

    Directory of Open Access Journals (Sweden)

    Zhifeng Zhong

    2017-01-01

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

  20. Posture-based processing in visual short-term memory for actions.

    Science.gov (United States)

    Vicary, Staci A; Stevens, Catherine J

    2014-01-01

    Visual perception of human action involves both form and motion processing, which may rely on partially dissociable neural networks. If form and motion are dissociable during visual perception, then they may also be dissociable during their retention in visual short-term memory (VSTM). To elicit form-plus-motion and form-only processing of dance-like actions, individual action frames can be presented in the correct or incorrect order. The former appears coherent and should elicit action perception, engaging both form and motion pathways, whereas the latter appears incoherent and should elicit posture perception, engaging form pathways alone. It was hypothesized that, if form and motion are dissociable in VSTM, then recognition of static body posture should be better after viewing incoherent than after viewing coherent actions. However, as VSTM is capacity limited, posture-based encoding of actions may be ineffective with increased number of items or frames. Using a behavioural change detection task, recognition of a single test posture was significantly more likely after studying incoherent than after studying coherent stimuli. However, this effect only occurred for spans of two (but not three) items and for stimuli with five (but not nine) frames. As in perception, posture and motion are dissociable in VSTM.

  1. The effects of long- and short-term interdisciplinary treatment approaches in women with fibromyalgia: a randomized controlled trial.

    Science.gov (United States)

    Saral, Ilknur; Sindel, Dilsad; Esmaeilzadeh, Sina; Sertel-Berk, Hanife Ozlem; Oral, Aydan

    2016-10-01

    We investigated the effects of long- and short-term interdisciplinary treatment approaches for reducing symptoms and improving health-related quality of life (HRQoL) and physical functions of patients with fibromyalgia and compared the effects of two different interdisciplinary treatment approaches. We conducted a prospective, randomized, controlled trial involving 66 women with fibromyalgia eligible for the study at a university hospital setting. The patients were randomized into three groups (allocation ratio 1:1:1) using a computer-generated random numbers: a long-term interdisciplinary treatment group (LG, n = 22) that participated in 10 sessions (3-h once-weekly session for 10 weeks) of cognitive behavioral therapy (CBT) together with exercise training and other fibromyalgia related educational programs (two full days); a short-term interdisciplinary treatment group (SG, n = 22) that received two full days of educational, exercise, and CBT programs; and a control group (CG, n = 22). The patients were evaluated at baseline and 6 months after treatment using the visual analog scale (pain, fatigue, and sleep), Fibromyalgia Impact Questionnaire, Beck Depression Inventory, Short Form-36, tender point numbers, and pressure algometry as primary outcomes. The statistical analysis was confined to the 'per-protocol' set. No blinding was performed. The number of patients analyzed was 21 in the LG, 19 in the SG, and 19 in the CG. The intensity of pain (p treatment approaches when compared with controls; the long-term treatment was found more effective in reducing pain than the short-term. Both, long- and short-term interdisciplinary treatments were effective in reducing the severity of some symptoms and disease activity in patients with fibromyalgia. The short-term program well meets the needs of women with fibromyalgia particularly in relation to pain and health status as measured using FIQ; however, a long-term program may be beneficial in reducing fatigue and

  2. A Methodological Approach for Testing the Viability of Seeds Stored in Short-Term Seed Banks

    Directory of Open Access Journals (Sweden)

    Jose A. FORTE GIL

    2017-12-01

    Full Text Available Efficient management of ‘active’ seed banks – specifically aimed at the short-term storage at room temperature of seeds to be used locally in conservation/regeneration programmes of endemic or endangered plant species – requires establishing the optimal storage time to maintain high seed viability, for each stored species. In this work, germination of seeds of the halophytes Thalictrum maritimum, Centaurea dracunculifolia and Linum maritimum has been investigated. The seeds had been stored for different periods of time in the seed bank of ‘La Albufera’ Natural Park (Valencia, SE Spain after collection in salt marshes of the Park, where small populations of the three species are present. Seeds of T. maritimum and C. dracunculifolia have a relatively short period of viability at room temperature, and should not be stored for more than three years. On the other hand, L. maritimum seeds maintain a high germination percentage and can be kept at room temperature for up to 10 years. T. maritimum seeds, in contrast to those of the other two species, did not germinate in in vitro tests nor when sown directly on a standard substrate, unless a pre-treatment of the seeds was applied, mechanical scarification being the most effective. These results will help to improve the management of the seed bank, to generate more efficiently new plants for reintroduction and reinforcement of populations of these species in their natural ecosystems within the Natural Park.

  3. Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

    Directory of Open Access Journals (Sweden)

    Erick López

    2018-02-01

    Full Text Available Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM and Echo State Network (ESN, have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i the hidden layer is trained with a descending gradient method online using one epoch; (ii the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii, a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used.

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

    Science.gov (United States)

    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.

  5. Macroinvertebrate short-term responses to flow variation and oxygen depletion: A mesocosm approach.

    Science.gov (United States)

    Calapez, Ana R; Branco, Paulo; Santos, José M; Ferreira, Teresa; Hein, Thomas; Brito, António G; Feio, Maria João

    2017-12-01

    In Mediterranean rivers, water scarcity is a key stressor with direct and indirect effects on other stressors, such as water quality decline and inherent oxygen depletion associated with pollutants inputs. Yet, predicting the responses of macroinvertebrates to these stressors combination is quite challenging due to the reduced available information, especially if biotic and abiotic seasonal variations are taken under consideration. This study focused on the response of macroinvertebrates by drift to single and combined effects of water scarcity and dissolved oxygen (DO) depletion over two seasons (winter and spring). A factorial design of two flow velocity levels - regular and low (vL) - with three levels of oxygen depletion - normoxia, medium depletion (dM) and higher depletion (dH) - was carried out in a 5-artificial channels system, in short-term experiments. Results showed that both stressors individually and together had a significant effect on macroinvertebrate drift ratio for both seasons. Single stressor effects showed that macroinvertebrate drift decreased with flow velocity reduction and increased with DO depletion, in both winter and spring experiments. Despite single stressors opposing effects in drift ratio, combined stressors interaction (vL×dM and vL×dH) induced a positive synergistic drift effect for both seasons, but only in winter the drift ratio was different between the levels of DO depletion. Stressors interaction in winter seemed to intensify drift response when reached lower oxygen saturation. Also, drift patterns were different between seasons for all treatments, which may depend on individual's life stage and seasonal behaviour. Water scarcity seems to exacerbate the oxygen depletion conditions resulting into a greater drifting of invertebrates. The potential effects of oxygen depletion should be evaluated when addressing the impacts of water scarcity on river ecosystems, since flow reductions will likely contribute to a higher oxygen

  6. U.S.-Based Short-Term Public Health Cultural Immersion Experience for Chinese Undergraduate Students

    Science.gov (United States)

    Powell, Dorothy Lewis; Biederman, Donna J.

    2017-01-01

    A U.S. and Chinese university developed a short-term student exchange program in public/community health. The program--which consisted of lectures, seminars, field trips, cross-cultural experiences, and a synthesis excursion--resulted in high levels of program satisfaction, increased intrapersonal awareness, and skill acquisition. Program content…

  7. A Simplified Short Term Load Forecasting Method Based on Sequential Patterns

    DEFF Research Database (Denmark)

    Kouzelis, Konstantinos; Bak-Jensen, Birgitte; Mahat, Pukar

    2014-01-01

    Load forecasting is an essential part of a power system both for planning and daily operation purposes. As far as the latter is concerned, short term load forecasting has been broadly used at the transmission level. However, recent technological advancements and legislation have facilitated the i...... in comparison with an ARIMA model....

  8. Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

    DEFF Research Database (Denmark)

    López, Erick; Allende, Héctor; Gil, Esteban

    2018-01-01

    involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed...

  9. Room Volume Estimation Based on Ambiguity of Short-Term Interaural Phase Differences Using Humanoid Robot Head

    Directory of Open Access Journals (Sweden)

    Ryuichi Shimoyama

    2016-07-01

    Full Text Available Humans can recognize approximate room size using only binaural audition. However, sound reverberation is not negligible in most environments. The reverberation causes temporal fluctuations in the short-term interaural phase differences (IPDs of sound pressure. This study proposes a novel method for a binaural humanoid robot head to estimate room volume. The method is based on the statistical properties of the short-term IPDs of sound pressure. The humanoid robot turns its head toward a sound source, recognizes the sound source, and then estimates the ego-centric distance by its stereovision. By interpolating the relations between room volume, average standard deviation, and ego-centric distance experimentally obtained for various rooms in a prepared database, the room volume was estimated by the binaural audition of the robot from the average standard deviation of the short-term IPDs at the estimated distance.

  10. Short-term Prediction of Coronary Heart Disease Mortality in the Czech Republic Based on Data from 1968-2014.

    Czech Academy of Sciences Publication Activity Database

    Reissigová, Jindra; Zvolský, M.

    2018-01-01

    Roč. 26, č. 1 (2018), s. 10-15 ISSN 1210-7778 Institutional support: RVO:67985807 Keywords : mortality * coronary heart diseases * short-term prediction * long-term prediction * national health registries Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Applied mathematics Impact factor: 0.682, year: 2016 https://cejph.szu.cz/artkey/cjp-201801-0002_short-term-prediction-of-coronary- heart -disease-mortality-in-the-czech-republic-based-on-data-from-1968-2014.php

  11. An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Qiang Ni

    2017-10-01

    Full Text Available High quality photovoltaic (PV power prediction intervals (PIs are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

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

    International Nuclear Information System (INIS)

    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

  13. Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

    Directory of Open Access Journals (Sweden)

    Chan-Uk Yeom

    2017-10-01

    Full Text Available This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design includes a two-phase development. First, we generate an initial random-partition matrix and estimate cluster centers for random clustering. The obtained cluster centers are used to determine the premise parameters of fuzzy if-then rules. Next, the linear weights of the TSK fuzzy type are estimated using the least squares estimate (LSE method. These linear weights are used as the consequent parameters in the TSK-ELM design. The experiments were performed on short-term electricity-load data for forecasting. The electricity-load data were used to forecast hourly day-ahead loads given temperature forecasts; holiday information; and historical loads from the New England ISO. In order to quantify the performance of the forecaster, we use metrics and statistical characteristics such as root mean squared error (RMSE as well as mean absolute error (MAE, mean absolute percent error (MAPE, and R-squared, respectively. The experimental results revealed that the proposed method showed good performance when compared with a conventional ELM with four activation functions such sigmoid, sine, radial basis function, and rectified linear unit (ReLU. It possessed superior prediction performance and knowledge information and a small number of rules.

  14. Time-Based Loss in Visual Short-Term Memory Is from Trace Decay, Not Temporal Distinctiveness

    Science.gov (United States)

    Ricker, Timothy J.; Spiegel, Lauren R.; Cowan, Nelson

    2014-01-01

    There is no consensus as to why forgetting occurs in short-term memory tasks. In past work, we have shown that forgetting occurs with the passage of time, but there are 2 classes of theories that can explain this effect. In the present work, we investigate the reason for time-based forgetting by contrasting the predictions of temporal…

  15. Fragile visual short-term memory is an object-based and location-specific store.

    Science.gov (United States)

    Pinto, Yaïr; Sligte, Ilja G; Shapiro, Kimron L; Lamme, Victor A F

    2013-08-01

    Fragile visual short-term memory (FM) is a recently discovered form of visual short-term memory. Evidence suggests that it provides rich and high-capacity storage, like iconic memory, yet it exists, without interference, almost as long as visual working memory. In the present study, we sought to unveil the functional underpinnings of this memory storage. We found that FM is only completely erased when the new visual scene appears at the same location and consists of the same objects as the to-be-recalled information. This result has two important implications: First, it shows that FM is an object- and location-specific store, and second, it suggests that FM might be used in everyday life when the presentation of visual information is appropriately designed.

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

    OpenAIRE

    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...

  17. Short-term hydro generation scheduling of Three Gorges–Gezhouba cascaded hydropower plants using hybrid MACS-ADE approach

    International Nuclear Information System (INIS)

    Mo, Li; Lu, Peng; Wang, Chao; Zhou, Jianzhong

    2013-01-01

    Highlights: • MACS and ADE algorithms are hybridized as MACS-ADE method for solving STHGS problem. • An adaptive mutation is integrated into the proposed algorithm to avoid premature convergence. • MACS and ADE are run in parallel in search of better solution. • Several effective heuristic strategies are designed for dealing with various constraints of STHGS problem. - Abstract: Short-term hydro generation scheduling (STHGS) aims at determining optimal hydro generation scheduling to obtain minimum water consumption for one day or week while meeting various system constraints. In this paper, the STHGS problem is decomposed into two sub-problems: (i) unit commitment (UC) sub-problem; (ii) economic load dispatch (ELD) sub-problem. Then, we present a hybrid algorithm based on multi ant colony system (MACS) and differential evolution (DE) for solving the STHGS problem. First, MACS is used for dealing with UC sub-problem. A set of cooperating ant colonies cooperate to choose the unit state over the scheduled time horizon. Then, the adaptive differential evolution (ADE) is used to solve ELD sub-problem. MACS and ADE are run in parallel with adjusting their solutions in search of a better solution. Meanwhile, local and global pheromone updating rules in MACS and adaptive dynamic parameter adjusting strategy in DE are applied for enhancing the search ability of MACS-ADE. Finally, the proposed method is implemented to solve STHGS problem of Three Gorges–Gezhouba cascaded hydropower plants to verify the feasibility and effectiveness. Compared with other established methods, the simulation results reveal that the proposed MACS-ADE approach has the best convergence property, computational efficiency with less water consumption

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

    International Nuclear Information System (INIS)

    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)

  19. A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting

    Directory of Open Access Journals (Sweden)

    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

  20. Short-term forecasting of thunderstorms at Kennedy Space Center, based on the surface wind field

    Science.gov (United States)

    Watson, Andrew I.; Lopez, Raul E.; Holle, Ronald L.; Daugherty, John R.; Ortiz, Robert

    1989-01-01

    Techniques incorporating wind convergence that can be used for the short-term prediction of thunderstorm development are described. With these techniques, the convergence signal is sensed by the wind network array 15 to 90 min before actual storm development. Particular attention is given to the convergence cell technique (which has been applied at the Kennedy Space Center) where each convective region is analyzed independently. It is noted that, while the monitoring of areal and cellular convergence can be used to help locate the seeds of developing thunderstorms and pinpoint the lightning threat areas, this forecasting aid cannot be used in isolation.

  1. Short-term variability of Johor River discharge based on wavelet analysis

    Science.gov (United States)

    Ahmad, N.; Kamaruddin, S. A.; Heryansyah, A.

    2015-02-01

    River discharge provides a direct measure of water quantity and availability of water for specific uses. It also provides the basis for understanding river basin processes and is essential for interpreting and understanding river flow characteristics. This study investigates the temporal variability of river discharge records of Johor River. Wavelet analysis of discharge records for 30 years was carried out to characterize the river flow variability. Our results indicate that Johor River discharge data shows a significant short-term variability of between 0.6 to 2.5 years.

  2. A short-term spatio-temporal approach for Photovoltaic power forecasting

    NARCIS (Netherlands)

    Tascikaraoglu, A.; Sanandaji, B.M.; Chicco, G.; Cocina, V.; Spertino, F.; Erdinc, Ozan; Paterakis, N.G.; Catalão, J.P.S.

    2016-01-01

    This paper presents a Photovoltaic (PV) power conversion model and a forecasting approach which uses spatial dependency of variables along with their temporal information. The power produced by a PV plant is forecasted by a PV conversion model using the predictions of three weather variables,

  3. The European smoking prevention framework approach (ESFA): short-term effects.

    NARCIS (Netherlands)

    Vries, H. de; Mudde, A.; Kremers, S.; Wetzels, J.; Uiters, E.; Ariza, C.; Duarte Vitoria, P.; Fielder, A.; Holm, K.; Janssen, L.H.M.; Lehtuvuori, R.; Candel, M.

    2003-01-01

    The European Smoking Prevention Framework Approach (ESFA) resulted in a smoking prevention project for six European countries. It included activities on four levels: adolescents, schools, parents and out-of-school activities. Common goals and objectives were developed, but countries were also able

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

    Science.gov (United States)

    Kulhánek, Tomáš; Kofránek, Jiří; Mateják, Marek

    2014-11-01

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

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

    Science.gov (United States)

    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-based

  6. Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

    Directory of Open Access Journals (Sweden)

    Dongxiao Niu

    2015-01-01

    Full Text Available In order to realize the predicting and positioning of short-term load inflection point, this paper made reference to related research in the field of computer image recognition. It got a load sharp degree sequence by the transformation of the original load sequence based on the algorithm of sharp degree. Then this paper designed a forecasting model based on the chaos theory and RBF neural network. It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point. Finally, in the empirical example analysis, this paper predicted the daily load point of a region using the actual load data of the certain region to verify the effectiveness and applicability of this method. Prediction results showed that most of the test sample load points could be accurately predicted.

  7. A hybrid approach for short-term forecasting of wind speed.

    Science.gov (United States)

    Tatinati, Sivanagaraja; Veluvolu, Kalyana C

    2013-01-01

    We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.

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

    Science.gov (United States)

    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.

  9. Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

    Directory of Open Access Journals (Sweden)

    Juan Prieto

    2013-04-01

    Full Text Available Short-term load forecasting (STLF in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones.

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

    Directory of Open Access Journals (Sweden)

    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.

  11. Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation.

    Science.gov (United States)

    Jimeno Yepes, Antonio

    2017-09-01

    Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word. The type of features have an impact on machine learning methods, thus affect disambiguation performance. In this work, we have evaluated several types of features derived from the context of the ambiguous word and we have explored as well more global features derived from MEDLINE using word embeddings. Results show that word embeddings improve the performance of more traditional features and allow as well using recurrent neural network classifiers based on Long-Short Term Memory (LSTM) nodes. The combination of unigrams and word embeddings with an SVM sets a new state of the art performance with a macro accuracy of 95.97 in the MSH WSD data set. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    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 Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    Directory of Open Access Journals (Sweden)

    Hongshan Zhao

    2012-05-01

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

  14. Protein remote homology detection based on bidirectional long short-term memory.

    Science.gov (United States)

    Li, Shumin; Chen, Junjie; Liu, Bin

    2017-10-10

    Protein remote homology detection plays a vital role in studies of protein structures and functions. Almost all of the traditional machine leaning methods require fixed length features to represent the protein sequences. However, it is never an easy task to extract the discriminative features with limited knowledge of proteins. On the other hand, deep learning technique has demonstrated its advantage in automatically learning representations. It is worthwhile to explore the applications of deep learning techniques to the protein remote homology detection. In this study, we employ the Bidirectional Long Short-Term Memory (BLSTM) to learn effective features from pseudo proteins, also propose a predictor called ProDec-BLSTM: it includes input layer, bidirectional LSTM, time distributed dense layer and output layer. This neural network can automatically extract the discriminative features by using bidirectional LSTM and the time distributed dense layer. Experimental results on a widely-used benchmark dataset show that ProDec-BLSTM outperforms other related methods in terms of both the mean ROC and mean ROC50 scores. This promising result shows that ProDec-BLSTM is a useful tool for protein remote homology detection. Furthermore, the hidden patterns learnt by ProDec-BLSTM can be interpreted and visualized, and therefore, additional useful information can be obtained.

  15. Similarity-based distortion of visual short-term memory is due to perceptual averaging.

    Science.gov (United States)

    Dubé, Chad; Zhou, Feng; Kahana, Michael J; Sekuler, Robert

    2014-03-01

    A task-irrelevant stimulus can distort recall from visual short-term memory (VSTM). Specifically, reproduction of a task-relevant memory item is biased in the direction of the irrelevant memory item (Huang & Sekuler, 2010a). The present study addresses the hypothesis that such effects reflect the influence of neural averaging under conditions of uncertainty about the contents of VSTM (Alvarez, 2011; Ball & Sekuler, 1980). We manipulated subjects' attention to relevant and irrelevant study items whose similarity relationships were held constant, while varying how similar the study items were to a subsequent recognition probe. On each trial, subjects were shown one or two Gabor patches, followed by the probe; their task was to indicate whether the probe matched one of the study items. A brief cue told subjects which Gabor, first or second, would serve as that trial's target item. Critically, this cue appeared either before, between, or after the study items. A distributional analysis of the resulting mnemometric functions showed an inflation in probability density in the region spanning the spatial frequency of the average of the two memory items. This effect, due to an elevation in false alarms to probes matching the perceptual average, was diminished when cues were presented before both study items. These results suggest that (a) perceptual averages are computed obligatorily and (b) perceptual averages are relied upon to a greater extent when item representations are weakened. Implications of these results for theories of VSTM are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. An Untargeted Metabolomics Approach to Characterize Short-Term and Long-Term Metabolic Changes after Bariatric Surgery.

    Directory of Open Access Journals (Sweden)

    Sophie H Narath

    Full Text Available Bariatric surgery is currently one of the most effective treatments for obesity and leads to significant weight reduction, improved cardiovascular risk factors and overall survival in treated patients. To date, most studies focused on short-term effects of bariatric surgery on the metabolic profile and found high variation in the individual responses to surgery. The aim of this study was to identify relevant metabolic changes not only shortly after bariatric surgery (Roux-en-Y gastric bypass but also up to one year after the intervention by using untargeted metabolomics. 132 serum samples taken from 44 patients before surgery, after hospital discharge (1-3 weeks after surgery and at a 1-year follow-up during a prospective study (NCT01271062 performed at two study centers (Austria and Switzerland. The samples included 24 patients with type 2 diabetes at baseline, thereof 9 with diabetes remission after one year. The samples were analyzed by using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS, HILIC-QExactive. Raw data was processed with XCMS and drift-corrected through quantile regression based on quality controls. 177 relevant metabolic features were selected through Random Forests and univariate testing and 36 metabolites were identified. Identified metabolites included trimethylamine-N-oxide, alanine, phenylalanine and indoxyl-sulfate which are known markers for cardiovascular risk. In addition we found a significant decrease in alanine after one year in the group of patients with diabetes remission relative to non-remission. Our analysis highlights the importance of assessing multiple points in time in subjects undergoing bariatric surgery to enable the identification of biomarkers for treatment response, cardiovascular benefit and diabetes remission. Key-findings include different trend pattern over time for various metabolites and demonstrated that short term changes should not necessarily be used to identify

  17. Emotion based attentional priority for storage in visual short-term memory.

    Directory of Open Access Journals (Sweden)

    Luca Simione

    Full Text Available A plethora of research demonstrates that the processing of emotional faces is prioritised over non-emotive stimuli when cognitive resources are limited (this is known as 'emotional superiority'. However, there is debate as to whether competition for processing resources results in emotional superiority per se, or more specifically, threat superiority. Therefore, to investigate prioritisation of emotional stimuli for storage in visual short-term memory (VSTM, we devised an original VSTM report procedure using schematic (angry, happy, neutral faces in which processing competition was manipulated. In Experiment 1, display exposure time was manipulated to create competition between stimuli. Participants (n = 20 had to recall a probed stimulus from a set size of four under high (150 ms array exposure duration and low (400 ms array exposure duration perceptual processing competition. For the high competition condition (i.e. 150 ms exposure, results revealed an emotional superiority effect per se. In Experiment 2 (n = 20, we increased competition by manipulating set size (three versus five stimuli, whilst maintaining a constrained array exposure duration of 150 ms. Here, for the five-stimulus set size (i.e. maximal competition only threat superiority emerged. These findings demonstrate attentional prioritisation for storage in VSTM for emotional faces. We argue that task demands modulated the availability of processing resources and consequently the relative magnitude of the emotional/threat superiority effect, with only threatening stimuli prioritised for storage in VSTM under more demanding processing conditions. Our results are discussed in light of models and theories of visual selection, and not only combine the two strands of research (i.e. visual selection and emotion, but highlight a critical factor in the processing of emotional stimuli is availability of processing resources, which is further constrained by task demands.

  18. Laboratory and Field-Based Evaluation of Short-Term Effort with Maximal Intensity in Individuals with Intellectual Disabilities

    Directory of Open Access Journals (Sweden)

    Lencse-Mucha Judit

    2015-12-01

    Full Text Available Results of previous studies have not indicated clearly which tests should be used to assess short-term efforts of people with intellectual disabilities. Thus, the aim of the present study was to evaluate laboratory and field-based tests of short-term effort with maximal intensity of subjects with intellectual disabilities. Twenty four people with intellectual disability, who trained soccer, participated in this study. The 30 s Wingate test and additionally an 8 s test with maximum intensity were performed on a bicycle ergometer. The fatigue index, maximal and mean power, relative maximal and relative mean power were measured. Overall, nine field-based tests were conducted: 5, 10 and 20 m sprints, a 20 m shuttle run, a seated medicine ball throw, a bent arm hang test, a standing broad jump, sit-ups and a hand grip test. The reliability of the 30 s and 8 s Wingate tests for subjects with intellectual disability was confirmed. Significant correlation was observed for mean power between the 30 s and 8 s tests on the bicycle ergometer at a moderate level (r >0.4. Moreover, significant correlations were indicated between the results of laboratory tests and field tests, such as the 20 m sprint, the 20 m shuttle run, the standing long jump and the medicine ball throw. The strongest correlation was in the medicine ball throw. The 30 s Wingate test is a reliable test assessing maximal effort in subjects with intellectual disability. The results of this research confirmed that the 8 s test on a bicycle ergometer had a moderate correlation with the 30 s Wingate test in this population, thus, this comparison needs further investigation to examine alternativeness of the 8 s to 30 s Wingate tests. The non-laboratory tests could be used to indirectly assess performance in short-term efforts with maximal intensity.

  19. Laboratory and Field-Based Evaluation of Short-Term Effort with Maximal Intensity in Individuals with Intellectual Disabilities

    Science.gov (United States)

    Lencse-Mucha, Judit; Molik, Bartosz; Marszałek, Jolanta; Kaźmierska-Kowalewska, Kalina; Ogonowska-Słodownik, Anna

    2015-01-01

    Results of previous studies have not indicated clearly which tests should be used to assess short-term efforts of people with intellectual disabilities. Thus, the aim of the present study was to evaluate laboratory and field-based tests of short-term effort with maximal intensity of subjects with intellectual disabilities. Twenty four people with intellectual disability, who trained soccer, participated in this study. The 30 s Wingate test and additionally an 8 s test with maximum intensity were performed on a bicycle ergometer. The fatigue index, maximal and mean power, relative maximal and relative mean power were measured. Overall, nine field-based tests were conducted: 5, 10 and 20 m sprints, a 20 m shuttle run, a seated medicine ball throw, a bent arm hang test, a standing broad jump, sit-ups and a hand grip test. The reliability of the 30 s and 8 s Wingate tests for subjects with intellectual disability was confirmed. Significant correlation was observed for mean power between the 30 s and 8 s tests on the bicycle ergometer at a moderate level (r >0.4). Moreover, significant correlations were indicated between the results of laboratory tests and field tests, such as the 20 m sprint, the 20 m shuttle run, the standing long jump and the medicine ball throw. The strongest correlation was in the medicine ball throw. The 30 s Wingate test is a reliable test assessing maximal effort in subjects with intellectual disability. The results of this research confirmed that the 8 s test on a bicycle ergometer had a moderate correlation with the 30 s Wingate test in this population, thus, this comparison needs further investigation to examine alternativeness of the 8 s to 30 s Wingate tests. The non-laboratory tests could be used to indirectly assess performance in short-term efforts with maximal intensity. PMID:26834874

  20. Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term Prediction

    Directory of Open Access Journals (Sweden)

    Changbin Hu

    2015-02-01

    Full Text Available According to the topological structure of wind-storage-load complementation microgrids, this paper proposes a method for energy coordinative optimization which focuses on improvement of the economic benefits of microgrids in the prediction framework. First of all, the external characteristic mathematical model of distributed generation (DG units including wind turbines and storage batteries are established according to the requirements of the actual constraints. Meanwhile, using the minimum consumption costs from the external grid as the objective function, a grey prediction model with residual modification is introduced to output the predictive wind turbine power and load at specific periods. Second, based on the basic framework of receding horizon optimization, an intelligent genetic algorithm (GA is applied to figure out the optimum solution in the predictive horizon for the complex non-linear coordination control model of microgrids. The optimum results of the GA are compared with the receding solution of mixed integer linear programming (MILP. The obtained results show that the method is a viable approach for energy coordinative optimization of microgrid systems for energy flow and reasonable schedule. The effectiveness and feasibility of the proposed method is verified by examples.

  1. A Novel Short-Term Maintenance Strategy for Power Transmission and Transformation Equipment Based on Risk-Cost-Analysis

    Directory of Open Access Journals (Sweden)

    Hang Yang

    2017-11-01

    Full Text Available Current studies on preventive condition-based maintenance of power transmission and transformation equipment mainly focus on mid-term or long-term maintenance, and cannot meet the requirements of short-term especially temporary maintenance. In order to solve the defects of the present preventive maintenance strategies, according to the engineering application and based on risk-cost analysis, a short-term maintenance strategy is proposed in this manuscript. For the equipment working in bad health condition, its active maintenance costs and operation risk costs are evaluated, respectively. Then the latest maintenance time is calculated in accordance with the principle that its operation risk costs are no higher than active maintenance costs. Utilizing the latest maintenance time, the best maintenance time is calculated by setting the maximum relative earnings of postponing maintenance as the target, which provides the operation staffs with comprehensive maintenance-decision support. In the end, different cases on the IEEE 24-bus system are simulated. The effectiveness and advantages of the proposed strategy are demonstrated by the simulation results.

  2. Further Applications of Sector-Based Detection and Short-Term Clustering

    OpenAIRE

    Lathoud, Guillaume

    2006-01-01

    This paper presents an effective implementation of detection-localization of multiple speech sources with microphone arrays. In particular, the Scaled Conjugate Gradient descent is used for fast and precise localization, within a pre-detected volume of space. The approach is fit for real-time implementation. An unsupervised approach to speech/non-speech discrimination is also proposed. The integrated system is then successfully applied to segmentation of spontaneous multi-party speech, as fou...

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

    Directory of Open Access Journals (Sweden)

    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.

  4. Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

    Directory of Open Access Journals (Sweden)

    Jaime Lloret

    2013-08-01

    Full Text Available Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.

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

    Science.gov (United States)

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

    2014-09-01

    Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

    Science.gov (United States)

    He, Yaoyao; Yang, Shanlin; Xu, Qifa

    2013-07-01

    In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.

  7. Design-Based Research: Is This a Suitable Methodology for Short-Term Projects?

    Science.gov (United States)

    Pool, Jessica; Laubscher, Dorothy

    2016-01-01

    This article reports on a design-based methodology of a thesis in which a fully face-to-face contact module was converted into a blended learning course. The purpose of the article is to report on how design-based phases, in the form of micro-, meso- and macro-cycles were applied to improve practice and to generate design principles. Design-based…

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

    DEFF Research Database (Denmark)

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

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

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

    Science.gov (United States)

    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

  10. Short-Term Memory Scanning Viewed as Exemplar-Based Categorization

    Science.gov (United States)

    Nosofsky, Robert M.; Little, Daniel R.; Donkin, Christopher; Fific, Mario

    2011-01-01

    Exemplar-similarity models such as the exemplar-based random walk (EBRW) model (Nosofsky & Palmeri, 1997b) were designed to provide a formal account of multidimensional classification choice probabilities and response times (RTs). At the same time, a recurring theme has been to use exemplar models to account for old-new item recognition and to…

  11. Collaboration, Pedagogy, and Media: Short-Term Summer Programs Emphasize Project Based and Social Emotional Learning

    Science.gov (United States)

    Bowden, William R.

    2015-01-01

    Summer programs that experiment with combining media literacy and social-emotional learning can potentially affect students' academic performance. Based on a six-week program, working with rising eighth grade students in a low-income school district, this program allowed students to work on media projects while trying to develop stronger…

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

    International Nuclear Information System (INIS)

    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

  13. Short-term load and wind power forecasting using neural network-based prediction intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

  14. Subthreshold pharmacological and genetic approaches to analyzing CaV2.1-mediated NMDA receptor signaling in short-term memory.

    Science.gov (United States)

    Takahashi, Eiki; Niimi, Kimie; Itakura, Chitoshi

    2010-10-25

    Ca(V)2.1 is highly expressed in the nervous system and plays an essential role in the presynaptic modulation of neurotransmitter release machinery. Recently, the antiepileptic drug levetiracetam was reported to inhibit presynaptic Ca(V)2.1 functions, reducing glutamate release in the hippocampus, although the precise physiological role of Ca(V)2.1-regulated synaptic functions in cognitive performance at the system level remains unknown. This study examined whether Ca(V)2.1 mediates hippocampus-dependent spatial short-term memory using the object location and Y-maze tests, and perirhinal cortex-dependent nonspatial short-term memory using the object recognition test, via a combined pharmacological and genetic approach. Heterozygous rolling Nagoya (rol/+) mice carrying the Ca(V)2.1alpha(1) mutation had normal spatial and nonspatial short-term memory. A 100mg/kg dose of levetiracetam, which is ineffective in wild-type controls, blocked spatial short-term memory in rol/+ mice. At 5mg/kg, the N-methyl-D-aspartate (NMDA) receptor blocker (+/-)-3-(2-carboxypiperazin-4-yl)-propyl-1-phosphonic acid (CPP), which is ineffective in wild-type controls, also blocked the spatial short-term memory in rol/+ mice. Furthermore, a combination of subthreshold doses of levetiracetam (25 mg/kg) and CPP (2.5mg/kg) triggered a spatial short-term memory deficit in rol/+ mice, but not in wild-type controls. Similar patterns of nonspatial short-term memory were observed in wild-type and rol/+ mice when injected with levetiracetam (0-300 mg/kg). These results indicate that Ca(V)2.1-mediated NMDA receptor signaling is critical in hippocampus-dependent spatial short-term memory and differs in various regions. The combination subthreshold pharmacological and genetic approach presented here is easily performed and can be used to study functional signaling pathways in neuronal circuits. Copyright © 2010 Elsevier B.V. All rights reserved.

  15. Pointing towards visuospatial patterns in short-term memory: differential effects on familiarity- and recollection-based judgments.

    Science.gov (United States)

    Rossi-Arnaud, Clelia; Spataro, Pietro; Marques, Valeria R S; Longobardi, Emiddia

    2015-03-01

    Previous studies have indicated that pointing toward to-be-remembered visuospatial patterns enhances short-term memory (STM) when the presentation of pointing and no-pointing trials is mixed (Chum et al., 2007; Dodd & Shumborski, 2009; Rossi-Arnaud et al., 2012). By contrast, when presentation is blocked, pointing has inhibitory effects on memory (Dodd & Shumborski, 2009; Rossi-Arnaud et al., 2012). In the present study, we demonstrated that pointing has different effects on short-term recollection- and familiarity-based judgments, depending on the length of the visuospatial patterns (5- vs. 7-item arrays) and the interval between the encoding and test phases (2 vs. 5 s). More specifically, pointing decreased the accuracy of recollection-based judgments for 5-item arrays, but not for 7-item arrays (this negative effect did not interact with interval length). In contrast, pointing facilitated familiarity-based judgments when the interval between the study and test phases was 5 s, but not when it was 2 s (this positive effect did not interact with pattern length). We proposed that the negative effects might be accounted for by the simultaneous recruitment of attention resources in the planning and execution of pointing movements. As a consequence, executive resources are diverted from the primary memory task, resulting in a less efficient use of attention-demanding retrieval strategies, like chunking. By contrast, the positive effects on familiarity judgments might reflect the unitization of the to-be-remembered items into a single shape. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

  16. Short-term forecasting of lightning based on the surface wind field at Kennedy Space Center

    Science.gov (United States)

    Watson, Andrew I.; Lopez, Raul E.; Ortiz, Robert; Holle, Ronald L.

    1987-01-01

    Cloud-to-ground lightning is related in time and space to surface convergence for 244 days during the summer over a 790 sqkm network. The method uses surface convergence, particularly the average over the area, to identify the potential for new, local thunderstorm growth, and can be used to specify the likely time and location of lightning during the life cycle of the convection. A threshold of 0.0000075/sec change in divergence is used to define a convergence event, and a separation of 30 min between flashes defines a lightning event. Time intervals are found to be on the order of 1 hr from beginning convergence to first flash, and (CH110) 2 hr from beginning convergence to the end of lightning. Major differences between the convergence-lightning relationships based on low-level mean onshore and offshore flow are noted.

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

    International Nuclear Information System (INIS)

    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.

  18. Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

    Directory of Open Access Journals (Sweden)

    Yuancheng Li

    2016-11-01

    Full Text Available The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays. Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

  19. Wind characteristics on the Yucatan Peninsula based on short term data from meteorological stations

    International Nuclear Information System (INIS)

    Soler-Bientz, Rolando; Watson, Simon; Infield, David

    2010-01-01

    Due to the availability of sparsely populated and flat open terrain, the Yucatan Peninsula located in eastern Mexico is a promising region from the perspective of wind energy development. Study of the diurnal and seasonal wind resource is an important stage in the move towards commercial exploitation of wind power in this Latin American region. An analysis of the characteristics of the wind resource of the Yucatan Peninsula is presented in this paper, based on 10 min averaged wind speed data from nine meteorological stations, between 2000 and 2007. Hourly and monthly patterns of the main environmental parameters have been examined. Highly directional behaviour was identified that reflects the influence of winds coming from the Caribbean Sea and the Gulf of Mexico. The characteristics of the wind speed variation observed at the studied sites reflected their proximity to the coast and whether they were influenced by wind coming predominantly from over the land or predominantly from over the sea. The atmospheric stability over the eastern seas of the Yucatan Peninsula was also analysed to assess thermal effects for different wind directions. The findings were consistent with the variation in average wind speeds observed at the coastal sites where winds came predominantly from over the sea. The research presented here is to be used as a basis for a wind atlas for the Yucatan Peninsula.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

    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.

  2. [A method to estimate the short-term fractal dimension of heart rate variability based on wavelet transform].

    Science.gov (United States)

    Zhonggang, Liang; Hong, Yan

    2006-10-01

    A new method of calculating fractal dimension of short-term heart rate variability signals is presented. The method is based on wavelet transform and filter banks. The implementation of the method is: First of all we pick-up the fractal component from HRV signals using wavelet transform. Next, we estimate the power spectrum distribution of fractal component using auto-regressive model, and we estimate parameter 7 using the least square method. Finally according to formula D = 2- (gamma-1)/2 estimate fractal dimension of HRV signal. To validate the stability and reliability of the proposed method, using fractional brown movement simulate 24 fractal signals that fractal value is 1.6 to validate, the result shows that the method has stability and reliability.

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

    Directory of Open Access Journals (Sweden)

    Jiancang Zhuang

    2012-07-01

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

  4. Endolymphatic Thoracic Duct Stent-Graft Reconstruction for Chylothorax: Approach, Technical Success, Safety, and Short-term Outcomes.

    Science.gov (United States)

    Srinivasa, Rajiv N; Chick, Jeffrey Forris Beecham; Hage, Anthony N; Gemmete, Joseph J; Murrey, Douglas C; Srinivasa, Ravi N

    2018-04-01

    To report approach, technical success, safety, and short-term outcomes of thoracic duct stent-graft reconstruction for the treatment of chylothorax. Two patients, 1 (50%) male and 1 (50%) female, with mean age of 38 years (range: 16-59 years) underwent endolymphatic thoracic duct stent-graft reconstruction between September 2016 and July 2017. Patients had radiographic left-sided chylothoraces (n = 2) from idiopathic causes (n = 1) and heart transplantation (n = 1). In both (100%) patients, antegrade lymphatic access was used to opacify the thoracic duct after which retrograde access was used for thoracic duct stent-graft placement. Pelvic lymphangiography technical success, antegrade cisterna chyli cannulation technical success, thoracic duct opacification technical success, retrograde thoracic duct access technical success, thoracic duct stent-graft reconstruction technical success, ethiodized oil volume, contrast volume, estimated blood loss, procedure time, fluoroscopy time, radiation dose, clinical success, complications, deaths, and follow-up were recorded. Pelvic lymphangiography, antegrade cisterna chyli cannulation, thoracic duct opacification, retrograde thoracic duct access, and thoracic duct stent-graft reconstruction were technically successful in both (100%) patients. Mean ethiodized oil volume was 8 mL (range: 5-10 mL). Mean contrast volume was 13 mL (range: 5-20 mL). Mean estimated blood loss was 13 mL (range: 10-15 mL). Mean fluoroscopy time was 50.4 min (range: 31.2-69.7 min). Mean dose area product and reference air kerma were 954.4 μGmy 2 (range: 701-1,208 μGmy 2 ) and 83.5 mGy (range: 59-108 mGy), respectively. Chylothorax resolved in both (100%) patients. There were no minor or major complications directly related to the procedure. Thoracic duct stent-graft reconstruction may be a technically successful and safe alternative to thoracic duct embolization, disruption, and surgical ligation for the treatment of chylothorax

  5. A Neuro-genetic Based Short-term Forecasting Framework for Network Intrusion Prediction System

    Institute of Scientific and Technical Information of China (English)

    Siva S. Sivatha Sindhu; S. Geetha; M. Marikannan; A. Kannan

    2009-01-01

    work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.

  6. A Short Term Analogue Memory

    DEFF Research Database (Denmark)

    Shah, Peter Jivan

    1992-01-01

    A short term analogue memory is described. It is based on a well-known sample-hold topology in which leakage currents have been minimized partly by circuit design and partly by layout techniques. Measurements on a test chip implemented in a standard 2.4 micron analogue CMOS process show a droop...

  7. Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk

    Science.gov (United States)

    Li, Yane; Fan, Ming; Cheng, Hu; Zhang, Peng; Zheng, Bin; Li, Lihua

    2018-01-01

    This study aims to develop and test a new imaging marker-based short-term breast cancer risk prediction model. An age-matched dataset of 566 screening mammography cases was used. All ‘prior’ images acquired in the two screening series were negative, while in the ‘current’ screening images, 283 cases were positive for cancer and 283 cases remained negative. For each case, two bilateral cranio-caudal view mammograms acquired from the ‘prior’ negative screenings were selected and processed by a computer-aided image processing scheme, which segmented the entire breast area into nine strip-based local regions, extracted the element regions using difference of Gaussian filters, and computed both global- and local-based bilateral asymmetrical image features. An initial feature pool included 190 features related to the spatial distribution and structural similarity of grayscale values, as well as of the magnitude and phase responses of multidirectional Gabor filters. Next, a short-term breast cancer risk prediction model based on a generalized linear model was built using an embedded stepwise regression analysis method to select features and a leave-one-case-out cross-validation method to predict the likelihood of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) values significantly increased from 0.5863  ±  0.0237 to 0.6870  ±  0.0220 when the model trained by the image features extracted from the global regions and by the features extracted from both the global and the matched local regions (p  =  0.0001). The odds ratio values monotonically increased from 1.00-8.11 with a significantly increasing trend in slope (p  =  0.0028) as the model-generated risk score increased. In addition, the AUC values were 0.6555  ±  0.0437, 0.6958  ±  0.0290, and 0.7054  ±  0.0529 for the three age groups of 37

  8. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-09-01

    Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

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

    Directory of Open Access Journals (Sweden)

    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.

  10. Short-term water-based aerobic training promotes improvements in aerobic conditioning parameters of mature women.

    Science.gov (United States)

    Costa, Rochelle Rocha; Reichert, Thais; Coconcelli, Leandro; Simmer, Nicole Monticelli; Bagatini, Natália Carvalho; Buttelli, Adriana Cristine Koch; Bracht, Cláudia Gomes; Stein, Ricardo; Kruel, Luiz Fernando Martins

    2017-08-01

    Aging is accompanied by a decrease in aerobic capacity. Therefore, physical training has been recommended to soften the effects of advancement age. The aim of this study was to assess the effects of a short-term water-based aerobic training on resting heart rate (HR rest ), heart rate corresponding to anaerobic threshold (HR AT ), peak heart rate (HR peak ), percentage value of HR AT in relation to HR peak and test duration (TD) of mature women. Twenty-two women (65.91 ± 4.83 years) were submitted to a five-week water-based interval aerobic training. Aerobic capacity parameters were evaluated through an aquatic incremental test. After training, there was an increase in TD (16%) and HR AT percentage in relation to HR peak (4.68%), and a reduction of HR rest (9%). It is concluded that a water-based aerobic interval training prescribed through HR AT of only five weeks is able to promote improvements in aerobic capacity of mature women. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Short-term retention of visual information: Evidence in support of feature-based attention as an underlying mechanism.

    Science.gov (United States)

    Sneve, Markus H; Sreenivasan, Kartik K; Alnæs, Dag; Endestad, Tor; Magnussen, Svein

    2015-01-01

    Retention of features in visual short-term memory (VSTM) involves maintenance of sensory traces in early visual cortex. However, the mechanism through which this is accomplished is not known. Here, we formulate specific hypotheses derived from studies on feature-based attention to test the prediction that visual cortex is recruited by attentional mechanisms during VSTM of low-level features. Functional magnetic resonance imaging (fMRI) of human visual areas revealed that neural populations coding for task-irrelevant feature information are suppressed during maintenance of detailed spatial frequency memory representations. The narrow spectral extent of this suppression agrees well with known effects of feature-based attention. Additionally, analyses of effective connectivity during maintenance between retinotopic areas in visual cortex show that the observed highlighting of task-relevant parts of the feature spectrum originates in V4, a visual area strongly connected with higher-level control regions and known to convey top-down influence to earlier visual areas during attentional tasks. In line with this property of V4 during attentional operations, we demonstrate that modulations of earlier visual areas during memory maintenance have behavioral consequences, and that these modulations are a result of influences from V4. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Chen Wang

    2016-01-01

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

  13. Mechanism for optimization of signal-to-noise ratio of dopamine release based on short-term bidirectional plasticity.

    Science.gov (United States)

    Da Cunha, Claudio; McKimm, Eric; Da Cunha, Rafael M; Boschen, Suelen L; Redgrave, Peter; Blaha, Charles D

    2017-07-15

    Repeated electrical stimulation of dopamine (dopamine) fibers can cause variable effects on further dopamine release; sometimes there are short-term decreases while in other cases short-term increases have been reported. Previous studies have failed to discover what factors determine in which way dopamine neurons will respond to repeated stimulation. The aim of the present study was therefore to investigate what determines the direction and magnitude of this particular form of short-term plasticity. Fixed potential amperometry was used to measure dopamine release in the nucleus accumbens in response to two trains of electrical pulses administered to the ventral tegmental area of anesthetized mice. When the pulse trains were of equal magnitude we found that low magnitude stimulation was associated with short-term suppression and high magnitude stimulation with short-term facilitation of dopamine release. Secondly, we found that the magnitude of the second pulse train was critical for determining the sign of the plasticity (suppression or facilitation), while the magnitude of the first pulse train determined the extent to which the response to the second train was suppressed or facilitated. This form of bidirectional plasticity might provide a mechanism to enhance signal-to-noise ratio of dopamine neurotransmission. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. The Safety Tips for ATV Riders (STARs) programme: short-term impact of a school-based educational intervention.

    Science.gov (United States)

    Jennissen, Charles A; Peck, Jeffrey; Wetjen, Kristel; Hoogerwerf, Pam; Harland, Karisa K; Denning, Gerene M

    2015-06-01

    Since 1985, one-third of all US all-terrain vehicle (ATV)-related injuries and one-quarter of deaths involved victims safety education of youth could help reduce these tragedies. To assess the efficacy of the Safety Tips for ATV Riders (STARs) school-based programme targeting adolescents. A survey was anonymously administered before and after the programme to determine demographics, knowledge and reported likelihood of using the information learned. Over 4600 students in 30 Iowa schools participated from November 2010 to April 2013. Initially, 52% knew most ATVs are designed for one rider, 25% knew the recommended vehicle size for their age range and 42% knew riding on Iowa's roads was legal only for agricultural purposes. After the programme, this increased to 92%, 82% and 76%, respectively (psafety information learned, respectively; younger students, females and infrequent riders reported higher likelihoods. STARs increased short-term ATV safety knowledge and almost half the participants reported they would use the safety information presented. Males and frequent riders seemed more resistant, but some groups that may be more vulnerable to potential ATV crash and injury appeared amenable to the training with higher increases in postprogramme scores and greater intention of improving safety behaviours. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  15. Short-term impact of deep sand extraction and ecosystem-based landscaping on macrozoobenthos and sediment characteristics.

    Science.gov (United States)

    de Jong, Maarten F; Baptist, Martin J; Lindeboom, Han J; Hoekstra, Piet

    2015-08-15

    We studied short-term changes in macrozoobenthos in a 20m deep borrow pit. A boxcorer was used to sample macrobenthic infauna and a bottom sledge was used to sample macrobenthic epifauna. Sediment characteristics were determined from the boxcore samples, bed shear stress and near-bed salinity were estimated with a hydrodynamic model. Two years after the cessation of sand extraction, macrozoobenthic biomass increased fivefold in the deepest areas. Species composition changed significantly and white furrow shell (Abra alba) became abundant. Several sediment characteristics also changed significantly in the deepest parts. Macrozoobenthic species composition and biomass significantly correlated with time after cessation of sand extraction, sediment and hydrographical characteristics. Ecosystem-based landscaped sand bars were found to be effective in influencing sediment characteristics and macrozoobenthic assemblage. Significant changes in epifauna occurred in deepest parts in 2012 which coincided with the highest sedimentation rate. We recommend continuing monitoring to investigate medium and long-term impacts. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Incidence and Short-term Mortality From Perforated Peptic Ulcer in Korea: A Population-Based Study

    Science.gov (United States)

    Bae, SeungJin; Shim, Ki-Nam; Kim, Nayoung; Kang, Jung Mook; Kim, Dong-Sook; Kim, Kyoung-Min; Cho, Yu Kyung; Jung, Sung Woo

    2012-01-01

    Background Perforated peptic ulcer (PPU) is associated with serious health and economic outcomes. However, few studies have estimated the incidence and health outcomes of PPU using a nationally representative sample in Asia. We estimated age- and sex-specific incidence and short-term mortality from PPU among Koreans and investigated the risk factors for mortality associated with PPU development. Methods A retrospective population-based study was conducted from 2006 through 2007 using the Korean National Health Insurance claims database. A diagnostic algorithm was derived and validated to identify PPU patients, and PPU incidence rates and 30-day mortality rates were determined. Results From 2006 through 2007, the PPU incidence rate per 100 000 population was 4.4; incidence among men (7.53) was approximately 6 times that among women (1.24). Incidence significantly increased with advanced age, especially among women older than 50 years. Among 4258 PPU patients, 135 (3.15%) died within 30 days of the PPU event. The 30-day mortality rate increased with advanced age and reached almost 20% for patients older than 80 years. The 30-day mortality rate was 10% for women and 2% for men. Older age, being female, and higher comorbidity were independently associated with 30-day mortality rate among PPU patients in Korea. Conclusions Special attention should be paid to elderly women with high comorbidity who develop PPU. PMID:22955110

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

    International Nuclear Information System (INIS)

    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.

  18. Predictors of the short-term responder rate of Electroconvulsive therapy in depressive disorders - a population based study

    Directory of Open Access Journals (Sweden)

    Nordenskjöld Axel

    2012-08-01

    Full Text Available Abstract Background The aim of the present study is to investigate the responder rate of Electroconvulsive therapy, ECT, in clinical routine work and to define clinical characteristics predictive of response to ECT. The main hypothesis is that the responder rate of ECT might be lower in clinical routine than in controlled trials. Methods This is a population-based study of all patients (N = 990 treated with ECT for depressive disorders, between 2008–2010 in eight hospitals in Sweden. Patients with Clinical Global Impression-Improvement scores of 1 or 2 (much improved within one week after ECT were considered responders to ECT. The predictive values of single clinical variables were tested by means of chi-squared tests and the relative importance was tested in a logistic regression analysis. Results The responder rate was 80.1%. A higher proportion of older patients (>50 years responded (84.3% vs. 74.2%, p  Conclusions This study focuses exclusively on the short term responder rate with ECT in clinical practice. Similarly to results from controlled trials a high responder rate is reported. Older patients, more severely ill patients, psychotically ill patients and patients without personality disorders had the highest responder rates. Inpatients may have better outcome with ECT than outpatients.

  19. Short-term memory

    Science.gov (United States)

    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.

  20. Emotion-based decision-making in healthy subjects: short-term effects of reducing dopamine levels.

    Science.gov (United States)

    Sevy, Serge; Hassoun, Youssef; Bechara, Antoine; Yechiam, Eldad; Napolitano, Barbara; Burdick, Katherine; Delman, Howard; Malhotra, Anil

    2006-10-01

    Converging evidences from animal and human studies suggest that addiction is associated with dopaminergic dysfunction in brain reward circuits. So far, it is unclear what aspects of addictive behaviors are related to a dopaminergic dysfunction. We hypothesize that a decrease in dopaminergic activity impairs emotion-based decision-making. To demonstrate this hypothesis, we investigated the effects of a decrease in dopaminergic activity on the performance of an emotion-based decision-making task, the Iowa gambling task (IGT), in 11 healthy human subjects. We used a double-blind, placebo-controlled, within-subject design to examine the effect of a mixture containing the branched-chain amino acids (BCAA) valine, isoleucine and leucine on prolactin, IGT performance, perceptual competency and visual aspects of visuospatial working memory, visual attention and working memory, and verbal memory. The expectancy-valence model was used to determine the relative contributions of distinct IGT components (attention to past outcomes, relative weight of wins and losses, and choice strategies) in the decision-making process. Compared to placebo, the BCAA mixture increased prolactin levels and impaired IGT performance. BCAA administration interfered with a particular component process of decision-making related to attention to more recent events as compared to more distant events. There were no differences between placebo and BCAA conditions for other aspects of cognition. Our results suggest a direct link between a reduced dopaminergic activity and poor emotion-based decision-making characterized by shortsightedness, and thus difficulties resisting short-term reward, despite long-term negative consequences. These findings have implications for behavioral and pharmacological interventions targeting impaired emotion-based decision-making in addictive disorders.

  1. Validation of PC-based Sound Card with Biopac for Digitalization of ECG Recording in Short-term HRV Analysis.

    Science.gov (United States)

    Maheshkumar, K; Dilara, K; Maruthy, K N; Sundareswaren, L

    2016-07-01

    Heart rate variability (HRV) analysis is a simple and noninvasive technique capable of assessing autonomic nervous system modulation on heart rate (HR) in healthy as well as disease conditions. The aim of the present study was to compare (validate) the HRV using a temporal series of electrocardiograms (ECG) obtained by simple analog amplifier with PC-based sound card (audacity) and Biopac MP36 module. Based on the inclusion criteria, 120 healthy participants, including 72 males and 48 females, participated in the present study. Following standard protocol, 5-min ECG was recorded after 10 min of supine rest by Portable simple analog amplifier PC-based sound card as well as by Biopac module with surface electrodes in Leads II position simultaneously. All the ECG data was visually screened and was found to be free of ectopic beats and noise. RR intervals from both ECG recordings were analyzed separately in Kubios software. Short-term HRV indexes in both time and frequency domain were used. The unpaired Student's t-test and Pearson correlation coefficient test were used for the analysis using the R statistical software. No statistically significant differences were observed when comparing the values analyzed by means of the two devices for HRV. Correlation analysis revealed perfect positive correlation (r = 0.99, P < 0.001) between the values in time and frequency domain obtained by the devices. On the basis of the results of the present study, we suggest that the calculation of HRV values in the time and frequency domains by RR series obtained from the PC-based sound card is probably as reliable as those obtained by the gold standard Biopac MP36.

  2. A Time Series Regime Classification Approach for Short-Term Forecasting; Identificacion de Mecanismos en Series Temporales para la Prediccion a Corto Plazo

    Energy Technology Data Exchange (ETDEWEB)

    Gallego, C. J.

    2010-03-08

    Abstract: This technical report is focused on the analysis of stochastic processes that switch between different dynamics (also called regimes or mechanisms) over time. The so-called Switching-regime models consider several underlying functions instead of one. In this case, a classification problem arises as the current regime has to be assessed at each time-step. The identification of the regimes allows the performance of regime-switching models for short-term forecasting purposes. Within this framework, identifying different regimes showed by time-series is the aim of this work. The proposed approach is based on a statistical tool called Gamma-test. One of the main advantages of this methodology is the absence of a mathematical definition for the different underlying functions. Applications with both simulated and real wind power data have been considered. Results on simulated time series show that regimes can be successfully identified under certain hypothesis. Nevertheless, this work highlights that further research has to be done when considering real wind power time-series, which usually show different behaviours (e.g. fluctuations or ramps, followed by low variance periods). A better understanding of these events eventually will improve wind power forecasting. (Author) 15 refs.

  3. An approach estimating the short-term effect of NO2 on daily mortality in Spanish cities.

    Science.gov (United States)

    Linares, Cristina; Falcón, Isabel; Ortiz, Cristina; Díaz, Julio

    2018-04-07

    Road traffic is the most significant source of urban air pollution. PM 2.5 is the air pollutant whose health effects have been most closely studied, and is the variable most commonly used as a proxy indicator of exposure to air pollution, whereas evidence on NO 2 concentrations per se is still under study. In the case of Spain, there are no specific updated studies which calculate short-term NO 2 -related mortality. To quantify the relative risks (RRs) and attributable risks (ARs) of daily mortality associated with NO 2 concentrations recorded in Spain across the study period, 2000-2009; and to calculate the number of NO 2 -related deaths. We calculated daily mortality due to natural causes (ICD-10: A00 R99), circulatory causes (ICD-10: I00 I99) and respiratory causes (ICD-10: J00 J99) for each province across the period 2000-2009, using data supplied by the National Statistics Institute. Mean daily NO 2 concentrations in μg/m 3 for each provincial capital were furnished by the Ministry of Agriculture & Environment, along with the equivalent figures for the control pollutants (PM 10 ). To estimate RRs and ARs, we used generalised linear models with a Poisson link, controlling for maximum and minimum daily temperature, trend of the series, seasonalities, and the autoregressive nature of the series. A meta-analysis with random effects was used to estimate RRs and ARs nationwide. The overall RRs obtained for Spain, corresponding to increases of 10 μg/m 3 in NO 2 concentrations were 1.012 (95% CI: 1.010 1.014) for natural-cause mortality, 1.028 (95% CI: 1.019 1.037) for respiratory-cause mortality, and 1.016 (95% CI: 1.012 1.021) for circulatory-cause mortality. This amounted to an annual overall 6085 deaths (95% CI: 3288 9427) due to natural causes, 1031 (95% CI: 466 1585) due to respiratory causes, and 1978 (95% CI: 828 3197) due to circulatory causes. By virtue of the number of cities involved and the nature of the analysis performed, with quantification of the

  4. A Column-Generation Approach for a Short-Term Production Planning Problem in Closed-Loop Supply Chains

    Directory of Open Access Journals (Sweden)

    Florian Sahling

    2013-05-01

    Full Text Available We present a new model formulation for a multi-product lot-sizing problem with product returns and remanufacturing subject to a capacity constraint. The given external demand of the products has to be satisfied by remanufactured or newly produced goods. The objective is to determine a feasible production plan, which minimizes production, holding, and setup costs. As the LP relaxation of a model formulation based on the well-known CLSP leads to very poor lower bounds, we propose a column-generation approach to determine tighter bounds. The lower bound obtained by column generation can be easily transferred into a feasible solution by a truncated branch-and-bound approach using CPLEX. The results of an extensive numerical study show the high solution quality of the proposed solution approach.

  5. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM which is optimized by fruit fly algorithm (FOA for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.

  6. The Demonstration of Short-Term Consolidation.

    Science.gov (United States)

    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)

  7. Gradient decent based multi-objective cultural differential evolution for short-term hydrothermal optimal scheduling of economic emission with integrating wind power and photovoltaic power

    International Nuclear Information System (INIS)

    Zhang, Huifeng; Yue, Dong; Xie, Xiangpeng; Dou, Chunxia; Sun, Feng

    2017-01-01

    With the integration of wind power and photovoltaic power, optimal operation of hydrothermal power system becomes great challenge due to its non-convex, stochastic and complex-coupled constrained characteristics. This paper extends short-term hydrothermal system optimal model into short-term hydrothermal optimal scheduling of economic emission while considering integrated intermittent energy resources (SHOSEE-IIER). For properly solving SHOSEE-IIER problem, a gradient decent based multi-objective cultural differential evolution (GD-MOCDE) is proposed to improve the optimal efficiency of SHOSEE-IIER combined with three designed knowledge structures, which mainly enhances search ability of differential evolution in the shortest way. With considering those complex-coupled and stochastic constraints, a heuristic constraint-handling measurement is utilized to tackle with them both in coarse and fine tuning way, and probability constraint-handling procedures are taken to properly handle those stochastic constraints combined with their probability density functions. Ultimately, those approaches are implemented on five test systems, which testify the optimization efficiency of proposed GD-MOCDE and constraint-handling efficiency for system load balance, water balance and stochastic constraint-handling measurements, those obtained results reveal that the proposed GD-MOCDE can properly solve the SHOSEE-IIER problem combined with those constraint-handling approaches. - Highlights: • Gradient decent method is proposed to improve mutation operator. • Hydrothermal system is extended to hybrid energy system. • The uncertainty constraint is converted into deterministic constraint. • The results show the viability and efficiency of proposed algorithm.

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

    Science.gov (United States)

    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.

  9. Short-term impact of deep sand extraction and ecosystem-based landscaping on macrozoobenthos and sediment characteristics

    NARCIS (Netherlands)

    de Jong, Maarten F.; Baptist, Martin J.; Lindeboom, Han J.; Hoekstra, Piet

    2015-01-01

    We studied short-term changes in macrozoobenthos in a 20. m deep borrow pit. A boxcorer was used to sample macrobenthic infauna and a bottom sledge was used to sample macrobenthic epifauna. Sediment characteristics were determined from the boxcore samples, bed shear stress and near-bed salinity were

  10. New Approaches for Very Short-term Steady-State Analysis of An Electrical Distribution System with Wind Farms

    Directory of Open Access Journals (Sweden)

    Antonio Bracale

    2010-04-01

    Full Text Available Distribution networks are undergoing radical changes due to the high level of penetration of dispersed generation. Dispersed generation systems require particular attention due to their incorporation of uncertain energy sources, such as wind farms, and due to the impacts that such sources have on the planning and operation of distribution networks. In particular, the foreseeable, extensive use of wind turbine generator units in the future requires that distribution system engineers properly account for their impacts on the system. Many new technical considerations must be addressed, including protection coordination, steady-state analysis, and power quality issues. This paper deals with the very short-term, steady-state analysis of a distribution system with wind farms, for which the time horizon of interest ranges from one hour to a few hours ahead. Several wind-forecasting methods are presented in order to obtain reliable input data for the steady-state analysis. Both deterministic and probabilistic methods were considered and used in performing deterministic and probabilistic load-flow analyses. Numerical applications on a 17-bus, medium-voltage, electrical distribution system with various wind farms connected at different busbars are presented and discussed.

  11. Short term hydroelectric power system scheduling with wind turbine generators using the multi-pass iteration particle swarm optimization approach

    International Nuclear Information System (INIS)

    Lee, T.-Y.

    2008-01-01

    This paper uses multi-pass iteration particle swarm optimization (MIPSO) to solve short term hydroelectric generation scheduling of a power system with wind turbine generators. MIPSO is a new algorithm for solving nonlinear optimal scheduling problems. A new index called iteration best (IB) is incorporated into particle swarm optimization (PSO) to improve solution quality. The concept of multi-pass dynamic programming is applied to modify PSO further and improve computation efficiency. The feasible operational regions of the hydro units and pumped storage plants over the whole scheduling time range must be determined before applying MIPSO to the problem. Wind turbine power generation then shaves the power system load curves. Next, MIPSO calculates hydroelectric generation scheduling. It begins with a coarse time stage and searching space and refines the time interval between two time stages and the search spacing pass by pass (iteration). With the cooperation of agents called particles, the near optimal solution of the scheduling problem can be effectively reached. The effects of wind speed uncertainty were also considered in this paper. The feasibility of the new algorithm is demonstrated by a numerical example, and MIPSO solution quality and computation efficiency are compared to those of other algorithms

  12. Two-dimensional knowledge-based volumetric reconstruction of the right ventricle documents short-term improvement in pulmonary hypertension.

    Science.gov (United States)

    Schwaiger, Johannes P; Knight, Daniel S; Kaier, Thomas; Gallimore, Adele; Denton, Christopher P; Schreiber, Benjamin E; Handler, Clive; Coghlan, John G

    2017-06-01

    Data are scarce about short-term right ventricular changes in pulmonary hypertension. Two-dimensional knowledge-based reconstruction of the right ventricle with 2D echocardiography (2DKBR) has been shown to be a valid alternative to Cardiac MRI. In this longitudinal study 25 pulmonary hypertension patients underwent 2DKBR of the right ventricle, assessment of NT-proBNP levels and functional class at baseline and after a mean follow-up of 6.1 months. Patients were followed up clinically for a further mean of 8.2 months. The majority of patients had connective tissue disease (CTD) associated pulmonary arterial hypertension (n=15) or chronic thromboembolic pulmonary hypertension (CTEPH; n=6). A total of 15 patients underwent an intervention, either new targeted therapy, escalation of targeted therapy or pulmonary endarterectomy. A total of 10 clinically stable patients were routinely followed up without any change in therapy. There were significant improvements in the right ventricular end-diastolic volume index (111±29 mL/m² vs 100±36 mL/m²; P=.038), end-systolic volume index (72±23 mL/m² vs 61±25 mL/m²; P=.001), and ejection fraction (35±10% vs 40±9%; P=.030). Changes in NT-proBNP levels correlated strongest with changes in end-systolic volume index (r=-.77; P=right ventricle was associated with clinical worsening. In a CTD and CTEPH dominated patient population significant reverse remodeling and improvement of ejection fraction occurred despite a short follow-up and was paralleled by significant changes in NT-proBNP levels. Further right ventricular dilatation was associated with worse clinical outcome. 2DKBR is a feasible substitute for Cardiac MRI to follow-up right ventricular indices in pulmonary hypertension. © 2017, Wiley Periodicals, Inc.

  13. Short term protein supplementation during a long interval prostaglandin-based protocol for timed AI in sheep.

    Science.gov (United States)

    Errandonea, N; Fierro, S; Viñoles, C; Gil, J; Banchero, G; Olivera-Muzante, J

    2018-03-21

    The aim of this study was to evaluate the reproductive impact of a short-term protein supplementation on a long interval prostaglandin-based protocol (two PG injections 15 d apart; PG15) for timed artificial insemination in sheep. During the breeding season, 437 multiparous Merino ewes grazing native pastures (forage allowance of 6 kg of dry matter/100 kg of live weight; crude protein: 10.8%, metabolic energy: 2.1 Mcal/kg of dry matter) were selected. Ewes were allocated, according to body condition score (3.2 ± 0.2) and body weight (40.6 ± 4.9 kg, mean ± SD), to a 2 × 2 factorial design: type of estrus -spontaneous estrus (SE) or induced with PG15 (PG15)-, and supplementation (yes or no) before insemination (+FF; soybean meal at Days -10 to -3; crude protein: 51.9%, metabolic energy: 2.8 Mcal/kg of dry matter; average consumption 0.9% live weight/ewe/day of dry matter). All ewes were cervically artificial inseminated (Day -2 to -3 in SE ewes at estrus detection; Day 0 = timed artificial insemination in PG15 ewes). Ovulation rate on Day 7, non-return to service on Day 23, conception, fertility, prolificacy and fecundity on Day 60 were evaluated. Ovulation rate (1.17 ± 0.40 vs. 1.06 ± 0.25), non-return to service at Day 23 (81.7 vs. 64.2%), conception (78.8 vs. 61.5%), fertility (75.2 vs. 61.5%) and fecundity (0.77 vs. 0.62) were higher in ewes from SE than PG15 group (P  0.05). Protein supplementation increased ovulation rate (1.30 ± 0.45 vs. 1.17 ± 0.40), prolificacy (1.18 ± 0.39 vs. 1.02 ± 0.16) and fecundity (0.94 vs. 0.77%; P conception (82.9 vs. 78.8%) or fertility (79.1 vs. 75.2%; P > 0.05) in SE group. The supplement feed to PG15 ewes increased ovulation rate (1.35 ± 0.45 vs. 1.06 ± 0.25), prolificacy (1.25 ± 0.43 vs. 1.01 ± 0.12) and fecundity (0.79 vs. 0.62%; P  0.05). The magnitude of the increase in ovulation rate in PG15 was greater than in the SE group (27 vs. 11%; P conception (63.3 vs 61

  14. Effect of Short Term NaCl Stress on Cultivars of S. lycopersicum: A Comparative Biochemical Approach

    Directory of Open Access Journals (Sweden)

    Chaitali Roy

    2014-03-01

    Full Text Available Tomato is a crop plant with high fruit nutritive value and other useful properties. The cultivation of this species is dependent on many environmental factors, e.g. temperature, salinity, nutrients etc, affecting the yield and reproductive potential of the plant. Salinity in soil or water is of increasing importance to agriculture because it causes stress to crop plants. Plants exposed to an excess amount of salts such as NaCl undergo osmotic stress, water deficit and ionic imbalances and can increase production of reactive oxygen species(ROS. Higher plants possess very efficient enzymatic and non-enzymatic antioxidative defense mechanisms that allow the scavenging of ROS and protection of cellular components from oxidative damage. Studies were conducted to investigate the effect of short term salinity stress on some physiological alterations in three tomato cultivars Pusa Ruby(PR, Punjab Keshari (PK and Ailsa Craig(AC. Some biochemical parameters (anthocyanin and carotenoeid content, polyamines, proline, cysteine, peroxidase and malondialdehyde were set and applied at two month old stage of tomato plants. Three tomato cultivars were grown in 0.5xMS for 2 months and at this stage, they were treated with 0 and 200mM NaCl for a short period of six hours in hydroponic conditions. The genotypes exhibited different responses in terms of different osmoprotectant, antioxidant, and pigment level. The relationships among the salinity and accumulation of these compounds in leaf were then determined. It was concluded that, tomato cultivars under study responded differently showing their sensitivity or tolerance to salinity stress. Among three cultivars PK appeared to be more tolerant genotype than the other two cultivars PR and AC. PK could rapidly evolve physiological and antioxidant mechanisms to adapt to salt and manage the oxidative stress. The research was conducted in a completely randomized design with three replications.

  15. Aging of Dissolved Copper and Copper-based Nanoparticles in Five Different Soils: Short-term Kinetics vs. Long-term Fate

    Science.gov (United States)

    With the growing availability and use of copper-based nanomaterials (Cu-NMs), there is increasing concern regarding their release and potential impact on the environment. In this study, the short term (≤5 d) aging profile and the long-term (135 d) speciation of dissolved Cu, cop...

  16. Role of serial order in the impact of talker variability on short-term memory: testing a perceptual organization-based account.

    Science.gov (United States)

    Hughes, Robert W; Marsh, John E; Jones, Dylan M

    2011-11-01

    In two experiments, we examined the impact of the degree of match between sequential auditory perceptual organization processes and the demands of a short-term memory task (memory for order vs. item information). When a spoken sequence of digits was presented so as to promote its perceptual partitioning into two distinct streams by conveying it in alternating female (F) and male (M) voices (FMFMFMFM)--thereby disturbing the perception of true temporal order--recall of item order was greatly impaired (as compared to recall of item identity). Moreover, an order error type consistent with the formation of voice-based streams was committed more quickly in the alternating-voice condition (Exp. 1). In contrast, when the perceptual organization of the sequence mapped well onto an optimal two-group serial rehearsal strategy--by presenting the two voices in discrete clusters (FFFFMMMM)--order, but not item, recall was enhanced (Exp. 2). The results are consistent with the view that the degree of compatibility between perceptual and deliberate sequencing processes is a key determinant of serial short-term memory performance. Alternative accounts of talker variability effects in short-term memory, based on the concept of a dedicated phonological short-term store and a capacity-limited focus of attention, are also reviewed.

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

    International Nuclear Information System (INIS)

    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)

  18. An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction

    International Nuclear Information System (INIS)

    Zheng, Xiujuan; Fang, Huajing

    2015-01-01

    The gradual decreasing capacity of lithium-ion batteries can serve as a health indicator for tracking the degradation of lithium-ion batteries. It is important to predict the capacity of a lithium-ion battery for future cycles to assess its health condition and remaining useful life (RUL). In this paper, a novel method is developed using unscented Kalman filter (UKF) with relevance vector regression (RVR) and applied to RUL and short-term capacity prediction of batteries. A RVR model is employed as a nonlinear time-series prediction model to predict the UKF future residuals which otherwise remain zero during the prediction period. Taking the prediction step into account, the predictive value through the RVR method and the latest real residual value constitute the future evolution of the residuals with a time-varying weighting scheme. Next, the future residuals are utilized by UKF to recursively estimate the battery parameters for predicting RUL and short-term capacity. Finally, the performance of the proposed method is validated and compared to other predictors with the experimental data. According to the experimental and analysis results, the proposed approach has high reliability and prediction accuracy, which can be applied to battery monitoring and prognostics, as well as generalized to other prognostic applications. - Highlights: • An integrated method is proposed for RUL prediction as well as short-term capacity prediction. • Relevance vector regression model is employed as a nonlinear time-series prediction model. • Unscented Kalman filter is used to recursively update the states for battery model parameters during the prediction. • A time-varying weighting scheme is utilized to improve the accuracy of the RUL prediction. • The proposed method demonstrates high reliability and prediction accuracy.

  19. Behavioral relevance of gamma-band activity for short-term memory-based auditory decision-making.

    Science.gov (United States)

    Kaiser, Jochen; Heidegger, Tonio; Lutzenberger, Werner

    2008-06-01

    Oscillatory activity in the gamma-band range has been established as a correlate of cognitive processes, including perception, attention and memory. Only a few studies, however, have provided evidence for an association between gamma-band activity (GBA) and measures of behavioral performance. Here we focused on the comparison between sample and test stimuli S1 and S2 during an auditory spatial short-term memory task. Applying statistical probability mapping to magnetoencephalographic recordings from 28 human subjects, we identified GBA components distinguishing nonidentical from identical S1-S2 pairs. This activity was found at frequencies between 65 and 90 Hz and was localized over posterior cortical regions contralateral to the hemifield in which the stimuli were presented. The 10 best task performers showed higher amplitudes of this GBA component than the 10 worst performers. This group difference was most pronounced between about 150 and 300 ms after stimulus onset. Apparently the decision about whether test stimuli matched the stored representation of previously presented sample sounds relied partly on the oscillatory activation of networks representing differences between both stimuli. This result could be replicated by reanalyzing the combined data from two previous studies assessing short-term memory for sound duration and sound lateralization, respectively. Similarly to our main study, GBA amplitudes to nonmatching vs. matching S1-S2 pairs were higher in good performers than poor performers. The present findings demonstrate the behavioral relevance of GBA.

  20. The Impact of Laparoscopic Approaches on Short-term Outcomes in Patients Undergoing Liver Surgery for Metastatic Tumors.

    Science.gov (United States)

    Karagkounis, Georgios; Seicean, Andreea; Berber, Eren

    2015-06-01

    To compare the perioperative outcomes associated with open and laparoscopic (LAP) surgical approaches for liver metastases. The American College of Surgeons National Surgical Quality Improvement Program database was used to identify all adult patients who underwent surgical therapy for metastatic liver tumors between 2006 and 2012 (N=7684). Patients who underwent >1 procedure were excluded. Logistic regression after matching on propensity scores was used to assess the association between surgical approaches and perioperative outcomes. A total of 4555 patients underwent open resection, 387 LAP resection, 297 open radiofrequency ablation (RFA), and 265 LAP RFA. In propensity-matched samples (over 95% of patients successfully matched), there was no significant difference between LAP resection and LAP RFA in perioperative complications and length of stay and both compared favorably with their open counterparts. Minimally invasive approaches for secondary hepatic malignancies were associated with improved postoperative morbidity and length of stay and should be preferred in appropriate patients.

  1. A Bayesian Approach to Excess Volatility, Short-term Underreaction and Long-term Overreaction during Financial Crises

    NARCIS (Netherlands)

    X. Guo (Xu); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung); L. Zhu (Lixing)

    2016-01-01

    textabstractIn this paper, we introduce a new Bayesian approach to explain some market anomalies during financial crises and subsequent recovery. We assume that the earnings shock of an asset follows a random walk model with and without drift to incorporate the impact of financial crises. We further

  2. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

    Science.gov (United States)

    Qiu, Yuchen; Wang, Yunzhi; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

    2016-03-01

    In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 "prior" negative screening cases was assembled. In the next sequential ("current") screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.

  3. A hybrid approach EMD-HW for short-term forecasting of daily stock market time series data

    Science.gov (United States)

    Awajan, Ahmad Mohd; Ismail, Mohd Tahir

    2017-08-01

    Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Holt-Winter method (EMD-HW) is used to improve forecasting performances in financial time series. The strength of this EMD-HW lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 11 countries is applied to show the forecasting performance of the proposed EMD-HW. Based on the three forecast accuracy measures, the results indicate that EMD-HW forecasting performance is superior to traditional Holt-Winter forecasting method.

  4. Short-term prediction of threatening and violent behaviour in an Acute Psychiatric Intensive Care Unit based on patient and environment characteristics

    Directory of Open Access Journals (Sweden)

    Morken Gunnar

    2011-03-01

    Full Text Available Abstract Background The aims of the present study were to investigate clinically relevant patient and environment-related predictive factors for threats and violent incidents the first three days in a PICU population based on evaluations done at admittance. Methods In 2000 and 2001 all 118 consecutive patients were assessed at admittance to a Psychiatric Intensive Care Unit (PICU. Patient-related conditions as actuarial data from present admission, global clinical evaluations by physician at admittance and clinical nurses first day, a single rating with an observer rated scale scoring behaviours that predict short-term violence in psychiatric inpatients (The Brøset Violence Checklist (BVC at admittance, and environment-related conditions as use of segregation or not were related to the outcome measure Staff Observation Aggression Scale-Revised (SOAS-R. A multiple logistic regression analysis with SOAS-R as outcome variable was performed. Results The global clinical evaluations and the BVC were effective and more suitable than actuarial data in predicting short-term aggression. The use of segregation reduced the number of SOAS-R incidents. Conclusions In a naturalistic group of patients in a PICU segregation of patients lowers the number of aggressive and threatening incidents. Prediction should be based on clinical global judgment, and instruments designed to predict short-term aggression in psychiatric inpatients. Trial registrations NCT00184119/NCT00184132

  5. Short-term effects of a rights-based sexuality education curriculum for high-school students: a cluster-randomized trial.

    Science.gov (United States)

    Constantine, Norman A; Jerman, Petra; Berglas, Nancy F; Angulo-Olaiz, Francisca; Chou, Chih-Ping; Rohrbach, Louise A

    2015-03-26

    An emerging model for sexuality education is the rights-based approach, which unifies discussions of sexuality, gender norms, and sexual rights to promote the healthy sexual development of adolescents. A rigorous evaluation of a rights-based intervention for a broad population of adolescents in the U.S. has not previously been published. This paper evaluates the immediate effects of the Sexuality Education Initiative (SEI) on hypothesized psychosocial determinants of sexual behavior. A cluster-randomized trial was conducted with ninth-grade students at 10 high schools in Los Angeles. Classrooms at each school were randomized to receive either a rights-based curriculum or basic sex education (control) curriculum. Surveys were completed by 1,750 students (N = 934 intervention, N = 816 control) at pretest and immediate posttest. Multilevel regression models examined the short-term effects of the intervention on nine psychosocial outcomes, which were hypothesized to be mediators of students' sexual behaviors. Compared with students who received the control curriculum, students receiving the rights-based curriculum demonstrated significantly greater knowledge about sexual health and sexual health services, more positive attitudes about sexual relationship rights, greater communication about sex and relationships with parents, and greater self-efficacy to manage risky situations at immediate posttest. There were no significant differences between the two groups for two outcomes, communication with sexual partners and intentions to use condoms. Participation in the rights-based classroom curriculum resulted in positive, statistically significant effects on seven of nine psychosocial outcomes, relative to a basic sex education curriculum. Longer-term effects on students' sexual behaviors will be tested in subsequent analyses. ClinicalTrials.gov NCT02009046.

  6. The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

    Directory of Open Access Journals (Sweden)

    Jin-peng Liu

    2017-07-01

    Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.

  7. Effect of Short-Term Fasting on Systemic Cytochrome P450-Mediated Drug Metabolism in Healthy Subjects: A Randomized, Controlled, Crossover Study Using a Cocktail Approach

    NARCIS (Netherlands)

    Lammers, Laureen A.; Achterbergh, Roos; van Schaik, Ron H. N.; Romijn, Johannes A.; Mathôt, Ron A. A.

    2017-01-01

    Short-term fasting can alter drug exposure but it is unknown whether this is an effect of altered oral bioavailability and/or systemic clearance. Therefore, the aim of our study was to assess the effect of short-term fasting on oral bioavailability and systemic clearance of different drugs. In a

  8. Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold

    International Nuclear Information System (INIS)

    Lim, Hyungkwang; Kim, Inho; Kim, Jin-Sang; Jeong, Doo Seok; Seong Hwang, Cheol

    2013-01-01

    Chemical synapses are important components of the large-scaled neural network in the hippocampus of the mammalian brain, and a change in their weight is thought to be in charge of learning and memory. Thus, the realization of artificial chemical synapses is of crucial importance in achieving artificial neural networks emulating the brain’s functionalities to some extent. This kind of research is often referred to as neuromorphic engineering. In this study, we report short-term memory behaviours of electrochemical capacitors (ECs) utilizing TiO 2 mixed ionic–electronic conductor and various reactive electrode materials e.g. Ti, Ni, and Cr. By experiments, it turned out that the potentiation behaviours did not represent unlimited growth of synaptic weight. Instead, the behaviours exhibited limited synaptic weight growth that can be understood by means of an empirical equation similar to the Bienenstock–Cooper–Munro rule, employing a sliding threshold. The observed potentiation behaviours were analysed using the empirical equation and the differences between the different ECs were parameterized. (paper)

  9. Prediction of short-term newborn infectious morbidity based on maternal characteristics in patients with PPROM and Ureaplasma species infection.

    Science.gov (United States)

    Mikołajczyk, Mateusz; Wirstlein, Przemysław Krzysztof; Wróbel, Magdalena; Mazela, Jan; Chojnacka, Karolina; Skrzypezak, Jana

    2015-09-01

    Preterm premature rupture of membranes (PPROM) complicates about 5% of pregnancies. Ureaplasma species is the most common pathogen found in the amniotic fluid in pregnancieneonatal outcome. The aim of the following study was to evaluate the impact of colonization with the Ureaplasma spp. on pregnant women with PPROM, coin fection with different microorganisms, and antimicrobial treatment on neonatal outcome. The study included 30 women with PPROM hospitalized in Division of Reproduction in s complicated by PPROM. It is speculated that it requires a coin fection to produce unfavorable Poznan's K. Marcinkowski University of Medical Sciences. Swabs from cenvical canal were obtained for the identifidation of bacterial and ureaplasma tic infections by culture and POR. The presence of any infection during the pregnancy a fter PP ROM was con firmed in 22 patients (Ureaplasma spp. in 12 patients, coin fection in 10 women). The cure rate for Ureaplasma species and other infections was 17% (2/12 patients) and 23% (5/22 patients), respectively There was no correlation between Ureaplasma species infection, coin fection, and cure status with the infection in the newborn. The PPROM to delivery duration also did not affect the newborn infection status. A negative relationship with leukocyte level was detected in patient with newborn infection. The presence of colonization with Ureaplasma species is not attributable to neonatal short-term morbidity The evaluation of maternal biochemical and microbiological data, regardless of the duration of the pregnancy after PPROM or the cure status, does not add any insight into the newborn infection status.

  10. Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang; Zhang, Yingchen

    2016-11-14

    This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

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

    Science.gov (United States)

    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.

  12. Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

    Directory of Open Access Journals (Sweden)

    Federico Divina

    2018-04-01

    Full Text Available The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.

  13. Energy management strategy based on short-term generation scheduling for a renewable microgrid using a hydrogen storage system

    International Nuclear Information System (INIS)

    Cau, Giorgio; Cocco, Daniele; Petrollese, Mario; Knudsen Kær, Søren; Milan, Christian

    2014-01-01

    Highlights: • Energy management strategy for hybrid stand-alone power plant with hydrogen storage. • Optimal scheduling of storage devices to minimize the utilization costs. • A scenario tree method is used to manage uncertainties of weather and load forecasts. • A reduction of operational costs and energy losses is achieved. - Abstract: This paper presents a novel energy management strategy (EMS) to control an isolated microgrid powered by a photovoltaic array and a wind turbine and equipped with two different energy storage systems: electric batteries and a hydrogen production and storage system. In particular, an optimal scheduling of storage devices is carried out to maximize the benefits of available renewable resources by operating the photovoltaic systems and the wind turbine at their maximum power points and by minimizing the overall utilization costs. Unlike conventional EMS based on the state-of-charge (SOC) of batteries, the proposed EMS takes into account the uncertainty due to the intermittent nature of renewable resources and electricity demand. In particular, the uncertainties are evaluated with a stochastic approach through the construction of different scenarios with corresponding probabilities. The EMS is defined by minimizing the utilization costs of the energy storage equipment. The weather conditions recorded in four different weeks between April and December are used as case studies to test the proposed EMS and the results obtained are compared with a conventional EMS based on the state-of-charge of batteries. The results show a reduction of utilization costs of about 15% in comparison to conventional SOC-based EMS and an increase of the average energy storage efficiency

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

    Directory of Open Access Journals (Sweden)

    Chuan Ding

    2016-10-01

    Full Text Available Understanding the relationship between short-term subway ridership and its influential factors is crucial to improving the accuracy of short-term subway ridership prediction. Although there has been a growing body of studies on short-term ridership prediction approaches, limited effort is made to investigate the short-term subway ridership prediction considering bus transfer activities and temporal features. To fill this gap, a relatively recent data mining approach called gradient boosting decision trees (GBDT is applied to short-term subway ridership prediction and used to capture the associations with the independent variables. Taking three subway stations in Beijing as the cases, the short-term subway ridership and alighting passengers from its adjacent bus stops are obtained based on transit smart card data. To optimize the model performance with different combinations of regularization parameters, a series of GBDT models are built with various learning rates and tree complexities by fitting a maximum of trees. The optimal model performance confirms that the gradient boosting approach can incorporate different types of predictors, fit complex nonlinear relationships, and automatically handle the multicollinearity effect with high accuracy. In contrast to other machine learning methods—or “black-box” procedures—the GBDT model can identify and rank the relative influences of bus transfer activities and temporal features on short-term subway ridership. These findings suggest that the GBDT model has considerable advantages in improving short-term subway ridership prediction in a multimodal public transportation system.

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

    International Nuclear Information System (INIS)

    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.

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

    International Nuclear Information System (INIS)

    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

  17. A short-term, comprehensive, yoga-based lifestyle intervention is efficacious in reducing anxiety, improving subjective well-being and personality

    Directory of Open Access Journals (Sweden)

    Raj Kumar Yadav

    2012-01-01

    Full Text Available Objective: To assess the efficacy of a short-term comprehensive yoga-based lifestyle intervention in reducing anxiety, improving subjective well-being and personality. Materials and Methods: The study is a part of an ongoing larger study at a tertiary care hospital. Participants (n=90 included patients with chronic diseases attending a 10-day, yoga-based lifestyle intervention program for prevention and management of chronic diseases, and healthy controls (n=45 not attending any such intervention. Primary Outcome Measures: Change in state and trait anxiety questionnaire (STAI-Y; 40 items, subjective well-being inventory (SUBI; 40 items, and neuroticism extraversion openness to experience five factor personality inventory revised (NEO-FF PI-R; 60 items at the end of intervention. Results: Following intervention, the STAI-Y scores reduced significantly (P0.01 at Day 10 versus Day 1. Similarly NEO-FF PI-R scores improved significantly (P<0.001 at Day 10 versus Day 1. Control group showed an increase in STAI-Y while SUBI and NEO-FF PI-R scores remained comparable at Day 10 versus Day 1. Conclusions: The observations suggest that a short-term, yoga-based lifestyle intervention may significantly reduce anxiety and improve subjective well-being and personality in patients with chronic diseases.

  18. Short-term corneal changes with gas-permeable contact lens wear in keratoconus subjects: a comparison of two fitting approaches.

    Science.gov (United States)

    Romero-Jiménez, Miguel; Santodomingo-Rubido, Jacinto; Flores-Rodríguez, Patricia; González-Méijome, Jose-Manuel

    2015-01-01

    To evaluate changes in anterior corneal topography and higher-order aberrations (HOA) after 14-days of rigid gas-permeable (RGP) contact lens (CL) wear in keratoconus subjects comparing two different fitting approaches. Thirty-one keratoconus subjects (50 eyes) without previous history of CL wear were recruited for the study. Subjects were randomly fitted to either an apical-touch or three-point-touch fitting approach. The lens' back optic zone radius (BOZR) was 0.4mm and 0.1mm flatter than the first definite apical clearance lens, respectively. Differences between the baseline and post-CL wear for steepest, flattest and average corneal power (ACP) readings, central corneal astigmatism (CCA), maximum tangential curvature (KTag), anterior corneal surface asphericity, anterior corneal surface HOA and thinnest corneal thickness measured with Pentacam were compared. A statistically significant flattening was found over time on the flattest and steepest simulated keratometry and ACP in apical-touch group (all p<0.01). A statistically significant reduction in KTag was found in both groups after contact lens wear (all p<0.05). Significant reduction was found over time in CCA (p=0.001) and anterior corneal asphericity in both groups (p<0.001). Thickness at the thinnest corneal point increased significantly after CL wear (p<0.0001). Coma-like and total HOA root mean square (RMS) error were significantly reduced following CL wearing in both fitting approaches (all p<0.05). Short-term rigid gas-permeable CL wear flattens the anterior cornea, increases the thinnest corneal thickness and reduces anterior surface HOA in keratoconus subjects. Apical-touch was associated with greater corneal flattening in comparison to three-point-touch lens wear. Copyright © 2014 Spanish General Council of Optometry. Published by Elsevier Espana. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Peng Sun

    2016-10-01

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

  20. Short-term exposure to mobile phone base station signals does not affect cognitive functioning or physiological measures in individuals who report sensitivity to electromagnetic fields and controls.

    Science.gov (United States)

    Eltiti, Stacy; Wallace, Denise; Ridgewell, Anna; Zougkou, Konstantina; Russo, Riccardo; Sepulveda, Francisco; Fox, Elaine

    2009-10-01

    Individuals who report sensitivity to electromagnetic fields often report cognitive impairments that they believe are due to exposure to mobile phone technology. Previous research in this area has revealed mixed results, however, with the majority of research only testing control individuals. Two studies using control and self-reported sensitive participants found inconsistent effects of mobile phone base stations on cognitive functioning. The aim of the present study was to clarify whether short-term (50 min) exposure at 10 mW/m(2) to typical Global System for Mobile Communication (GSM) and Universal Mobile Telecommunications System (UMTS) base station signals affects attention, memory, and physiological endpoints in sensitive and control participants. Data from 44 sensitive and 44 matched-control participants who performed the digit symbol substitution task (DSST), digit span task (DS), and a mental arithmetic task (MA), while being exposed to GSM, UMTS, and sham signals under double-blind conditions were analyzed. Overall, cognitive functioning was not affected by short-term exposure to either GSM or UMTS signals in the current study. Nor did exposure affect the physiological measurements of blood volume pulse (BVP), heart rate (HR), and skin conductance (SC) that were taken while participants performed the cognitive tasks.

  1. Behavior change techniques used in group-based behavioral support by the English stop-smoking services and preliminary assessment of association with short-term quit outcomes.

    Science.gov (United States)

    West, Robert; Evans, Adam; Michie, Susan

    2011-12-01

    To develop a reliable coding scheme for components of group-based behavioral support for smoking cessation, to establish the frequency of inclusion in English Stop-Smoking Service (SSS) treatment manuals of specific components, and to investigate the associations between inclusion of behavior change techniques (BCTs) and service success rates. A taxonomy of BCTs specific to group-based behavioral support was developed and reliability of use assessed. All English SSSs (n = 145) were contacted to request their group-support treatment manuals. BCTs included in the manuals were identified using this taxonomy. Associations between inclusion of specific BCTs and short-term (4-week) self-reported quit outcomes were assessed. Fourteen group-support BCTs were identified with >90% agreement between coders. One hundred and seven services responded to the request for group-support manuals of which 30 had suitable documents. On average, 7 BCTs were included in each manual. Two were positively associated with 4-week quit rates: "communicate group member identities" and a "betting game" (a financial deposit that is lost if a stop-smoking "buddy" relapses). It is possible to reliably code group-specific BCTs for smoking cessation. Fourteen such techniques are present in guideline documents of which 2 appear to be associated with higher short-term self-reported quit rates when included in treatment manuals of English SSSs.

  2. IRT-Based Measurement of Short-Term Changes of Ability, with an Application to Assessing the "Mozart Effect"

    Science.gov (United States)

    Gittler, Georg; Fischer, Gerhard

    2011-01-01

    The article extends and applies previous approaches by Klauer and Fischer to the statistical evaluation of ability changes in tests conforming to the Rasch model (RM). Exact uniformly most powerful unbiased (UMPU) hypothesis tests and uniformly most accurate (UMA) confidence intervals (CIs) for the amount of change can be constructed for each…

  3. Onboard Short Term Plan Viewer

    Science.gov (United States)

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

    2011-01-01

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

  4. Short-term memory across eye blinks.

    Science.gov (United States)

    Irwin, David E

    2014-01-01

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

  5. Short-term Forecast of Automatic Frequency Restoration Reserve from a Renewable Energy Based Virtual Power Plant

    OpenAIRE

    Camal , Simon; Michiorri , Andrea; Kariniotakis , Georges; Liebelt , Andreas

    2017-01-01

    International audience; This paper presents the initial findings on a new forecast approach for ancillary services delivered by aggregated renewable power plants. The increasing penetration of distributed variable generators challenges grid reliability. Wind and photovoltaic power plants are technically able to provide ancillary services, but their stochastic behavior currently impedes their integration into reserve mechanisms. A methodology is developed to forecast the flexibility that a win...

  6. A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Yuyang Gao

    2016-09-01

    Full Text Available With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.

  7. [A Paired Case Controlled Study Comparing the Short-term Outcomes of Da Vinci RATS and VATS Approach for Non-small Cell Lung Cancer].

    Science.gov (United States)

    Dai, Feng; Xu, Shiguang; Xu, Wei; Ding, Renquan; Liu, Bo; Meng, Hao; Kang, Yunteng; Meng, Xiangrui; Lin, Jie; Wang, Shumin

    2018-03-20

    Da Vinci Surgical System is one of the greatest inventions of the 20th century, which represents the development direction of the precise minimally invasive surgical techniques, the aim of this study was to comparing the short-term outcomes between da Vinci robot-assisted lobectomy and video-assisted thoracic surgery (VATS) lobectomy for non-small cell lung cancer. 45 pairs of non-small cell lung cancer patients underwent pulmonary lobectomy with da Vinci Robotic assisted thoracoscopic (RATS) and VATS approach during the same period from January 2014 to January 2017. The operative time, estimated blood loss (EBL), total number and total groups of dissected lymph nodes, postoperative duration of drainage, the first day volume of drainage, total volume of drainage were compared. No perioperative death and convertion to thoracotomy occured in both groups. There were significant difference between RATS group and VATS group in EBL [(50.30±32.33) mL vs (208.60±132.63) mL], the first day volume of drainage [(275.00±145.42) mL vs (347.60±125.80) mL], the dissected total number [(22.67±9.67) vs (15.51±5.41)] and total team [(6.31±1.43) vs (4.91±1.04)] of lymph node. There were no significant difference in other outcomes. RATS is safe and effective and took better short-outcomes than VATS in non-small cell lung cancer.

  8. Short-term effectiveness of web-based guided self-help for phobic outpatients: Randomized controlled trial.

    NARCIS (Netherlands)

    Kok, R.N.; van Straten, A.; Beekman, A.T.F.; Cuijpers, P.

    2014-01-01

    Background: Internet-based guided self-help has been successfully used in the general population, but it is unknown whether this method can be effectively used in outpatient clinics for patients waiting for face-to-face psychotherapy for phobias. Objective: The aim was to assess the clinical

  9. Short-Term Impact of Safer Choices: A Multicomponent, School-Based HIV, Other STD, and Pregnancy Prevention Program.

    Science.gov (United States)

    Coyle, Karin; Basen-Engquist, Karen; Kirby, Douglas; Parcel, Guy; Banspach, Stephen; Harrist, Ronald; Baumler, Elizabeth; Weil, Marsha

    1999-01-01

    Evaluated the effectiveness of the first year of "Safer Choices," a two-year, multicomponent HIV, STD, and pregnancy-prevention program for high school students based on social theory. Student self-report surveys indicated that "Safer Choices" succeeded in reducing selected risk behaviors and in enhancing selected protective…

  10. The efficacy of a technology-based system in a short-term behavioral weight loss intervention.

    Science.gov (United States)

    Polzien, Kristen M; Jakicic, John M; Tate, Deborah F; Otto, Amy D

    2007-04-01

    The objective was to examine the efficacy of adding a technology-based program to an in-person, behavioral weight loss intervention. Fifty-seven subjects (BMI=33.1+/-2.8 kg/m2; age=41.3+/-8.7 years) participated in a 12-week intervention with random assignment to Standard In-Person Behavioral Weight Control Program (SBWP) or Intermittent or Continuous Technology-Based Program (INT-TECH, CON-TECH). SBWP subjects received seven individualized weight loss sessions encouraging dietary and exercise modifications. INT-TECH and CON-TECH subjects received all SBWP components; additionally, these groups used a SenseWear Pro Armband (BodyMedia, Inc.) to monitor energy expenditure and an Internet-based program to monitor eating behaviors. These features were used by INT-TECH subjects during weeks 1, 5, and 9 and CON-TECH subjects weekly throughout the intervention. Intent-to-treat analysis revealed weight loss of 4.1+/-2.8 kg, 3.4+/-3.4 kg, and 6.2+/-4.0 kg, for SBWP, INT-TECH, and CON-TECH groups, respectively (CON-TECH>INT-TECH, ptechnology-based program needs to be used continuously throughout the intervention period to significantly impact weight loss. Future studies should examine the long-term and independent effect of this technology on weight loss, and for whom this intervention format is most effective.

  11. Preadmission Use of Platelet Inhibitors and Short-Term Stroke Mortality:A Population-Based Cohort Study

    DEFF Research Database (Denmark)

    Würtz, Morten; Schmidt, Morten; Grove, Erik Lerkevang

    2018-01-01

    Aims: The impact of preadmission antiplatelet treatment on prognosis after stroke is poorly understood. We therefore investigated whether preadmission use of aspirin and clopidogrel was associated with mortality in patients hospitalized with ischemic stroke, intracerebral hemorrhage (ICH......), or subarachnoid hemorrhage (SAH). Methods and Results: We used nationwide population-based registries to identify all first-time hospitalizations for stroke and subsequent mortality in patients treated with aspirin and clopidogrel in Denmark during 2004-2012. Based on redeemed prescriptions, we computed absolute...... 30-day mortality rates and mortality rate ratios (MRRs) for current platelet inhibitor users and non-users. We used Cox regression to control for potentially confounding factors. Among platelet inhibitor non-users, 30-day stroke mortality was 12.0% (8.8% for ischemic stroke, 29.6% for ICH, and 21...

  12. [Assessment on the short-term impact regarding the community-based interventions to improve physical activities in three urban areas of Hangzhou city].

    Science.gov (United States)

    Gao, Fang; Liu, Qing-min; Ren, Yan-jun; He, Ping-ping; LV, Jun; Li, Li-ming

    2013-06-01

    To evaluate the short-term impact of comprehensive community based intervention on physical activity (PA) of adults living in the three urban communities of Hangzhou city. Within the framework of Community Interventions for Health (CIH) Program, a community trial was conducted in two urban areas (Xiacheng district and Gongshu district)and an urban area(Xihu district)as control, by a parallel comparison and random grouping based quasi-experimental design. Two independent questionnaire-based surveys of cross-sectional samples in the intervention and comparison areas were used to assess the short-term impact of the intervention program. A total of 2016 adults at baseline and 2016 adults at follow-up stages, completed the survey, including 1016 adults from the intervention areas and 1000 from the comparison area. Over the two-year intervention period, the cognitive level on benefits of physical activity in the intervention areas were trending downward. The changes observed in the comparison area did not show statistical significance. Intervention areas showed a statistically significant increase (1204 vs. 1386, P = 0.023) in the level of physical activity(metabolic equivalent, MET-minutes/week)compared with the comparison area(918 vs. 924, P = 0.201). And results remained the same after eliminating the possible effects of age factor. After a two-year intervention, beneficial changes were noted in the intervention areas with respect to the level of physical activity. A community-based intervention program on physical activity seemed feasible and effective in the urban areas of Hangzhou.

  13. Effect of Short-Term Fasting on Systemic Cytochrome P450-Mediated Drug Metabolism in Healthy Subjects: A Randomized, Controlled, Crossover Study Using a Cocktail Approach.

    Science.gov (United States)

    Lammers, Laureen A; Achterbergh, Roos; van Schaik, Ron H N; Romijn, Johannes A; Mathôt, Ron A A

    2017-10-01

    Short-term fasting can alter drug exposure but it is unknown whether this is an effect of altered oral bioavailability and/or systemic clearance. Therefore, the aim of our study was to assess the effect of short-term fasting on oral bioavailability and systemic clearance of different drugs. In a randomized, controlled, crossover trial, 12 healthy subjects received a single administration of a cytochrome P450 (CYP) probe cocktail, consisting of caffeine (CYP1A2), metoprolol (CYP2D6), midazolam (CYP3A4), omeprazole (CYP2C19) and warfarin (CYP2C9), on four occasions: an oral (1) and intravenous (2) administration after an overnight fast (control) and an oral (3) and intravenous (4) administration after 36 h of fasting. Pharmacokinetic parameters of the probe drugs were analyzed using the nonlinear mixed-effects modeling software NONMEM. Short-term fasting increased systemic caffeine clearance by 17% (p = 0.04) and metoprolol clearance by 13% (p < 0.01), whereas S-warfarin clearance decreased by 19% (p < 0.01). Fasting did not affect bioavailability. The study demonstrates that short-term fasting alters CYP-mediated drug metabolism in a non-uniform pattern without affecting oral bioavailability.

  14. Short-term effectiveness of web-based guided self-help for phobic outpatients: randomized controlled trial.

    Science.gov (United States)

    Kok, Robin N; van Straten, Annemieke; Beekman, Aartjan T F; Cuijpers, Pim

    2014-09-29

    Internet-based guided self-help has been successfully used in the general population, but it is unknown whether this method can be effectively used in outpatient clinics for patients waiting for face-to-face psychotherapy for phobias. The aim was to assess the clinical effectiveness of Phobias Under Control, an Internet-based intervention based on exposure therapy with weekly guidance. We conducted a randomized controlled trial, recruiting 212 outpatients scheduled to receive face-to-face psychotherapy for any type of phobia at an outpatient clinic. Participants suffering from at least 1 DSM-IV or ICD-10 classified phobia (social phobia, agoraphobia with or without panic disorder, and/or specific phobia as ascertained by a telephone interview at baseline) were randomly allocated to either a 5-week Internet-based guided self-help program based on exposure therapy with weekly student support followed by face-to-face psychotherapy (n=105) or a wait-list control group followed by face-to-face psychotherapy (n=107). Primary outcome was the Fear Questionnaire (FQ). Secondary outcomes were the Beck Anxiety Inventory (BAI) and Center of Epidemiological Studies-Depression scale (CES-D). Assessments took place by telephone at baseline (T0) and on the Internet at posttest (T1, self-assessment at 5 weeks after baseline). Missing data at T1 were imputed. At posttest, analysis of covariance on the intention-to-treat sample showed significant but small effect sizes between intervention and control groups on the FQ (d=0.35, P=.02), CES-D (d=0.34, P=.03), and a nonsignificant effect size on the BAI (d=0.28. P=.05). Although initial acceptance was good, high nonresponse was observed, with 86 of 212 participants (40.5%) lost to follow-up at T1 and only 14 of 105 (13.3%) intervention participants finishing all 5 weeks. Phobias Under Control is modestly effective in lowering phobic and depressive symptoms in a relatively short period and may be clinically beneficial when implemented in

  15. Persistent spatial information in the FEF during object-based short-term memory does not contribute to task performance.

    Science.gov (United States)

    Clark, Kelsey L; Noudoost, Behrad; Moore, Tirin

    2014-06-01

    We previously reported the existence of a persistent spatial signal in the FEF during object-based STM. This persistent activity reflected the location at which the sample appeared, irrespective of the location of upcoming targets. We hypothesized that such a spatial signal could be used to maintain or enhance object-selective memory activity elsewhere in cortex, analogous to the role of a spatial signal during attention. Here, we inactivated a portion of the FEF with GABAa agonist muscimol to test whether the observed activity contributes to object memory performance. We found that, although RTs were slowed for saccades into the inactivated portion of retinotopic space, performance for samples appearing in that region was unimpaired. This contrasts with the devastating effects of the same FEF inactivation on purely spatial working memory, as assessed with the memory-guided saccade task. Thus, in a task in which a significant fraction of FEF neurons displayed persistent, sample location-based activity, disrupting this activity had no impact on task performance.

  16. Efficacy of a short-term yoga-based lifestyle intervention in reducing stress and inflammation: preliminary results.

    Science.gov (United States)

    Yadav, Raj Kumar; Magan, Dipti; Mehta, Nalin; Sharma, Ratna; Mahapatra, Sushil Chandra

    2012-07-01

    Previously it was shown that a brief yoga-based lifestyle intervention was efficacious in reducing oxidative stress and risk of chronic diseases even in a short duration. The objective of this study was to assess the efficacy of this intervention in reducing stress and inflammation in patients with chronic inflammatory diseases. This study reports preliminary results from a nonrandomized prospective ongoing study with pre-post design. The study was conducted at the Integral Health Clinic, an outpatient facility conducting these yoga-based lifestyle intervention programs for prevention and management of chronic diseases. Patients with chronic inflammatory diseases and overweight/obese subjects were included while physically challenged, and those on other interventions were excluded from the study. A pretested intervention program included asanas (postures), pranayama (breathing exercises), stress management, group discussions, lectures, and individualized advice. There was a reduction in stress (plasma cortisol and β-endorphin) and inflammation (interleukin [IL]-6 and tumor necrosis factor [TNF]-α) at day 0 versus day 10. Eighty-six (86) patients (44 female, 42 male, 40.07 ± 13.91 years) attended this program. Overall, the mean level of cortisol decreased from baseline to day 10 (149.95 ± 46.07, 129.07 ± 33.30 ng/mL; p=0.001) while β-endorphins increased from baseline to day 10 (3.53 ± 0.88, 4.06 ± 0.79 ng/mL; p=0.024). Also, there was reduction from baseline to day 10 in mean levels of IL-6 (2.16 ± 0.42, 1.94 ± 0.10 pg/mL, p=0.036) and TNF-α (2.85 ± 0.59, 1.95 ± 0.32 pg/mL, p=0.002). This brief yoga-based lifestyle intervention reduced the markers of stress and inflammation as early as 10 days in patients with chronic diseases; however, complete results of this study will confirm whether this program has utility as complementary and alternative therapy.

  17. Do TETRA (Airwave) base station signals have a short-term impact on health and well-being? A randomized double-blind provocation study.

    Science.gov (United States)

    Wallace, Denise; Eltiti, Stacy; Ridgewell, Anna; Garner, Kelly; Russo, Riccardo; Sepulveda, Francisco; Walker, Stuart; Quinlan, Terence; Dudley, Sandra; Maung, Sithu; Deeble, Roger; Fox, Elaine

    2010-06-01

    "Airwave" is the new communication system currently being rolled out across the United Kingdom for the police and emergency services, based on the Terrestrial Trunked Radio Telecommunications System (TETRA). Some police officers have complained about skin rashes, nausea, headaches, and depression as a consequence of using their Airwave handsets. In addition, a small subgroup in the population self-report being sensitive to electromagnetic fields (EMFs) in general. We conducted a randomized double-blind provocation study to establish whether short-term exposure to a TETRA base station signal has an impact on the health and well-being of individuals with self-reported "electrosensitivity" and of participants who served as controls. Fifty-one individuals with self-reported electrosensitivity and 132 age- and sex-matched controls participated in an open provocation test; 48 sensitive and 132 control participants went on to complete double-blind tests in a fully screened semianechoic chamber. Heart rate, skin conductance, and blood pressure readings provided objective indices of short-term physiological response. Visual analog scales and symptom scales provided subjective indices of well-being. We found no differences on any measure between TETRA and sham (no signal) under double-blind conditions for either controls or electrosensitive participants, and neither group could detect the presence of a TETRA signal at rates greater than chance (50%). When conditions were not double blind, however, the self-reported electrosensitive individuals did report feeling worse and experienced more severe symptoms during TETRA compared with sham. Our findings suggest that the adverse symptoms experienced by electrosensitive individuals are due to the belief of harm from TETRA base stations rather than to the low-level EMF exposure itself.

  18. Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system

    International Nuclear Information System (INIS)

    Arciniegas, Alvaro I.; Arciniegas Rueda, Ismael E.

    2008-01-01

    The Ontario Electricity Market (OEM), which opened in May 2002, is relatively new and is still under change. In addition, the bidding strategies of the participants are such that the relationships between price and fundamentals are non-linear and dynamic. The lack of market maturity and high complexity hinders the use of traditional statistical methodologies (e.g., regression analysis) for price forecasting. Therefore, a flexible model is needed to achieve good forecasting in OEM. This paper uses a Takagi-Sugeno-Kang (TSK) fuzzy inference system in forecasting the one-day-ahead real-time peak price of the OEM. The forecasting results of TSK are compared with those obtained by traditional statistical and neural network based forecasting. The comparison suggests that TSK has considerable value in forecasting one-day-ahead peak price in OEM. (author)

  19. Rhodiola crenulata- and Cordyceps sinensis-based supplement boosts aerobic exercise performance after short-term high altitude training.

    Science.gov (United States)

    Chen, Chung-Yu; Hou, Chien-Wen; Bernard, Jeffrey R; Chen, Chiu-Chou; Hung, Ta-Cheng; Cheng, Lu-Ling; Liao, Yi-Hung; Kuo, Chia-Hua

    2014-09-01

    High altitude training is a widely used strategy for improving aerobic exercise performance. Both Rhodiola crenulata (R) and Cordyceps sinensis (C) supplements have been reported to improve exercise performance. However, it is not clear whether the provision of R and C during high altitude training could further enhance aerobic endurance capacity. In this study, we examined the effect of R and C based supplementation on aerobic exercise capacity following 2-week high altitude training. Alterations to autonomic nervous system activity, circulatory hormonal, and hematological profiles were investigated. Eighteen male subjects were divided into two groups: Placebo (n=9) and R/C supplementation (RC, n=9). Both groups received either RC (R: 1400 mg+C: 600 mg per day) or the placebo during a 2-week training period at an altitude of 2200 m. After 2 weeks of altitude training, compared with Placebo group, the exhaustive run time was markedly longer (Placebo: +2.2% vs. RC: +5.7%; paltitude training (paltitude training provides greater training benefits in improving aerobic performance. This beneficial effect of RC treatment may result from better maintenance of PNS activity and accelerated physiological adaptations during high altitude training.

  20. Evaluation of Short-Term Cepstral Based Features for Detection of Parkinson’s Disease Severity Levels through Speech signals

    Science.gov (United States)

    Oung, Qi Wei; Nisha Basah, Shafriza; Muthusamy, Hariharan; Vijean, Vikneswaran; Lee, Hoileong

    2018-03-01

    Parkinson’s disease (PD) is one type of progressive neurodegenerative disease known as motor system syndrome, which is due to the death of dopamine-generating cells, a region of the human midbrain. PD normally affects people over 60 years of age, which at present has influenced a huge part of worldwide population. Lately, many researches have shown interest into the connection between PD and speech disorders. Researches have revealed that speech signals may be a suitable biomarker for distinguishing between people with Parkinson’s (PWP) from healthy subjects. Therefore, early diagnosis of PD through the speech signals can be considered for this aim. In this research, the speech data are acquired based on speech behaviour as the biomarker for differentiating PD severity levels (mild and moderate) from healthy subjects. Feature extraction algorithms applied are Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and Weighted Linear Prediction Cepstral Coefficients (WLPCC). For classification, two types of classifiers are used: k-Nearest Neighbour (KNN) and Probabilistic Neural Network (PNN). The experimental results demonstrated that PNN classifier and KNN classifier achieve the best average classification performance of 92.63% and 88.56% respectively through 10-fold cross-validation measures. Favourably, the suggested techniques have the possibilities of becoming a new choice of promising tools for the PD detection with tremendous performance.

  1. A Short-term ESPERTA-based Forecast Tool for Moderate-to-extreme Solar Proton Events

    Science.gov (United States)

    Laurenza, M.; Alberti, T.; Cliver, E. W.

    2018-04-01

    The ESPERTA (Empirical model for Solar Proton Event Real Time Alert) forecast tool has a Probability of Detection (POD) of 63% for all >10 MeV events with proton peak intensity ≥10 pfu (i.e., ≥S1 events, S1 referring to minor storms on the NOAA Solar Radiation Storms scale), from 1995 to 2014 with a false alarm rate (FAR) of 38% and a median (minimum) warning time (WT) of ∼4.8 (0.4) hr. The NOAA space weather scale includes four additional categories: moderate (S2), strong (S3), severe (S4), and extreme (S5). As S1 events have only minor impacts on HF radio propagation in the polar regions, the effective threshold for significant space radiation effects appears to be the S2 level (100 pfu), above which both biological and space operation impacts are observed along with increased effects on HF propagation in the polar regions. We modified the ESPERTA model to predict ≥S2 events and obtained a POD of 75% (41/55) and an FAR of 24% (13/54) for the 1995–2014 interval with a median (minimum) WT of ∼1.7 (0.2) hr based on predictions made at the time of the S1 threshold crossing. The improved performance of ESPERTA for ≥S2 events is a reflection of the big flare syndrome, which postulates that the measures of the various manifestations of eruptive solar flares increase as one considers increasingly larger events.

  2. Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

    Science.gov (United States)

    Ni, X. Y.; Huang, H.; Du, W. P.

    2017-02-01

    The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words 'Beijing; Air pollution' show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.

  3. Short-term use of glucocorticoids and risk of peptic ulcer bleeding: a nationwide population-based case-crossover study.

    Science.gov (United States)

    Tseng, C-L; Chen, Y-T; Huang, C-J; Luo, J-C; Peng, Y-L; Huang, D-F; Hou, M-C; Lin, H-C; Lee, F-Y

    2015-09-01

    Controversy exists regarding glucocorticoids therapy and the risk of peptic ulcer bleeding (PUB). The present study was undertaken to determine whether short-term use of glucocorticoids is associated with the occurrence of peptic ulcer bleeding. The records of adult patients hospitalised for newly diagnosed peptic ulcer bleeding from 2000 to 2012 were retrieved from the Taiwan National Health Insurance Research Database, a nationwide population-based registry system. The association between systemic glucocorticoids usage and peptic ulcer bleeding was determined with a conditional logistic regression model comparing cases and controls during time windows of 7, 14 and 28 days using a case-crossover design. Of the 8894 enrolled patients, the adjusted self-matched odds ratios for peptic ulcer bleeding after exposure to the glucocorticoids were 1.37 (95% CI: 1.12-1.68, P = 0.003) for the 7-day window, 1.66 (95% CI: 1.38-2.00, P peptic ulcer bleeding. Concomitant use of a nonselective nonsteroidal anti-inflammatory drug (NSAID) or aspirin further elevated the risk. However, it does not eliminate the effect of underlying diseases flare-up that may have placed the patients at risk for peptic ulcer bleeding in this kind of study design. Short-term (7-28 days) exposure to glucocorticoids is significantly associated with peptic ulcer bleeding; this risk seems dose-dependent and is higher when nonselective NSAIDs or aspirin are used concurrently. © 2015 John Wiley & Sons Ltd.

  4. Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods

    International Nuclear Information System (INIS)

    Zhang, Yachao; Liu, Kaipei; Qin, Liang; An, Xueli

    2016-01-01

    Highlights: • Variational mode decomposition is adopted to process original wind power series. • A novel combined model based on machine learning methods is established. • An improved differential evolution algorithm is proposed for weight adjustment. • Probabilistic interval prediction is performed by quantile regression averaging. - Abstract: Due to the increasingly significant energy crisis nowadays, the exploitation and utilization of new clean energy gains more and more attention. As an important category of renewable energy, wind power generation has become the most rapidly growing renewable energy in China. However, the intermittency and volatility of wind power has restricted the large-scale integration of wind turbines into power systems. High-precision wind power forecasting is an effective measure to alleviate the negative influence of wind power generation on the power systems. In this paper, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. Variational mode decomposition is firstly adopted to handle the instability of the raw wind power series, and the subseries can be reconstructed by measuring sample entropy of the decomposed modes. Then the base models can be established for each subseries respectively. On this basis, the combined model is developed based on the optimal virtual prediction scheme, the weight matrix of which is dynamically adjusted by a self-adaptive multi-strategy differential evolution algorithm. Besides, a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models is presented to quantify the potential risks of the wind power series. The simulation results indicate that: (1) the normalized mean absolute errors of the proposed combined model from one-step to three-step forecasting are 4.34%, 6.49% and 7.76%, respectively, which are much lower than those of the base models and the hybrid

  5. 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

    Science.gov (United States)

    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.

  6. The acceptability, usability and short-term outcomes of Get Real: A web-based program for psychotic-like experiences (PLEs

    Directory of Open Access Journals (Sweden)

    Emma Stafford

    2015-09-01

    Conclusions: The current study provided initial support for the acceptability, utility and positive short-term outcomes of Get Real. The program now requires efficacy testing in randomized controlled trials.

  7. A community-based multilevel intervention for smoking, physical activity and diet: short-term findings from the Community Interventions for Health programme in Hangzhou, China.

    Science.gov (United States)

    Lv, Jun; Liu, Qing-Min; Ren, Yan-Jun; He, Ping-Ping; Wang, Sheng-Feng; Gao, Fang; Li, Li-Ming

    2014-04-01

    To assess the short-term impact of a comprehensive, community-based multilevel intervention on knowledge, beliefs and practices with respect to smoking, physical activity and diet in Hangzhou, China. A non-randomised, controlled, before-after quasi-experimental trial was conducted in two intervention areas and one comparison area. The intervention built on a socioecological framework and took place across four settings: neighbourhoods, schools, workplaces and community health centres. Two independent cross-sectional surveys of adults aged 18-64 years at baseline and a subsequent follow-up were conducted in 2008/2009 and 2011 in the intervention and comparison areas. A 2-year intervention programme was begun in mid-2009 and continued until mid-2011. A total of 2016 adults at baseline and 2016 adults at follow-up completed the survey. Over the 2-year intervention period, the intervention areas showed a statistically significant decline (25.2% vs 18.7%, psmoking compared with the comparison area (18.0% vs 16.4%, p=0.343). The proportion of individuals who had noticed anyone smoking in any of nine locations in the previous 30 days demonstrated a statistically significant decline in the intervention (78.9% vs 66.5%, psmoking and physical activity but not diet. A community-based multilevel intervention programme is feasible in urban China.

  8. Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning

    Science.gov (United States)

    Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

    2016-07-01

    Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

  9. Short-term volcano-tectonic earthquake forecasts based on a moving mean recurrence time algorithm: the El Hierro seismo-volcanic crisis experience

    Science.gov (United States)

    García, Alicia; De la Cruz-Reyna, Servando; Marrero, José M.; Ortiz, Ramón

    2016-05-01

    Under certain conditions, volcano-tectonic (VT) earthquakes may pose significant hazards to people living in or near active volcanic regions, especially on volcanic islands; however, hazard arising from VT activity caused by localized volcanic sources is rarely addressed in the literature. The evolution of VT earthquakes resulting from a magmatic intrusion shows some orderly behaviour that may allow the occurrence and magnitude of major events to be forecast. Thus governmental decision makers can be supplied with warnings of the increased probability of larger-magnitude earthquakes on the short-term timescale. We present here a methodology for forecasting the occurrence of large-magnitude VT events during volcanic crises; it is based on a mean recurrence time (MRT) algorithm that translates the Gutenberg-Richter distribution parameter fluctuations into time windows of increased probability of a major VT earthquake. The MRT forecasting algorithm was developed after observing a repetitive pattern in the seismic swarm episodes occurring between July and November 2011 at El Hierro (Canary Islands). From then on, this methodology has been applied to the consecutive seismic crises registered at El Hierro, achieving a high success rate in the real-time forecasting, within 10-day time windows, of volcano-tectonic earthquakes.

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

    Directory of Open Access Journals (Sweden)

    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.

  11. Effect of Short-Term Mobile Phone Base Station Exposure on Cognitive Performance, Body Temperature, Heart Rate and Blood Pressure of Malaysians.

    Science.gov (United States)

    Malek, F; Rani, K A; Rahim, H A; Omar, M H

    2015-08-19

    Individuals who report their sensitivity to electromagnetic fields often undergo cognitive impairments that they believe are due to the exposure of mobile phone technology. The aim of this study is to clarify whether short-term exposure at 1 V/m to the typical Global System for Mobile Communication and Universal Mobile Telecommunications System (UMTS) affects cognitive performance and physiological parameters (body temperature, blood pressure and heart rate). This study applies counterbalanced randomizing single blind tests to determine if sensitive individuals experience more negative health effects when they are exposed to base station signals compared with sham (control) individuals. The sample size is 200 subjects with 50.0% Idiopathic Environmental Intolerance attributed to electromagnetic fields (IEI-EMF) also known as sensitive and 50.0% (non-IEI-EMF). The computer-administered Cambridge Neuropsychological Test Automated Battery (CANTAB eclipse(TM)) is used to examine cognitive performance. Four tests are chosen to evaluate Cognitive performance in CANTAB: Reaction Time (RTI), Rapid Visual Processing (RVP), Paired Associates Learning (PAL) and Spatial Span (SSP). Paired sample t-test on the other hand, is used to examine the physiological parameters. Generally, in both groups, there is no statistical significant difference between the exposure and sham exposure towards cognitive performance and physiological effects (P's > 0.05).

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

    Directory of Open Access Journals (Sweden)

    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.

  13. A short-term neural network memory

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

    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.

  15. The effect of PBL and film showing, frequent quizzes and lecture-based method on short-term performance of dentistry students

    Directory of Open Access Journals (Sweden)

    Sadr Lahijani M.S

    2004-01-01

    Full Text Available Background: Advocates have proposed that frequent testing increases the effectiveness of instruction by encouraging learners to study and review more often. It has also been argued that in this way, student errors can be identified and corrected earlier and good performance can be recognized, leading to more positive attitudes toward learning process. In problem-based learning (PBL, medical students reportedly take a more active role in learning and have better recall than students in a conventional learning environment. The hypothetical benefits of a PBL and studentbased environment and use of films in the class are the development of self-learning and problem-solving skills and enhancement of knowledge and motivation. Purpose: To examine the effect of combination of PBL method and film showing on the short-term performance of dentistry students and to compare it with lecture-based method and frequent quizzes. Methods: All students of 3 years (from 2000 till 2002 that had theoretical endodontic course (part 1 participated in this descriptive-analytic study. The scores of final examinations of this course were obtained from their files. Data were analyzed by SPSS software & ANOVA. Results: The results showed that by changing the way of learning (PBL and film showing in 2001, there was a statistical difference between scores of the students of 2000 and 2001. Also there was a statistical difference with the students’ scores in 2002- the group with frequent quizzes. Conclusion: The variables such as changing the way of learning, using different methods in teaching, showing scientific films in class or, as a whole, active learning have significant effects on the results of final examination. Key Words: PBL, lecture based method, education, frequent quizzes

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

    Directory of Open Access Journals (Sweden)

    Simon Gorin

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

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

    Science.gov (United States)

    Gorin, Simon; Kowialiewski, Benjamin; Majerus, Steve

    2016-01-01

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

  18. Short-term population-based and spatiotemporal nonlinear concentration-response associations between fine particulate matter and children's respiratory clinic visits

    Science.gov (United States)

    Yu, Hwa-Lung; Chien, Lung-Chang

    2014-05-01

    Advert health impacts associated with the PM2.5 exposure have been confirmed in mortality and cardiovascular diseases; however, findings of the influence of PM2.5 on respiratory diseases investigated among previous studies are still inconsistent. We investigated the short-term population-based associations between the respiratory clinic visits of children population and the PM2.5 exposure levels with considering both the spatiotemporal distributions of the ambient pollution and clinic visit data. We applied a spatiotemporal structured additive regression model to examine the concentration-response (C-R) association between daily children's respiratory clinic visits and PM2.5 concentrations. The analysis was performed separately on the four selected respiratory disease categories of the population-based dataset, obtained from Taiwan National Health Insurance database, covering the 41 districts in Taipei area during the period of 2005 to 2007. This study reveals a strong nonlinear C-R pattern that the PM2.5 increment can significantly affect respiratory health at PM2.5 concentration ≤ 18.17µg/m3 for both preschool children and schoolchildren. The elevated risks are especially present in the category of acute respiratory infections. PM2.5 increase is mostly non-significant to the more severe respiratory diseases, e.g., COPD and pneumonia, over the ranges of 8.85-92.45µg/m3. The significantly higher relative rate of respiratory clinic visit most likely concentrated at populated areas. We highlight the nonlinearity of the respiratory health impacts of PM2.5 on children's populations from the first study, to our knowledge, to investigate this population-based association. The strong nonlinearity can possibly cause the inconsistency of PM2.5 health impact assessments with linear assumptions.

  19. Does short-term exposure to mobile phone base station signals increase symptoms in individuals who report sensitivity to electromagnetic fields? A double-blind randomized provocation study.

    Science.gov (United States)

    Eltiti, Stacy; Wallace, Denise; Ridgewell, Anna; Zougkou, Konstantina; Russo, Riccardo; Sepulveda, Francisco; Mirshekar-Syahkal, Dariush; Rasor, Paul; Deeble, Roger; Fox, Elaine

    2007-11-01

    Individuals with idiopathic environmental illness with attribution to electromagnetic fields (IEI-EMF) believe they suffer negative health effects when exposed to electromagnetic fields from everyday objects such as mobile phone base stations. This study used both open provocation and double-blind tests to determine if sensitive and control individuals experience more negative health effects when exposed to base station-like signals compared with sham. Fifty-six self-reported sensitive and 120 control participants were tested in an open provocation test. Of these, 12 sensitive and 6 controls withdrew after the first session. The remainder completed a series of double-blind tests. Subjective measures of well-being and symptoms as well as physiological measures of blood volume pulse, heart rate, and skin conductance were obtained. During the open provocation, sensitive individuals reported lower levels of well-being in both the global system for mobile communication (GSM) and universal mobile telecommunications system (UMTS) compared with sham exposure, whereas controls reported more symptoms during the UMTS exposure. During double-blind tests the GSM signal did not have any effect on either group. Sensitive participants did report elevated levels of arousal during the UMTS condition, whereas the number or severity of symptoms experienced did not increase. Physiological measures did not differ across the three exposure conditions for either group. Short-term exposure to a typical GSM base station-like signal did not affect well-being or physiological functions in sensitive or control individuals. Sensitive individuals reported elevated levels of arousal when exposed to a UMTS signal. Further analysis, however, indicated that this difference was likely to be due to the effect of order of exposure rather than the exposure itself.

  20. Short-term population-based non-linear concentration-response associations between fine particulate matter and respiratory diseases in Taipei (Taiwan): a spatiotemporal analysis.

    Science.gov (United States)

    Yu, Hwa-Lung; Chien, Lung-Chang

    2016-01-01

    Fine particulate matter respiratory disease remain inconsistent. The short-term, population-based association between the respiratory clinic visits of children and PM2.5 exposure levels were investigated by considering both the spatiotemporal distributions of ambient pollution and clinic visit data. We applied a spatiotemporal structured additive regression model to examine the concentration-response (C-R) association between children's respiratory clinic visits and PM2.5 concentrations. This analysis was separately performed on three respiratory disease categories that were selected from the Taiwanese National Health Insurance database, which includes 41 districts in the Taipei area of Taiwan from 2005 to 2007. The findings reveal a non-linear C-R pattern of PM2.5, particularly in acute respiratory infections. However, a PM2.5 increase at relatively lower levels can elevate the same-day respiratory health risks of both preschool children (increase from 0.76 to 7.44 μg/m(3), and in schoolchildren, same-day health risks rise when concentrations increase from 0.76 to 7.52 μg/m(3). Changes in PM2.5 levels generally exhibited no significant association with same-day respiratory risks, except in instances where PM2.5 levels are extremely high, and these occurrences do exhibit a significant positive influence on respiratory health that is especially notable in schoolchildren. A significant high relative rate of respiratory clinic visits are concentrated in highly populated areas. We highlight the non-linearity of the respiratory health effects of PM2.5 on children to investigate this population-based association. The C-R relationship in this study can provide a highly valuable alternative for assessing the effects of ambient air pollution on human health.

  1. A Case Study of Short-term Wave Forecasting Based on FIR Filter: Optimization of the Power Production for the Wavestar Device

    DEFF Research Database (Denmark)

    Ferri, Francesco; Sichani, Mahdi Teimouri; Frigaard, Peter

    2012-01-01

    Short-term wave forecasting plays a crucial role for the control of a wave energy converter (WEC), in order to increase the energy harvest from the waves, as well as to increase its life time. In the paper it is shown how the surface elevation of the waves and the force acting on the WEC can be p...

  2. Short-Term and Long-Term Effects of an Exercise-Based Patient Education Programme in People with Multiple Sclerosis: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Christina Lutz

    2017-01-01

    Full Text Available Background. Although people with Multiple Sclerosis (pwMS benefit from physical exercise, they still show reduced physical activity and exercise behaviour. This study aimed to investigate short- and long-term effects of an exercise-based patient education programme (ePEP that focuses on empowering pwMS to a sustainable and self-regulated exercise training management. Methods. Fourteen pwMS were randomly assigned to immediate experimental group (EG-I: n=8 and waitlist-control group (EG-W: n=6 and attended biweekly in a six-week ePEP. All participants were measured for walking ability, quality of life, fatigue, and self-efficacy towards physical exercise before and after the ePEP, after 12 weeks, and one year after baseline. Short-term effects were analysed in a randomised control trial and long-term effects of all ePEP participants (EG-I + EG-W = EG-all in a quasi-experimental design. Results. Only functional gait significantly improved in EG-I compared to EG-W (p=0.008, r=-0.67. Moderate to large effects were found in EG-all for walking ability. Not significant, however, relevant changes were detected for quality of life and fatigue. Self-efficacy showed no changes. Conclusion. The ePEP seems to be a feasible option to empower pwMS to a self-regulated and sustainable exercise training management shown in long-term walking improvements.

  3. Selective impact of disease on short-term and long-term components of self-reported memory: a population-based HUNT study.

    Science.gov (United States)

    Almkvist, Ove; Bosnes, Ole; Bosnes, Ingunn; Stordal, Eystein

    2017-05-09

    Subjective memory is commonly considered to be a unidimensional measure. However, theories of performance-based memory suggest that subjective memory could be divided into more than one dimension. To divide subjective memory into theoretically related components of memory and explore the relationship to disease. In this study, various aspects of self-reported memory were studied with respect to demographics and diseases in the third wave of the HUNT epidemiological study in middle Norway. The study included all individuals 55 years of age or older, who responded to a nine-item questionnaire on subjective memory and questionnaires on health (n=18 633). A principle component analysis of the memory items resulted in two memory components; the criterion used was an eigenvalue above 1, which accounted for 54% of the total variance. The components were interpreted as long-term memory (LTM; the first component; 43% of the total variance) and short-term memory (STM; the second component; 11% of the total variance). Memory impairment was significantly related to all diseases (except Bechterew's disease), most strongly to brain infarction, heart failure, diabetes, cancer, chronic obstructive pulmonary disease and whiplash. For most diseases, the STM component was more affected than the LTM component; however, in cancer, the opposite pattern was seen. Subjective memory impairment as measured in HUNT contained two components, which were differentially associated with diseases. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  4. The Short-term Effects of ASPIRA: A Web-based, Multimedia Smoking Prevention Program for Adolescents in Romania: A Cluster Randomized Trial.

    Science.gov (United States)

    Nădăşan, Valentin; Foley, Kristie L; Pénzes, Melinda; Paulik, Edit; Mihăicuţă, Ștefan; Ábrám, Zoltán; Bálint, Jozsef; Csibi, Monika; Urbán, Robert

    2017-08-01

    Although web-based, multimedia smoking prevention programs have been tested in several high-income countries, their efficacy in Central and Eastern Europe is unknown. The aim of this trial was to assess the short-term effects of ASPIRA, among Romanian and Hungarian speaking ninth graders in Tirgu Mures, Romania. ASPIRA is the Romanian acronym for the translated and adapted version of ASPIRE, "A Smoking Prevention Interactive Experience," an evidence-based smoking prevention program originally developed to prevent tobacco use among high school students in the United States. Sixteen high schools in Tirgu Mures, Romania were randomized to receive five weekly sessions of the ASPIRA web-based, multimedia program or to a control condition. Socio-demographic data, psychosocial characteristics, and smoking behavior were collected from students at baseline and at 6 months. A hierarchical logistic regression analysis was conducted to test the efficacy of the intervention on smoking initiation and current smoking among 1369 students. Never-smoker students in the intervention arm were 35% less likely to report smoking initiation 6 months after the baseline assessment (OR = 0.65, 95%CI: 0.44-0.97). Reduced smoking initiation was observed most notably among students who were exposed to at least 75% of the ASPIRA program. There was no statistically significant effect of the intervention on current tobacco use (OR = 0.80, 95%CI: 0.44-1.46). ASPIRA, an adapted version of the evidence-based, multimedia ASPIRE program that was originally developed and tested in the United States may decrease smoking initiation among multi-ethnic adolescents in Central and Eastern Europe. (1). Web-based, multimedia smoking prevention programs may be effective tools to prevent smoking initiation among multi-ethnic adolescent communities in Central and Eastern Europe. (2). The degree of exposure is critical, only high exposure to the multimedia smoking prevention program is associated with reduced

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

    KAUST Repository

    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

  6. Association between Short-Term Exposure to Air Pollution and Dyslipidemias among Type 2 Diabetic Patients in Northwest China: A Population-Based Study.

    Science.gov (United States)

    Wang, Minzhen; Zheng, Shan; Nie, Yonghong; Weng, Jun; Cheng, Ning; Hu, Xiaobin; Ren, Xiaowei; Pei, Hongbo; Bai, Yana

    2018-03-30

    Air pollution exposure may play an adverse role in diabetes. However, little data are available directly evaluating the effects of air pollution exposure in blood lipids of which dysfunction has been linked to diabetes or its complications. We aimed to evaluate the association between air pollution and lipids level among type 2 diabetic patients in Northwest China. We performed a population-based study of 3912 type 2 diabetes patients in an ongoing cohort study in China. Both spline and multiple linear regressions analysis were used to examine the association between short-term exposure to PM 10 , SO₂, NO₂ and total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). By spline analyses, we observed that the relationship between SO₂ and HDL-C and LDL-C was shown to be non-linear ( p _non-lin-association = 0.0162 and 0.000). An inverted U-shaped non-linear relationship between NO₂ and LDL-C was found ( p _non-lin-association < 0.0001). A J-shaped non-linear relationship between PM 10 and TC, HDL-C ( p _non-lin-association = 0.0173, 0.0367) was also revealed. In linear regression analyses, a 10 μg/m³ increment in SO₂ was associated with 1.31% (95% CI: 0.40-2.12%), 3.52% (95% CI: 1.07-6.03%) and 7.53% (95% CI: 5.98-9.09%) increase in TC, TG and LDL-C, respectively. A 10 μg/m³ increment in PM 10 was associated with 0.45% (95% CI: 0.08-0.82%), 0.29% (95% CI: 0.10-0.49%) and 0.83% (95% CI: 0.21-1.45%) increase in TC, HDL-C and LDL-C, respectively. For NO₂, an increment of 10 μg/m³ was statistically associated with -3.55% (95% CI: -6.40-0.61%) and 39.01% (95% CI: 31.43-47.03%) increase in HDL-C and LDL-C. The adverse effects of air pollutants on lipid levels were greater in female and elder people. Further, we found SO₂ and NO₂ played a more evident role in lipid levels in warm season, while PM 10 appeared stronger in cold season. The findings suggest that exposure to air

  7. Strong shift from HCO3 (-) to CO 2 uptake in Emiliania huxleyi with acidification: new approach unravels acclimation versus short-term pH effects.

    Science.gov (United States)

    Kottmeier, Dorothee M; Rokitta, Sebastian D; Tortell, Philippe D; Rost, Björn

    2014-09-01

    Effects of ocean acidification on Emiliania huxleyi strain RCC 1216 (calcifying, diploid life-cycle stage) and RCC 1217 (non-calcifying, haploid life-cycle stage) were investigated by measuring growth, elemental composition, and production rates under different pCO2 levels (380 and 950 μatm). In these differently acclimated cells, the photosynthetic carbon source was assessed by a (14)C disequilibrium assay, conducted over a range of ecologically relevant pH values (7.9-8.7). In agreement with previous studies, we observed decreased calcification and stimulated biomass production in diploid cells under high pCO2, but no CO2-dependent changes in biomass production for haploid cells. In both life-cycle stages, the relative contributions of CO2 and HCO3 (-) uptake depended strongly on the assay pH. At pH values ≤ 8.1, cells preferentially used CO2 (≥ 90 % CO2), whereas at pH values ≥ 8.3, cells progressively increased the fraction of HCO3 (-) uptake (~45 % CO2 at pH 8.7 in diploid cells; ~55 % CO2 at pH 8.5 in haploid cells). In contrast to the short-term effect of the assay pH, the pCO2 acclimation history had no significant effect on the carbon uptake behavior. A numerical sensitivity study confirmed that the pH-modification in the (14)C disequilibrium method yields reliable results, provided that model parameters (e.g., pH, temperature) are kept within typical measurement uncertainties. Our results demonstrate a high plasticity of E. huxleyi to rapidly adjust carbon acquisition to the external carbon supply and/or pH, and provide an explanation for the paradoxical observation of high CO2 sensitivity despite the apparently high HCO3 (-) usage seen in previous studies.

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

    Science.gov (United States)

    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.

  9. Measuring Short-term Energy Security

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2011-07-01

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

    Directory of Open Access Journals (Sweden)

    Tomas Grus

    2018-01-01

    Full Text Available Aim. The aim of this study was to evaluate short-term patency of the new prosthetic graft and its structural changes after explantation. Methods. The study team developed a three-layer conduit composed of a scaffold made from polyester coated with collagen from the inner and outer side with an internal diameter of 6 mm. The conduit was implanted as a bilateral bypass to the carotid artery in 7 sheep and stenosis was created in selected animals. After a period of 161 days, the explants were evaluated as gross and microscopic specimens. Results. The initial flow rate (median ± IQR in grafts with and without artificial stenosis was 120±79 ml/min and 255±255 ml/min, respectively. Graft occlusion occurred after 99 days in one of 13 conduits (patency rate: 92%. Wall-adherent thrombi occurred only in sharp curvatures in two grafts. Microscopic evaluation showed good engraftment and preserved structure in seven conduits; inflammatory changes with foci of bleeding, necrosis, and disintegration in four conduits; and narrowing of the graft due to thickening of the wall with multifocal separation of the outer layer in two conduits. Conclusions. This study demonstrates good short-term patency rates of a newly designed three-layer vascular graft even in low-flow conditions in a sheep model.

  11. Fertility after implementation of long- and short-term progesterone-based ovulation synchronization protocols for fixed-time artificial insemination in beef heifers.

    Science.gov (United States)

    Kasimanickam, R; Schroeder, S; Hall, J B; Whittier, W D

    2015-04-15

    Two experiments were conducted to evaluate the effect of long-term (LT; a 14-day controlled internal drug release insert [CIDR]-PGF2α [PGF]-GnRH) and short-term (ST; 5-day CO-Synch + CIDR) progesterone-based protocols on pregnancy rate to fixed-time artificial insemination (FTAI) in beef heifers. In experiment 1, Angus cross beef heifers (N = 1887) at nine locations received a body condition score and a reproductive tract score (RTS). Within the herd, heifers were randomly assigned to LT-72 and ST-56 protocol groups. Heifers in the LT-72 group received a CIDR from Days 0 to 14, followed by 25 mg of PGF 16 days later (Day 30). Heifers in the ST-56 group received a CIDR and 100 μg of gonadorelin hydrochloride (GnRH) on Day 25 followed by 25 mg of PGF at CIDR removal on Day 30 and a second dose of PGF 6 hours later (Day 30). Artificial insemination was performed at 56 hours (Day 32) after CIDR removal for the ST-56 group and at 72 hours (Day 33) after CIDR removal for the LT-72 group, and all heifers were given GnRH (100 μg, intramuscular) at the time of AI. In experiment 2, Angus cross beef heifers (N = 718) at four locations received a body condition score and an RTS. Within the herd, heifers were randomly assigned to LT-72 and ST-72 protocol groups. The protocol was similar to experiment 1 except that AI was performed at 72 hours after CIDR removal for both LT-72 and ST-72 groups. In experiment 1, no difference in AI pregnancy rates between the LT-72 and ST-56 groups was observed (54.5% [489 of 897] and 55.5% [549 of 990], respectively; P = 0.92) after accounting for the RTS. The AI pregnancy rates for heifers with RTS 3 or less, 4, and 5 were 52.6%, 53.6%, and 59.9%, respectively (P < 0.05). In experiment 2, controlling for the RTS, no difference in AI pregnancy rates was observed between the LT-72 and ST-72 groups, 56.9% (198 of 347) and 57.8% (214 of 371), respectively (P = 0.87). The AI pregnancy rates for heifers with RTS 3 or less, 4, and 5 were 49.3%, 58

  12. Association between Short-Term Exposure to Air Pollution and Dyslipidemias among Type 2 Diabetic Patients in Northwest China: A Population-Based Study

    Directory of Open Access Journals (Sweden)

    Minzhen Wang

    2018-03-01

    Full Text Available Air pollution exposure may play an adverse role in diabetes. However, little data are available directly evaluating the effects of air pollution exposure in blood lipids of which dysfunction has been linked to diabetes or its complications. We aimed to evaluate the association between air pollution and lipids level among type 2 diabetic patients in Northwest China. We performed a population-based study of 3912 type 2 diabetes patients in an ongoing cohort study in China. Both spline and multiple linear regressions analysis were used to examine the association between short-term exposure to PM10, SO2, NO2 and total cholesterol (TC, triglycerides (TG, low-density lipoprotein cholesterol (LDL-C, and high-density lipoprotein cholesterol (HDL-C. By spline analyses, we observed that the relationship between SO2 and HDL-C and LDL-C was shown to be non-linear (p_non-lin-association = 0.0162 and 0.000. An inverted U-shaped non-linear relationship between NO2 and LDL-C was found (p_non-lin-association < 0.0001. A J-shaped non-linear relationship between PM10 and TC, HDL-C (p_non-lin-association = 0.0173, 0.0367 was also revealed. In linear regression analyses, a 10 μg/m3 increment in SO2 was associated with 1.31% (95% CI: 0.40–2.12%, 3.52% (95% CI: 1.07–6.03% and 7.53% (95% CI: 5.98–9.09% increase in TC, TG and LDL-C, respectively. A 10 μg/m3 increment in PM10 was associated with 0.45% (95% CI: 0.08–0.82%, 0.29% (95% CI: 0.10–0.49% and 0.83% (95% CI: 0.21–1.45% increase in TC, HDL-C and LDL-C, respectively. For NO2, an increment of 10 μg/m3 was statistically associated with −3.55% (95% CI: −6.40–0.61% and 39.01% (95% CI: 31.43–47.03% increase in HDL-C and LDL-C. The adverse effects of air pollutants on lipid levels were greater in female and elder people. Further, we found SO2 and NO2 played a more evident role in lipid levels in warm season, while PM10 appeared stronger in cold season. The findings suggest that exposure to air

  13. Short term depression unmasks the ghost frequency.

    Directory of Open Access Journals (Sweden)

    Tjeerd V Olde Scheper

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

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

    Energy Technology Data Exchange (ETDEWEB)

    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. Short-term forecasting of internal migration.

    Science.gov (United States)

    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

  16. The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

    Science.gov (United States)

    Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

    2017-08-01

    The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

  17. Prediction of the effect of atrasentan on renal and heart failure outcomes based on short-term changes in multiple risk markers

    DEFF Research Database (Denmark)

    Schievink, Bauke; de Zeeuw, Dick; Smink, Paul A

    2016-01-01

    from the RADAR/JAPAN study to predict the effect of atrasentan on renal and heart failure outcomes. METHODS: We performed a post-hoc analysis of the RADAR/JAPAN randomized clinical trials in which 211 patients with type-2 diabetes and nephropathy were randomly assigned to atrasentan 0.75 mg/day, 1......BACKGROUND: A recent phase II clinical trial (Reducing Residual Albuminuria in Subjects with Diabetes and Nephropathy with AtRasentan trial and an identical trial in Japan (RADAR/JAPAN)) showed that the endothelin A receptor antagonist atrasentan lowers albuminuria, blood pressure, cholesterol......, hemoglobin, and increases body weight in patients with type 2 diabetes and nephropathy. We previously developed an algorithm, the Parameter Response Efficacy (PRE) score, which translates short-term drug effects into predictions of long-term effects on clinical outcomes. DESIGN: We used the PRE score on data...

  18. Short-term incentive schemes for hospital managers

    Directory of Open Access Journals (Sweden)

    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.

  19. Economics of solar energy: Short term costing

    Science.gov (United States)

    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.

  20. A stepwise approach to the etiologic diagnosis of pleural effusion in respiratory intensive care unit and short-term evaluation of treatment

    Directory of Open Access Journals (Sweden)

    Nilesh J Chinchkar

    2015-01-01

    Full Text Available Background: Pleural effusions in respiratory intensive care unit (RICU are associated with diseases of varied etiologies and often carry a grave prognosis. This prospective study was conducted to establish an etiologic diagnosis in a series of such patients before starting treatment. Materials and Methods: Fifty consecutive patients, diagnosed with pleural effusion on admission or during their stay in RICU, were further investigated by a two-step approach. (1 Etiologic diagnosis was established by sequential clinical history and findings on physical examination, laboratory tests, chest radiograph, CECT/HRCT/PET-CT and pleural fluid analysis. (2 Patients who remained undiagnosed were subjected to fiber-optic bronchoscopy, video-assisted thoracoscopic pleural biopsy, and histopathology. Results: Etiologic diagnosis of pleural effusion was established in 44 (88% Metastases (24%; para-pneumonia (22%; congestive cardiac failure (18%; tuberculosis (14%; hemothorax (4%; trapped lung, renal failure, and liver cirrhosis (2% each. Six patients (12% remained undiagnosed, as the final diagnostic thoracoscopic biopsy could not be performed in five and tissue histopathology findings were inconclusive in one. Out of the 50 patients, 10 died in the hospital; 2 left against medical advice; and 2 were referred to oncology center for further treatment. The remaining 36 patients were clinically stabilized and discharged. During a 3-month follow-up, eight of them were re-hospitalized, of which four died. Conclusions: Pleural effusion in RICU carries a high risk of mortality. Etiologic diagnosis can be established in most cases.

  1. A stepwise approach to the etiologic diagnosis of pleural effusion in respiratory intensive care unit and short-term evaluation of treatment

    Science.gov (United States)

    Chinchkar, Nilesh J; Talwar, Deepak; Jain, Sushil K

    2015-01-01

    Background: Pleural effusions in respiratory intensive care unit (RICU) are associated with diseases of varied etiologies and often carry a grave prognosis. This prospective study was conducted to establish an etiologic diagnosis in a series of such patients before starting treatment. Materials and Methods: Fifty consecutive patients, diagnosed with pleural effusion on admission or during their stay in RICU, were further investigated by a two-step approach. (1) Etiologic diagnosis was established by sequential clinical history and findings on physical examination, laboratory tests, chest radiograph, CECT/HRCT/PET-CT and pleural fluid analysis. (2) Patients who remained undiagnosed were subjected to fiber-optic bronchoscopy, video-assisted thoracoscopic pleural biopsy, and histopathology. Results: Etiologic diagnosis of pleural effusion was established in 44 (88%) Metastases (24%); para-pneumonia (22%); congestive cardiac failure (18%); tuberculosis (14%); hemothorax (4%); trapped lung, renal failure, and liver cirrhosis (2% each). Six patients (12%) remained undiagnosed, as the final diagnostic thoracoscopic biopsy could not be performed in five and tissue histopathology findings were inconclusive in one. Out of the 50 patients, 10 died in the hospital; 2 left against medical advice; and 2 were referred to oncology center for further treatment. The remaining 36 patients were clinically stabilized and discharged. During a 3-month follow-up, eight of them were re-hospitalized, of which four died. Conclusions: Pleural effusion in RICU carries a high risk of mortality. Etiologic diagnosis can be established in most cases. PMID:25814793

  2. Robot-Assisted Thoracoscopic Surgery versus Video-Assisted Thoracoscopic Surgery for Lung Lobectomy: Can a Robotic Approach Improve Short-Term Outcomes and Operative Safety?

    Science.gov (United States)

    Mahieu, Julien; Rinieri, Philippe; Bubenheim, Michael; Calenda, Emile; Melki, Jean; Peillon, Christophe; Baste, Jean-Marc

    2016-06-01

    Background Minimally invasive surgery has been recently recommended for treatment of early-stage non-small cell lung cancer. Despite the recent increase of robotic surgery, the place and potential advantages of the robot in thoracic surgery has not been well defined until now. Methods We reviewed our prospective database for retrospective comparison of our first 28 video-assisted thoracoscopic surgery lobectomies (V group) and our first 28 robotic lobectomies (R group). Results No significant difference was shown in median operative time between the two groups (185 vs. 190 minutes, p = 0.56). Median preincision time was significantly longer in the R group (80 vs. 60 minutes, P < 0.0001). The rate of emergency conversion for uncontrolled bleeding was lower in the R group (one vs. four). Median length of stay was comparable (6 days in the R group vs. 7 days in the V group, p = 0.4) with no significant difference in the rate of postoperative complications (eight Grade I in both groups, four Grade III or IV in the V group vs. six in the R group, according to the Clavien-Dindo classification, p = 0.93). No postoperative cardiac morbidity was observed in the R group. Median drainage time was similar (5 days, p = 0.78), with a rate of prolonged air leak slightly higher in the R group (25 vs. 17.8%, p = 0.74). Conclusion Perioperative outcomes are similar even in the learning period but robotic approach seems to offer more operative safety with fewer conversions for uncontrolled bleeding. Georg Thieme Verlag KG Stuttgart · New York.

  3. Short-term Reproducibility of Computed Tomography-based Lung Density Measurements in Alpha-1 Antitrypsin Deficiency and Smokers with Emphysema

    International Nuclear Information System (INIS)

    Shaker, S.B.; Dirksen, A.; Laursen, L.C.; Maltbaek, N.; Christensen, L.; Sander, U.; Seersholm, N.; Skovgaard, L.T.; Nielsen, L.; Kok-Jensen, A.

    2004-01-01

    Purpose: To study the short-term reproducibility of lung density measurements by multi-slice computed tomography (CT) using three different radiation doses and three reconstruction algorithms. Material and Methods: Twenty-five patients with smoker's emphysema and 25 patients with 1-antitrypsin deficiency underwent 3 scans at 2-week intervals. Low-dose protocol was applied, and images were reconstructed with bone, detail, and soft algorithms. Total lung volume (TLV), 15th percentile density (PD-15), and relative area at -910 Hounsfield units (RA-910) were obtained from the images using Pulmo-CMS software. Reproducibility of PD-15 and RA-910 and the influence of radiation dose, reconstruction algorithm, and type of emphysema were then analysed. Results: The overall coefficient of variation of volume adjusted PD-15 for all combinations of radiation dose and reconstruction algorithm was 3.7%. The overall standard deviation of volume-adjusted RA-910 was 1.7% (corresponding to a coefficient of variation of 6.8%). Radiation dose, reconstruction algorithm, and type of emphysema had no significant influence on the reproducibility of PD-15 and RA-910. However, bone algorithm and very low radiation dose result in overestimation of the extent of emphysema. Conclusion: Lung density measurement by CT is a sensitive marker for quantitating both subtypes of emphysema. A CT-protocol with radiation dose down to 16 mAs and soft or detail reconstruction algorithm is recommended

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

    Directory of Open Access Journals (Sweden)

    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.

  5. The FAST-T approach for operational, real time, short term hydrological forecasting: Results from the Betania Hydropower Reservoir case study

    Science.gov (United States)

    Domínguez, Efraín; Angarita, Hector; Rosmann, Thomas; Mendez, Zulma; Angulo, Gustavo

    2013-04-01

    A viable quantitative hydrological forecasting service is a combination of technological elements, personnel and knowledge, working together to establish a stable operational cycle of forecasts emission, dissemination and assimilation; hence, the process for establishing such system usually requires significant resources and time to reach an adequate development and integration in order to produce forecasts with acceptable levels of performance. Here are presented the results of this process for the recently implemented Operational Forecast Service for the Betania's Hydropower Reservoir - or SPHEB, located at the Upper-Magdalena River Basin (Colombia). The current scope of the SPHEB includes forecasting of water levels and discharge for the three main streams affluent to the reservoir, for lead times between +1 to +57 hours, and +1 to +10 days. The core of the SPHEB is the Flexible, Adaptive, Simple and Transient Time forecasting approach, namely FAST-T. This comprises of a set of data structures, mathematical kernel, distributed computing and network infrastructure designed to provide seamless real-time operational forecast and automatic model adjustment in case of failures in data transmission or assimilation. Among FAST-T main features are: an autonomous evaluation and detection of the most relevant information for the later configuration of forecasting models; an adaptively linearized mathematical kernel, the optimal adaptive linear combination or OALC, which provides a computationally simple and efficient algorithm for real-time applications; and finally, a meta-model catalog, containing prioritized forecast models at given stream conditions. The SPHEB is at present feed by the fraction of hydrological monitoring network installed at the basin that has telemetric capabilities via NOAA-GOES satellites (8 stages, approximately 47%) with data availability of about a 90% at one hour intervals. However, there is a dense network of 'conventional' hydro

  6. Short term memory in echo state networks

    OpenAIRE

    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...

  7. Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm

    Directory of Open Access Journals (Sweden)

    Dongxiao Niu

    2018-04-01

    Full Text Available The continuous increase in energy consumption has made the potential of wind-power generation tremendous. However, the obvious intermittency and randomness of wind speed results in the fluctuation of the output power in a wind farm, seriously affecting the power quality. Therefore, the accurate prediction of wind power in advance can improve the ability of wind-power integration and enhance the reliability of the power system. In this paper, a model of wavelet decomposition (WD and weighted random forest (WRF optimized by the niche immune lion algorithm (NILA-WRF is presented for ultra-short-term wind power prediction. Firstly, the original serials of wind speed and power are decomposed into several sub-serials by WD because the original serials have no obvious day characteristics. Then, the model parameters are set and the model trained with the sub-serials of wind speed and wind power decomposed. Finally, the WD-NILA-WRF model is used to predict the wind power of the relative sub-serials and the result is reconstructed to obtain the final prediction result. The WD-NILA-WRF model combines the advantage of each single model, which uses WD for signal de-noising, and uses the niche immune lion algorithm (NILA to improve the model’s optimization efficiency. In this paper, two empirical analyses are carried out to prove the accuracy of the model, and the experimental results verify the proposed model’s validity and superiority compared with the back propagation neural network (BP neural network, support vector machine (SVM, RF and NILA-RF, indicating that the proposed method is superior in cases influenced by noise and unstable factors, and possesses an excellent generalization ability and robustness.

  8. Effects of short-term heated water-based exercise training on systemic blood pressure in patients with resistant hypertension: a pilot study.

    Science.gov (United States)

    Guimarães, Guilherme V; Cruz, Lais G B; Tavares, Aline C; Dorea, Egidio L; Fernandes-Silva, Miguel M; Bocchi, Edimar A

    2013-12-01

    High blood pressure (BP) increases the risk of cardiovascular diseases, and its control is a clinical challenge. Regular exercise lowers BP in patients with mild-to-moderate hypertension. No data are available on the effects of heated water-based exercise in hypertensive patients. Our objective was to evaluate the effects of heated water-based exercise on BP in patients with resistant hypertension. We tested the effects of 60-min heated water-based exercise training three times per week in 16 patients with resistant hypertension (age 55±6 years). The protocol included walking and callisthenic exercises. All patients underwent 24-h ambulatory blood pressure monitoring (ABPM) before and after a 2-week exercise program in a heated pool. Systolic office BP was reduced from 162 to 144 mmHg (Pexercise training during 24-h ABPM, systolic BP decreased from 135 to 123 mmHg (P=0.02), diastolic BP decreased from 83 to 74 mmHg (P=0.001), daytime systolic BP decreased from 141 to 125 mmHg (P=0.02), diastolic BP decreased from 87 to 77 mmHg (P=0.009), night-time systolic BP decreased from 128 to 118 mmHg (P=0.06), and diastolic BP decreased from 77 to 69 mmHg (P=0.01). In addition, BP cardiovascular load was reduced significantly during the 24-h daytime and night-time period after the heated water-based exercise. Heated water-based exercise reduced office BP and 24-h daytime and night-time ABPM levels. These effects suggest that heated water-based exercise may have a potential as a new therapeutic approach to resistant hypertensive patients.

  9. Fast Weight Long Short-Term Memory

    OpenAIRE

    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...

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

    Science.gov (United States)

    Rolls, Edmund T.; Dempere-Marco, Laura; Deco, Gustavo

    2013-01-01

    Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging. PMID:23613789

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

    Directory of Open Access Journals (Sweden)

    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.

    Science.gov (United States)

    Rolls, Edmund T; Dempere-Marco, Laura; Deco, Gustavo

    2013-01-01

    Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging.

  13. Prognostic relevance of the interaction between short-term, metronome-paced heart rate variability, and inflammation: results from the population-based CARLA cohort study.

    Science.gov (United States)

    Medenwald, Daniel; Swenne, Cees A; Loppnow, Harald; Kors, Jan A; Pietzner, Diana; Tiller, Daniel; Thiery, Joachim; Nuding, Sebastian; Greiser, Karin H; Haerting, Johannes; Werdan, Karl; Kluttig, Alexander

    2017-01-01

    To determine the interaction between HRV and inflammation and their association with cardiovascular/all-cause mortality in the general population. Subjects of the CARLA study (n = 1671; 778 women, 893 men, 45-83 years of age) were observed for an average follow-up period of 8.8 years (226 deaths, 70 cardiovascular deaths). Heart rate variability parameters were calculated from 5-min segments of 20-min resting electrocardiograms. High-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and soluble tumour necrosis factor-alpha receptor type 1 (sTNF-R1) were measured as inflammation parameters. The HRV parameters determined included the standard deviation of normal-to-normal intervals (SDNN), the root-mean-square of successive normal-interval differences (RMSSD), the low- and high-frequency (HF) power, the ratio of both, and non-linear parameters [Poincaré plot (SD1, SD2, SD1/SD2), short-term detrended fluctuation analysis]. We estimated hazard ratios by using covariate-adjusted Cox regression for cardiovascular and all-cause mortality incorporating an interaction term of HRV/inflammation parameters. Relative excess risk due to interactions (RERIs) were computed. We found an interaction effect of sTNF-R1 with SDNN (RERI: 0.5; 99% confidence interval (CI): 0.1-1.0), and a weaker effect with RMSSD (RERI: 0.4; 99% CI: 0.0-0.9) and HF (RERI: 0.4; 99% CI: 0.0-0.9) with respect to cardiovascular mortality on an additive scale after covariate adjustment. Neither IL-6 nor hsCRP showed a significant interaction with the HRV parameters. A change in TNF-α levels or the autonomic nervous system influences the mortality risk through both entities simultaneously. Thus, TNF-α and HRV need to be considered when predicating mortality. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.

  14. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China

    Science.gov (United States)

    Xu, Shiluo; Niu, Ruiqing

    2018-02-01

    Every year, landslides pose huge threats to thousands of people in China, especially those in the Three Gorges area. It is thus necessary to establish an early warning system to help prevent property damage and save peoples' lives. Most of the landslide displacement prediction models that have been proposed are static models. However, landslides are dynamic systems. In this paper, the total accumulative displacement of the Baijiabao landslide is divided into trend and periodic components using empirical mode decomposition. The trend component is predicted using an S-curve estimation, and the total periodic component is predicted using a long short-term memory neural network (LSTM). LSTM is a dynamic model that can remember historical information and apply it to the current output. Six triggering factors are chosen to predict the periodic term using the Pearson cross-correlation coefficient and mutual information. These factors include the cumulative precipitation during the previous month, the cumulative precipitation during a two-month period, the reservoir level during the current month, the change in the reservoir level during the previous month, the cumulative increment of the reservoir level during the current month, and the cumulative displacement during the previous month. When using one-step-ahead prediction, LSTM yields a root mean squared error (RMSE) value of 6.112 mm, while the support vector machine for regression (SVR) and the back-propagation neural network (BP) yield values of 10.686 mm and 8.237 mm, respectively. Meanwhile, the Elman network (Elman) yields an RMSE value of 6.579 mm. In addition, when using multi-step-ahead prediction, LSTM obtains an RMSE value of 8.648 mm, while SVR, BP and the Elman network obtains RSME values of 13.418 mm, 13.014 mm, and 13.370 mm. The predicted results indicate that, to some extent, the dynamic model (LSTM) achieves results that are more accurate than those of the static models (i.e., SVR and BP). LSTM even

  15. 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

    Directory of Open Access Journals (Sweden)

    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

  16. 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.

    Science.gov (United States)

    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

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

    Science.gov (United States)

    Alter, Adam L.; Oppenheimer, Daniel M.

    2006-01-01

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

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

    International Nuclear Information System (INIS)

    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

  19. Brain oscillatory substrates of visual short-term memory capacity.

    Science.gov (United States)

    Sauseng, Paul; Klimesch, Wolfgang; Heise, Kirstin F; Gruber, Walter R; Holz, Elisa; Karim, Ahmed A; Glennon, Mark; Gerloff, Christian; Birbaumer, Niels; Hummel, Friedhelm C

    2009-11-17

    The amount of information that can be stored in visual short-term memory is strictly limited to about four items. Therefore, memory capacity relies not only on the successful retention of relevant information but also on efficient suppression of distracting information, visual attention, and executive functions. However, completely separable neural signatures for these memory capacity-limiting factors remain to be identified. Because of its functional diversity, oscillatory brain activity may offer a utile solution. In the present study, we show that capacity-determining mechanisms, namely retention of relevant information and suppression of distracting information, are based on neural substrates independent of each other: the successful maintenance of relevant material in short-term memory is associated with cross-frequency phase synchronization between theta (rhythmical neural activity around 5 Hz) and gamma (> 50 Hz) oscillations at posterior parietal recording sites. On the other hand, electroencephalographic alpha activity (around 10 Hz) predicts memory capacity based on efficient suppression of irrelevant information in short-term memory. Moreover, repetitive transcranial magnetic stimulation at alpha frequency can modulate short-term memory capacity by influencing the ability to suppress distracting information. Taken together, the current study provides evidence for a double dissociation of brain oscillatory correlates of visual short-term memory capacity.

  20. Short-term natural gas consumption forecasting

    International Nuclear Information System (INIS)

    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

  1. Short term benefits for laparoscopic colorectal resection.

    Science.gov (United States)

    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. Short-term and working memory impairments in aphasia.

    Science.gov (United States)

    Potagas, Constantin; Kasselimis, Dimitrios; Evdokimidis, Ioannis

    2011-08-01

    The aim of the present study is to investigate short-term memory and working memory deficits in aphasics in relation to the severity of their language impairment. Fifty-eight aphasic patients participated in this study. Based on language assessment, an aphasia score was calculated for each patient. Memory was assessed in two modalities, verbal and spatial. Mean scores for all memory tasks were lower than normal. Aphasia score was significantly correlated with performance on all memory tasks. Correlation coefficients for short-term memory and working memory were approximately of the same magnitude. According to our findings, severity of aphasia is related with both verbal and spatial memory deficits. Moreover, while aphasia score correlated with lower scores in both short-term memory and working memory tasks, the lack of substantial difference between corresponding correlation coefficients suggests a possible primary deficit in information retention rather than impairment in working memory. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. Predictors of short-term mortality, cognitive and physical decline in older adults in northwest Russia: a population-based prospective cohort study.

    Science.gov (United States)

    Turusheva, Anna; Frolova, Elena; Hegendoerfer, Eralda; Degryse, Jean-Marie

    2017-08-01

    The classical phenotype, accumulated deficit model and self-report approach of frailty were found not useful in older adults in northwest Russia. More research is needed to identify predictors of adverse outcomes in this population. The aim of this study is to identify predictors of mortality, autonomy and cognitive decline in a population that is characterized by a high cardiovascular morbidity and mortality rate. A population-based prospective cohort study of 611 community-dwelling individuals 65+. Anthropometry, medical history nutritional status were recorded. An evaluation of cognitive, physical and autonomy function, spirometry, and laboratory tests were performed. The total follow-up was 5 years. Multiple imputation, backward stepwise Cox regression analysis, C-statistic, risk reclassification analysis and the bootstrapping techniques were used to analyze the data. We found that the combination of increasing age, male sex, low physical function, low mid-arm muscle area, low forced expiratory volume in 1 s and anemia was associated with mortality for people 65+. The substitution of anemia with anemia + high level of C-reactive protein (hCRP) and the addition of high brain natriuretic peptide (hBNP) levels improved the classification of older persons at risk for mortality. The combination of low physical function, low mid-arm muscle area, low forced expiratory volume in 1 s, anemia with hCRP levels and hBNP identified older persons at a higher risk for mortality. These predictors may be used for the development of a prediction model to detect older people who are at risk for adverse health outcomes in northwest Russia.

  4. Implementing a short-term loyalty program : case: Bosch Lawn & Garden and the Ventum short-term loyalty program

    OpenAIRE

    Logvinova, Veronika

    2015-01-01

    In 2015, one of the Bosch Home and Garden divisions, Bosch Lawn and Garden, has made a strategic decision to adopt a points-based short-term loyalty program called Ventum LG in the German supermarkets and petrol stations. It was decided that the base of this program will be completed Ventum PT short-term loyalty program which was managed by another division, Bosch Power Tools, and proved to be successful. This thesis aims to evaluate the worthiness of the Ventum LG loyalty program for Bosch L...

  5. An Internet- and mobile-based tailored intervention to enhance maintenance of physical activity after cardiac rehabilitation: short-term results of a randomized controlled trial.

    Science.gov (United States)

    Antypas, Konstantinos; Wangberg, Silje C

    2014-03-11

    An increase in physical activity for secondary prevention of cardiovascular disease and cardiac rehabilitation has multiple therapeutic benefits, including decreased mortality. Internet- and mobile-based interventions for physical activity have shown promising results in helping users increase or maintain their level of physical activity in general and specifically in secondary prevention of cardiovascular diseases and cardiac rehabilitation. One component related to the efficacy of these interventions is tailoring of the content to the individual. Our trial assessed the effect of a longitudinally tailored Internet- and mobile-based intervention for physical activity as an extension of a face-to-face cardiac rehabilitation stay. We hypothesized that users of the tailored intervention would maintain their physical activity level better than users of the nontailored version. The study population included adult participants of a cardiac rehabilitation program in Norway with home Internet access and a mobile phone. The participants were randomized in monthly clusters to a tailored or nontailored (control) intervention group. All participants had access to a website with information regarding cardiac rehabilitation, an online discussion forum, and an online activity calendar. Those using the tailored intervention received tailored content based on models of health behavior via the website and mobile fully automated text messages. The main outcome was self-reported level of physical activity, which was obtained using an online international physical activity questionnaire at baseline, at discharge, and at 1 month and 3 months after discharge from the cardiac rehabilitation program. Included in the study were 69 participants. One month after discharge, the tailored intervention group (n=10) had a higher median level of overall physical activity (median 2737.5, IQR 4200.2) than the control group (n=14, median 1650.0, IQR 2443.5), but the difference was not significant

  6. What are the short-term and long-term effects of occupation-focused and occupation-based occupational therapy in the home on older adults' occupational performance?

    DEFF Research Database (Denmark)

    Nielsen, Tove Lise; Petersen, Kirsten Schultz; Nielsen, Claus Vinther

    2016-01-01

    critically appraised 13 of 995 detected papers. Extracted data were presented and summarised descriptively. Results Eight high-quality papers showed that occupation-focused and occupation-based occupational therapy using cognitive, behavioural and environmental strategies may significantly improve......Abstract Title What are the short-term and long-term effects of occupation-focused and occupation-based occupational therapy in the home on older adults’ occupational performance? A systematic review Background There is a lack of evidence-based knowledge about the effectiveness of home......-based occupational therapy for older adults aimed at improving occupational performance by practicing activities and tasks. Aim This review synthesizes and discusses evidence for the effectiveness of occupation-focused and occupation-based occupational therapy for older adults at home. Material and methods Peer...

  7. Evaluating the quality of scenarios of short-term wind power generation

    DEFF Research Database (Denmark)

    Pinson, Pierre; Girard, R.

    2012-01-01

    Scenarios of short-term wind power generation are becoming increasingly popular as input to multi-stage decision-making problems e.g. multivariate stochastic optimization and stochastic programming. The quality of these scenarios is intuitively expected to substantially impact the benets from...... their use in decision-making. So far however, their verication is almost always focused on their marginal distributions for each individual lead time only, thus overlooking their temporal interdependence structure. The shortcomings of such an approach are discussed. Multivariate verication tools, as well...... as diagnostic approaches based on event-based verication are then presented. Their application to the evaluation of various sets of scenarios of short-term wind power generation demonstrates them as valuable discrimination tools....

  8. Real-time energy resources scheduling considering short-term and very short-term wind forecast

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Marco; Sousa, Tiago; Morais, Hugo; Vale, Zita [Polytechnic of Porto (Portugal). GECAD - Knowledge Engineering and Decision Support Research Center

    2012-07-01

    This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process the update of generation and consumption operation and of the storage and electric vehicles storage status are used. Besides the new operation conditions, the most accurate forecast values of wind generation and of consumption using results of short-term and very short-term methods are used. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented. (orig.)

  9. Visual Short-Term Memory Complexity

    DEFF Research Database (Denmark)

    Sørensen, Thomas Alrik

    Several recent studies have explored the nature and limits of visual short-term memory (VSTM) (e.g. Luck & Vogel, 1997). A general VSTM capacity limit of about 3 to 4 letters has been found, thus confirming results from earlier studies (e.g. Cattell, 1885; Sperling, 1960). However, Alvarez...

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

    Science.gov (United States)

    Wang, Xiaoli; Xuan, Yifu; Jarrold, Christopher

    2016-01-01

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

  11. Short term effectiveness and experiences of a peer guided web-based self-management intervention for young adults with juvenile idiopathic arthritis

    NARCIS (Netherlands)

    J. Ammerlaan (Judy); H. van Os-Medendorp (Harmieke); de Boer-Nijhof, N. (Nienke); Scholtus, L. (Lieske); A.A. Kruize (Aike); P.A. van Pelt (Philomine); B.J. Prakken (Berent); J.W.J. Bijlsma (Hans)

    2017-01-01

    textabstractBackground: A web-based self-management intervention guided by peer-trainers was developed to support young adults' self-management in coping with Juvenile Idiopathic Arthritis (JIA). To investigate its effectiveness, a randomized controlled trial (RCT) was conducted. In addition, the

  12. Low-protein vegetarian diet does not have a short-term effect on blood acid–base status but raises oxygen consumption during submaximal cycling

    Directory of Open Access Journals (Sweden)

    Hietavala Enni-Maria

    2012-11-01

    Full Text Available Abstract Background Acid–base balance refers to the equilibrium between acids and bases in the human body. Nutrition may affect acid–base balance and further physical performance. With the help of PRAL (potential renal acid load, a low-protein vegetarian diet (LPVD was designed to enhance the production of bases in body. The aim of this study was to investigate if LPVD has an effect on blood acid–base status and performance during submaximal and maximal aerobic cycling. Methods Nine healthy, recreationally active men (age 23.5 ± 3.4 yr participated in the study and were randomly divided into two groups in a cross-over study design. Group 1 followed LPVD for 4 days and group 2 ate normally (ND before performing a cycle ergometer test. The test included three 10-min stages at 40, 60 and 80% of VO2max. The fourth stage was performed at 100% of VO2max until exhaustion. After 10–16 days, the groups started a second 4-day diet, and at the end performed the similar ergometer test. Venous blood samples were collected at the beginning and at the end of both diet periods and after every stage cycled. Results Diet caused no significant difference in venous blood pH, strong ion difference (SID, total concentration of weak acids (Atot, partial pressure of CO2 (pCO2 or HCO3- at rest or during cycling between LPVD and ND. In the LPVD group, at rest SID significantly increased over the diet period (38.6 ± 1.8 vs. 39.8 ± 0.9, p=0.009. Diet had no significant effect on exercise time to exhaustion, but VO2 was significantly higher at 40, 60 and 80% of VO2max after LPVD compared to ND (2.03 ± 0.25 vs. 1.82 ± 0.21 l/min, p=0.035; 2.86 ± 0.36 vs. 2.52 ± 0.33 l/min, p Conclusion There was no difference in venous blood acid–base status between a 4-day LPVD and ND. VO2 was increased during submaximal cycling after LPVD suggesting that the exercise economy was poorer. This had no further effect on maximal aerobic performance. More studies are needed to

  13. Effects of short-term W-CDMA mobile phone base station exposure on women with or without mobile phone related symptoms.

    Science.gov (United States)

    Furubayashi, Toshiaki; Ushiyama, Akira; Terao, Yasuo; Mizuno, Yoko; Shirasawa, Kei; Pongpaibool, Pornanong; Simba, Ally Y; Wake, Kanako; Nishikawa, Masami; Miyawaki, Kaori; Yasuda, Asako; Uchiyama, Mitsunori; Yamashita, Hitomi Kobayashi; Masuda, Hiroshi; Hirota, Shogo; Takahashi, Miyuki; Okano, Tomoko; Inomata-Terada, Satomi; Sokejima, Shigeru; Maruyama, Eiji; Watanabe, Soichi; Taki, Masao; Ohkubo, Chiyoji; Ugawa, Yoshikazu

    2009-02-01

    To investigate possible health effects of mobile phone use, we conducted a double-blind, cross-over provocation study to confirm whether subjects with mobile phone related symptoms (MPRS) are more susceptible than control subjects to the effect of electromagnetic fields (EMF) emitted from base stations. We sent questionnaires to 5,000 women and obtained 2,472 valid responses from possible candidates; from these, we recruited 11 subjects with MPRS and 43 controls. There were four EMF exposure conditions, each of which lasted 30 min: continuous, intermittent, and sham exposure with and without noise. Subjects were exposed to EMF of 2.14 GHz, 10 V/m (W-CDMA), in a shielded room to simulate whole-body exposure to EMF from base stations, although the exposure strength we used was higher than that commonly received from base stations. We measured several psychological and cognitive parameters pre- and post-exposure, and monitored autonomic functions. Subjects were asked to report on their perception of EMF and level of discomfort during the experiment. The MPRS group did not differ from the controls in their ability to detect exposure to EMF; nevertheless they consistently experienced more discomfort, regardless of whether or not they were actually exposed to EMF, and despite the lack of significant changes in their autonomic functions. Thus, the two groups did not differ in their responses to real or sham EMF exposure according to any psychological, cognitive or autonomic assessment. In conclusion, we found no evidence of any causal link between hypersensitivity symptoms and exposure to EMF from base stations. Copyright 2008 Wiley-Liss, Inc.

  14. Iohexol clearance is superior to creatinine-based renal function estimating equations in detecting short-term renal function decline in chronic heart failure.

    Science.gov (United States)

    Cvan Trobec, Katja; Kerec Kos, Mojca; von Haehling, Stephan; Anker, Stefan D; Macdougall, Iain C; Ponikowski, Piotr; Lainscak, Mitja

    2015-12-01

    To compare the performance of iohexol plasma clearance and creatinine-based renal function estimating equations in monitoring longitudinal renal function changes in chronic heart failure (CHF) patients, and to assess the effects of body composition on the equation performance. Iohexol plasma clearance was measured in 43 CHF patients at baseline and after at least 6 months. Simultaneously, renal function was estimated with five creatinine-based equations (four- and six-variable Modification of Diet in Renal Disease, Cockcroft-Gault, Cockcroft-Gault adjusted for lean body mass, Chronic Kidney Disease Epidemiology Collaboration equation) and body composition was assessed using bioimpedance and dual-energy x-ray absorptiometry. Over a median follow-up of 7.5 months (range 6-17 months), iohexol clearance significantly declined (52.8 vs 44.4 mL/[min ×1.73 m2], P=0.001). This decline was significantly higher in patients receiving mineralocorticoid receptor antagonists at baseline (mean decline -22% of baseline value vs -3%, P=0.037). Mean serum creatinine concentration did not change significantly during follow-up and no creatinine-based renal function estimating equation was able to detect the significant longitudinal decline of renal function determined by iohexol clearance. After accounting for body composition, the accuracy of the equations improved, but not their ability to detect renal function decline. Renal function measured with iohexol plasma clearance showed relevant decline in CHF patients, particularly in those treated with mineralocorticoid receptor antagonists. None of the equations for renal function estimation was able to detect these changes. ClinicalTrials.gov registration number: NCT01829880.

  15. Iohexol clearance is superior to creatinine-based renal function estimating equations in detecting short-term renal function decline in chronic heart failure

    Science.gov (United States)

    Cvan Trobec, Katja; Kerec Kos, Mojca; von Haehling, Stephan; Anker, Stefan D.; Macdougall, Iain C.; Ponikowski, Piotr; Lainscak, Mitja

    2015-01-01

    Aim To compare the performance of iohexol plasma clearance and creatinine-based renal function estimating equations in monitoring longitudinal renal function changes in chronic heart failure (CHF) patients, and to assess the effects of body composition on the equation performance. Methods Iohexol plasma clearance was measured in 43 CHF patients at baseline and after at least 6 months. Simultaneously, renal function was estimated with five creatinine-based equations (four- and six-variable Modification of Diet in Renal Disease, Cockcroft-Gault, Cockcroft-Gault adjusted for lean body mass, Chronic Kidney Disease Epidemiology Collaboration equation) and body composition was assessed using bioimpedance and dual-energy x-ray absorptiometry. Results Over a median follow-up of 7.5 months (range 6-17 months), iohexol clearance significantly declined (52.8 vs 44.4 mL/[min ×1.73 m2], P = 0.001). This decline was significantly higher in patients receiving mineralocorticoid receptor antagonists at baseline (mean decline -22% of baseline value vs -3%, P = 0.037). Mean serum creatinine concentration did not change significantly during follow-up and no creatinine-based renal function estimating equation was able to detect the significant longitudinal decline of renal function determined by iohexol clearance. After accounting for body composition, the accuracy of the equations improved, but not their ability to detect renal function decline. Conclusions Renal function measured with iohexol plasma clearance showed relevant decline in CHF patients, particularly in those treated with mineralocorticoid receptor antagonists. None of the equations for renal function estimation was able to detect these changes. ClinicalTrials.gov registration number NCT01829880 PMID:26718759

  16. The effects of short term intravenous infusion of a soybean based lipid emulsion on some blood constituents in sheep: A preliminary study

    Directory of Open Access Journals (Sweden)

    Hamid Akbari

    2014-04-01

    Full Text Available To evaluate the effect of intravenous infusion of a soybean based lipid emulsion (Lipovenoes 10% on some blood constituents in sheep, a replicated 2 × 2 Latin square design experiment was conducted in four clinically healthy ewes. Lipid emulsion (LE group or normal saline (NS group was infused intravenously at a rate of 0.025 mL kg-1 per min for 6 hr and the concentrations of blood triglyceride, glucose, insulin, calcium, magnesium, phosphorous, sodium and potassium were measured before (baseline and then at timepoints 2, 4, 6, 12 and 24 hr after infusion. Compared to the baseline values and/or NS infusion, LE infusion resulted in a significant increase in the concentrations of triglyceride (p 0.05. In conclusion, intravenous infusion of Lipovenoes temporarily influenced some blood constituents. Increased triglyceride concentrations were associated with an increase in HOMA-IR values indicating a state of insulin resistance. No remarkable adverse effect was observed following LE infusion and lipid based emulsions can be safely used in ruminants not suffering from extensive lipid mobilization.

  17. Effectiveness of a Web-Based Guided Self-help Intervention for Outpatients With a Depressive Disorder: Short-term Results From a Randomized Controlled Trial.

    Science.gov (United States)

    Kenter, Robin Maria Francisca; Cuijpers, Pim; Beekman, Aartjan; van Straten, Annemieke

    2016-03-31

    Research has convincingly demonstrated that symptoms of depression can be reduced through guided Internet-based interventions. However, most of those studies recruited people form the general population. There is insufficient evidence for the effectiveness when delivered in routine clinical practice in outpatient clinics. The objective of this randomized controlled trial was to study patients with a depressive disorder (as defined by the Diagnostic and Statistical Manual of Disorders, fourth edition), as assessed by trained interviewers with the Composite International Diagnostic Interview, who registered for treatment at an outpatient mental health clinic. We aimed to examine the effectiveness of guided Internet-based self-help before starting face-to-face treatment. We recruited 269 outpatients, aged between 18 and 79 years, from outpatient clinics and randomly allocated them to Internet-based problem solving therapy (n=136), with weekly student support, or to a control condition, who remained on the waitlist with a self-help booklet (control group; n=133). Participants in both conditions were allowed to take up face-to-face treatment at the outpatient clinics afterward. We measured the primary outcome, depressive symptoms, by Center for Epidemiological Studies Depression scale (CES-D). Secondary outcome measures were the Hospital Anxiety and Depression Scale Anxiety subscale (HADS-A), Insomnia Severity Index questionnaire (ISI), and EuroQol visual analog scale (EQ-5D VAS). All outcomes were assessed by telephone at posttest (8 weeks after baseline). Posttest measures were completed by 184 (68.4%) participants. We found a moderate to large within-group effect size for both the intervention (d=0.75) and the control (d=0.69) group. However, the between-group effect size was very small (d=0.07), and regression analysis on posttreatment CES-D scores revealed no significant differences between the groups (b=1.134, 95% CI -2.495 to 4.763). The per-protocol analysis (

  18. Short-Term Efficacy of an Innovative Mobile Phone Technology-Based Intervention for Weight Management for Overweight and Obese Adolescents: Pilot Study.

    Science.gov (United States)

    Chen, Jyu-Lin; Guedes, Claudia M; Cooper, Bruce A; Lung, Audrey E

    2017-08-02

    In the United States, approximately one-third of adolescents are now overweight or obese, and one in six is obese. This financial cost and the larger nonfinancial costs of obesity make obesity prevention and management for adolescents imperative for the health of the nation. However, primary care visits are typically brief, and primary care providers may lack adequate resources to help overweight or obese adolescents to manage weight issues. To augment the efficacy of primary care visits for adolescent weight management, mobile phone technology can be used as an adjunct treatment that provides additional opportunities for encouraging improvement in lifestyle, attainment, and maintenance of healthy weight. The purposes of this study were to (1) measure effects of an innovative mobile phone technology-based intervention for overweight and obese adolescents and to (2) examine the intervention's feasibility for use in primary care clinics. The mobile phone-based intervention had three components: use of the Fitbit Flex, participation in an online educational program, and receipt of biweekly text messages during the maintenance phase. A randomized controlled study design was utilized. Data regarding anthropometrics (body mass index [BMI] and waist-to-hip ratio), blood pressure, levels of physical and sedentary activity, diet, and self-efficacy regarding physical activity and diet were collected at baseline and at 3 and 6 months after the baseline assessment. A total of 40 adolescents participated in the study. At the 6-month follow-up visit, compared to participants in the control group, the mobile phone-based intervention participants had significant improvement in BMI (z=-4.37, P=.001), diastolic blood pressure (z=-3.23, P=.001), physical activity days per week (z=2.58, P=.01), TV and computer time (z=-3.34, P=.001), servings of fruits and vegetables per day (z=2.74, P=.006), servings of soda and sweetened drinks (z=-3.19, P=.001), physical activity self-efficacy (z=2

  19. Pigeon visual short-term memory directly compared to primates.

    Science.gov (United States)

    Wright, Anthony A; Elmore, L Caitlin

    2016-02-01

    Three pigeons were trained to remember arrays of 2-6 colored squares and detect which of two squares had changed color to test their visual short-term memory. Procedures (e.g., stimuli, displays, viewing times, delays) were similar to those used to test monkeys and humans. Following extensive training, pigeons performed slightly better than similarly trained monkeys, but both animal species were considerably less accurate than humans with the same array sizes (2, 4 and 6 items). Pigeons and monkeys showed calculated memory capacities of one item or less, whereas humans showed a memory capacity of 2.5 items. Despite the differences in calculated memory capacities, the pigeons' memory results, like those from monkeys and humans, were all well characterized by an inverse power-law function fit to d' values for the five display sizes. This characterization provides a simple, straightforward summary of the fundamental processing of visual short-term memory (how visual short-term memory declines with memory load) that emphasizes species similarities based upon similar functional relationships. By closely matching pigeon testing parameters to those of monkeys and humans, these similar functional relationships suggest similar underlying processes of visual short-term memory in pigeons, monkeys and humans. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Retention interval affects visual short-term memory encoding.

    Science.gov (United States)

    Bankó, Eva M; Vidnyánszky, Zoltán

    2010-03-01

    Humans can efficiently store fine-detailed facial emotional information in visual short-term memory for several seconds. However, an unresolved question is whether the same neural mechanisms underlie high-fidelity short-term memory for emotional expressions at different retention intervals. Here we show that retention interval affects the neural processes of short-term memory encoding using a delayed facial emotion discrimination task. The early sensory P100 component of the event-related potentials (ERP) was larger in the 1-s interstimulus interval (ISI) condition than in the 6-s ISI condition, whereas the face-specific N170 component was larger in the longer ISI condition. Furthermore, the memory-related late P3b component of the ERP responses was also modulated by retention interval: it was reduced in the 1-s ISI as compared with the 6-s condition. The present findings cannot be explained based on differences in sensory processing demands or overall task difficulty because there was no difference in the stimulus information and subjects' performance between the two different ISI conditions. These results reveal that encoding processes underlying high-precision short-term memory for facial emotional expressions are modulated depending on whether information has to be stored for one or for several seconds.

  1. Analysis of recurrent neural networks for short-term energy load forecasting

    Science.gov (United States)

    Di Persio, Luca; Honchar, Oleksandr

    2017-11-01

    Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.

  2. Outsmart HPV: Acceptability and short-term effects of a web-based HPV vaccination intervention for young adult gay and bisexual men.

    Science.gov (United States)

    McRee, Annie-Laurie; Shoben, Abigail; Bauermeister, Jose A; Katz, Mira L; Paskett, Electra D; Reiter, Paul L

    2018-01-10

    Effective interventions to promote human papillomavirus (HPV) vaccination are needed, particularly among populations at increased risk of HPV-related disease. We developed and pilot tested a web-based intervention, Outsmart HPV, to promote HPV vaccination among young gay and bisexual men (YGBM). In 2016, we recruited a national sample (n = 150) of YGBM ages 18-25 in the United States who had not received any doses of HPV vaccine. Participants were randomized to receive either standard HPV vaccination information (control) or population-targeted, individually-tailored content (Outsmart HPV intervention). We assessed between group differences in HPV vaccination attitudes and beliefs immediately following the intervention using multiple linear regression. There were no differences in HPV vaccination attitudes, beliefs and intentions between groups at baseline. Compared to participants in the control group, intervention participants reported: greater perception that men who have sex with men are at higher risk for anal cancer relative to other men (b = 0.34); greater HPV vaccination self-efficacy (b = 0.15); and fewer perceived harms of HPV vaccine (b = -0.34) on posttest surveys (all p HPV intervention (all > 4.4 on a 5-point scale). Findings from this study provide preliminary support for a brief, tailored web-based intervention in improving HPV vaccination attitudes and beliefs among YGBM. An important next step is to determine the effects of Outsmart HPV on HPV vaccine uptake. ClinicalTrials.gov identifier NCT02835755. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Short term and dosage influences of palm based medium- and long-chain triacylglycerols on body fat and blood parameters in C57BL/6J mice.

    Science.gov (United States)

    Lee, Yee-Ying; Tang, Teck-Kim; Ab Karim, Nur Azwani; Alitheen, Noorjahan Banu Mohamed; Lai, Oi-Ming

    2014-01-01

    Structured lipid medium- and long-chain triacylglycerols (MLCT) are claimed to be able to manage obesity. The present study investigated the body fat influence of enzymatically interesterifed palm-based medium- and long-chain triacylglycerols (P-MLCT) on diet-induced obesity (DIO) C57BL/6J mice compared with commercial MLCT oil (C-MLCT) and a control, which was the non enzymatically modified palm kernel and palm oil blend (PKO-PO blend). It also investigated the low fat and high fat effects of P-MLCT. DIO C57BL/6J mice were fed ad libitum with low fat (7%) and high fat (30%) experimental diets for 8 weeks before being sacrificed to obtain blood serum for analysis. From the results, there is a trend that P-MLCT fed mice were found to have the lowest body weight, body weight gain, total fat pad accumulation (perirenal, retroperitoneal, epididymal and mesenteric), total triglyceride levels and efficiency in controlling blood glucose level, compared with C-MLCT and the PKO-PO blend in both low fat and high fat diets. Nevertheless, the PKO-PO blend and P-MLCT caused significantly (P < 0.05) higher total cholesterol levels compared to C-MLCT. P-MLCT present in low fat and high fat dosage were shown to be able to suppress body fat accumulation. This effect is more prominent with the low fat dosage.

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

    Science.gov (United States)

    Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

    2016-06-01

    To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

  5. Human procollagen type I surface-modified PHB-based non-woven textile scaffolds for cell growth: preparation and short-term biological tests

    International Nuclear Information System (INIS)

    Kawalec, Michał; Sobota, Michał; Kurcok, Piotr; Sitkowska, Anna; Sieroń, Aleksander L; Komar, Patrycja

    2014-01-01

    3D fine porous structures obtained by electrospinning a poly[(R,S)-3-hydroxybutyrate] (aPHB)/ poly[(R)-3-hydroxybutyrate] (PHB) (85/15 w/w) blend were successfully modified with human procollagen type I by simple immersion of the polyester scaffold in an aqueous solution of the protein. Effective modification of the scaffold with human procollagen I was confirmed by an immunodetection test, which revealed the presence of the procollagen type I as an outer layer even on inner structures of the porous matrixes. Biological tests of 3D fabrics made of the PHB blend provide support for the adhesion and proliferation of human fibroblasts, while their modification with procollagen type I increased the biocompatibility of the final scaffolds significantly, as shown by the notable increase in the number of attached cells during the early hours of their incubation. Based on these findings, human procollagen type I surface-modified aPHB/PHB scaffolds should be considered a promising material in regenerative medicine. (paper)

  6. Short-term effect of add on bell pepper (Capsicum annuum var. grossum) juice with integrated approach of yoga therapy on blood glucose levels and cardiovascular functions in patients with type 2 diabetes mellitus: A randomized controlled study.

    Science.gov (United States)

    Nagasukeerthi, Padakandla; Mooventhan, A; Manjunath, N K

    2017-10-01

    Type 2 diabetes mellitus (T2DM) is a major global health problem. Though various studies have reported the beneficial effect of Yoga in patient with T2DM, there is a lack of study in combination with bell pepper and yoga. Hence, the present study aims at evaluating short-term effect of add on bell pepper juice with integrated approach of yoga therapy (IAYT) on blood glucose levels and cardiovascular variables in patients with T2DM. Fifty T2DM subjects with the age varied from 34 to 69-years were recruited and randomly divided into either study group or control group. The study group received 100-ml of bell pepper juice (twice/day) along with IAYT while the control group received only IAYT for 4-consecutive days. Baseline and post-test assessments were taken before and after the intervention. Statistical analysis was performed using statistical package for the social sciences, version-16. Results of this study showed no significant difference in overall (fasting and post prandial) blood glucose level in the study group compared with control group. However, a significant reduction in Post prandial blood glucose (PPBG), systolic blood pressure (SBP), pulse pressure (PP), rate pressure product (RPP) and Double product (Do-P) was observed in the study group compared with control group. Results of this study suggest that though an addition of 100-ml of bell pepper juice (twice/day) along with IAYT is not more effective in reducing fasting blood glucose, it may be more effective in reducing PPBG, SBP, PP, RPP and Do-P than IAYT alone. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. The effect of mineral-based alkaline water on hydration status and the metabolic response to short-term anaerobic exercise

    Directory of Open Access Journals (Sweden)

    Jakub Chycki

    2017-04-01

    Full Text Available Previously it was demonstrated that mineralization and alkalization properties of mineral water are important factors influencing acid-base balance and hydration in athletes. The purpose of this study was to investigate the effects of drinking different types of water on urine pH, specific urine gravity, and post-exercise lactate utilization in response to strenuous exercise. Thirty-six male soccer players were divided into three intervention groups, consuming around 4.0 l/day of different types of water for 7 days: HM (n=12; highly mineralized water, LM (n=12; low mineralized water, and CON (n=12; table water. The athletes performed an exercise protocol on two occasions (before and after intervention. The exercise protocol consisted of 5 bouts of intensive 60-s (120% VO2max cycling separated by 60 s of passive rest. Body composition, urinalysis and lactate concentration were evaluated – before (t0, immediately after (t1, 5’ (t2, and 30’ (t3 after exercise. Total body water and its active transport (TBW – total body water / ICW – intracellular water / ECW – extracellular water showed no significant differences in all groups, at both occasions. In the post-hydration state we found a significant decrease of specific urine gravity in HM (1021±4.2 vs 1015±3.8 g/L and LM (1022±3.1 vs 1008±4.2 g/L. We also found a significant increase of pH and lactate utilization rate in LM. In conclusion, the athletes hydrated with alkaline, low mineralized water demonstrated favourable changes in hydration status in response to high-intensity interval exercise with a significant decrease of specific urine gravity, increased urine pH and more efficient utilization of lactate after supramaximal exercise.

  8. Short-term Memory of Deep RNN

    OpenAIRE

    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 ...

  9. Short-term LNG-markets

    International Nuclear Information System (INIS)

    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)

  10. SHORT-TERM AND LONG-TERM WATER LEVEL PREDICTION AT ONE RIVER MEASUREMENT LOCATION

    Directory of Open Access Journals (Sweden)

    Rudolf Scitovski

    2012-12-01

    Full Text Available Global hydrological cycles mainly depend on climate changes whose occurrence is predominantly triggered by solar and terrestrial influence, and the knowledge of the high water regime is widely applied in hydrology. Regular monitoring and studying of river water level behavior is important from several perspectives. On the basis of the given data, by using modifications of general approaches known from literature, especially from investigation in hydrology, the problem of long- and short-term water level forecast at one river measurement location is considered in the paper. Long-term forecasting is considered as the problem of investigating the periodicity of water level behavior by using linear-trigonometric regression and short-term forecasting is based on the modification of the nearest neighbor method. The proposed methods are tested on data referring to the Drava River level by Donji Miholjac, Croatia, in the period between the beginning of 1900 and the end of 2012.

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

    KAUST Repository

    Zhu, Xinxin

    2012-04-01

    The emphasis on renewable energy and concerns about the environment have led to large-scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High-quality short-term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented. © 2012 The Authors. International Statistical Review © 2012 International Statistical Institute.

  12. A neuromorphic circuit mimicking biological short-term memory.

    Science.gov (United States)

    Barzegarjalali, Saeid; Parker, Alice C

    2016-08-01

    Research shows that the way we remember things for a few seconds is a different mechanism from the way we remember things for a longer time. Short-term memory is based on persistently firing neurons, whereas storing information for a longer time is based on strengthening the synapses or even forming new neural connections. Information about location and appearance of an object is segregated and processed by separate neurons. Furthermore neurons can continue firing using different mechanisms. Here, we have designed a biomimetic neuromorphic circuit that mimics short-term memory by firing neurons, using biological mechanisms to remember location and shape of an object. Our neuromorphic circuit has a hybrid architecture. Neurons are designed with CMOS 45nm technology and synapses are designed with carbon nanotubes (CNT).

  13. Implementation of short-term prediction

    Energy Technology Data Exchange (ETDEWEB)

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

    1999-03-01

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

  14. Is visual short-term memory depthful?

    Science.gov (United States)

    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.

  15. Robust Short-Term Memory without Synaptic Learning

    OpenAIRE

    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...

  16. INCAP - Applying short-term flexibility to control inventories

    OpenAIRE

    Lödding , Hermann; Lohmann , Steffen

    2011-01-01

    Abstract Inventory Based Capacity Control (INCAP) is a very simple method that allows inventory levels to be effectively controlled by using short-term capacity flexibility in make-to-stock settings. Moreover, INCAP can be used for finished goods inventories as well as for semi-finished goods inventories. The basic idea is to define upper and lower inventory limits and to adjust capacities if the inventory level reaches either limit. Should the inventory fall below the lower limit,...

  17. Short term load forecasting using neuro-fuzzy networks

    Energy Technology Data Exchange (ETDEWEB)

    Hoffman, M.; Hassan, A. [South Dakota School of Mines and Technology, Rapid City, SD (United States); Martinez, D. [Black Hills Power and Light, Rapid City, SD (United States)

    2005-07-01

    Details of a neuro-fuzzy network-based short term load forecasting system for power utilities were presented. The fuzzy logic controller was used to fuzzify inputs representing historical temperature and load curves. The fuzzified inputs were then used to develop the fuzzy rules matrix. Output membership function values were determined by evaluating the fuzzified inputs with the fuzzy rules. Output membership function values were used as inputs for the neural network portion of the system. The training process used a back propagation gradient descent algorithm to adjust the weight values of the neural network in order to reduce the error between the neural network output and the desired output. The neural network was then used to predict future load values. Sample data were taken from a local power company's daily load curve to validate the system. A 10 per cent forecast error was introduced in the temperature values to determine the effect on load prediction. Results of the study suggest that the combined use of fuzzy logic and neural networks provide greater accuracy than studies where either approach is used alone. 6 refs., 6 figs.

  18. Short-term memory in networks of dissociated cortical neurons.

    Science.gov (United States)

    Dranias, Mark R; Ju, Han; Rajaram, Ezhilarasan; VanDongen, Antonius M J

    2013-01-30

    Short-term memory refers to the ability to store small amounts of stimulus-specific information for a short period of time. It is supported by both fading and hidden memory processes. Fading memory relies on recurrent activity patterns in a neuronal network, whereas hidden memory is encoded using synaptic mechanisms, such as facilitation, which persist even when neurons fall silent. We have used a novel computational and optogenetic approach to investigate whether these same memory processes hypothesized to support pattern recognition and short-term memory in vivo, exist in vitro. Electrophysiological activity was recorded from primary cultures of dissociated rat cortical neurons plated on multielectrode arrays. Cultures were transfected with ChannelRhodopsin-2 and optically stimulated using random dot stimuli. The pattern of neuronal activity resulting from this stimulation was analyzed using classification algorithms that enabled the identification of stimulus-specific memories. Fading memories for different stimuli, encoded in ongoing neural activity, persisted and could be distinguished from each other for as long as 1 s after stimulation was terminated. Hidden memories were detected by altered responses of neurons to additional stimulation, and this effect persisted longer than 1 s. Interestingly, network bursts seem to eliminate hidden memories. These results are similar to those that have been reported from similar experiments in vivo and demonstrate that mechanisms of information processing and short-term memory can be studied using cultured neuronal networks, thereby setting the stage for therapeutic applications using this platform.

  19. The epidemiology of long- and short-term cancer survivors

    DEFF Research Database (Denmark)

    Jarlbæk, Lene; Christensen, Linda; Bruera, Eduardo

    2014-01-01

    Introduction. In this study, we present data from a population-based cohort of incident cancer patients separated in long- and short-term survivors. Our aim was to procure denominators for use in the planning of rehabilitation and palliative care programs. Material and methods. A registry......-linkage cohort study. All cancer patients, diagnosed from 1993 to 2003 from a 470 000 large population, were followed individually from diagnosis to death or until 31 December 2008. Long-term survivors lived five years or more after the time of the cancer diagnosis (TOCD). Short-term survivors died less than...... and sex. Two-year crude cancer survival seems as a clinically relevant cut point for characterizing potential "denominators" for rehabilitation or palliative care programs. From this cohort of incident cancer patients, and using two-year survival as a cut point, it could be estimated that 54% would...

  20. Short-term energy outlook, July 1998

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-07-01

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

  1. Short-term energy outlook, January 1999

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1999-01-01

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

  2. Chances of short-term cooling trends over Canada for the next decades

    Science.gov (United States)

    Grenier, Patrick; de Elia, Ramon; Chaumont, Diane

    2014-05-01

    As climate services continue to develop in Quebec, Canada, an increasing number of requests are made for providing information relevant for the near term. As a response, one approach has been to consider short-term cooling trends as a basis for climate products. This project comprises different aspects: technical steps, knowledge transfer, and societal use. Each step does represent a different challenge. The technical part, i.e. producing probabilistic distributions of short-term temperature trends, involves relatively complex scenario construction methods including bias-related post-processing, and access to wide simulation and observation databases. Calculations are performed on 60 CMIP5-based scenarios on a grid covering Canada during the period 2006-2035, and for 5, 10, 15, 20 and 25-year trend durations. Knowledge transfer implies overcoming misinterpretation, given that probabilistic projections based on simulation ensembles are not perfectly related to real-Earth possible outcomes. Finally, societal use of this information remains the biggest challenge. On the one hand, users clearly state their interest in near-term relevant information, and intuitively it seems clear that short-term cooling trends embedded within the long-term warming path should be considered in adaptation plans, for avoiding over-adaptation. On the other hand, the exact way of incorporating such information within a decision-making process has proven not to be obvious. Irrespective of that, the study and communication of short-term cooling chances is necessary for preventing decision-makers to infer from the eventual occurrence of such a trend that global warming isn't happening. The presentation will discuss the three aspects aforementioned.

  3. Reinsurance by short-term reinsurers in South Africa

    Directory of Open Access Journals (Sweden)

    Fernhout, C. L. R.

    2016-02-01

    Full Text Available The short-term reinsurance process usually involves three parties, namely the insurer, the reinsurer and the original policyholder, as the insurer cedes a part of the covered risk of the policyholder to the reinsurer. This research however addresses the perceptions of reinsurers regarding their reinsurance activities, where the reinsurer sells reinsurance to other insurance entities (viz. insurers and reinsurers, as well as buys reinsurance from other insurance entities. The crux of short-term reinsurance is therefore mutually loss sharing between the various insurance entities. The objective of this research focuses on the improvement of financial decision-making regarding the reinsurance operations of the reinsurers. To achieve this objective a literature study was undertaken to provide adequate background to compile a questionnaire for the empirical survey. The primary study embodies the perceptions of the South African short-term reinsurers regarding the following aspects: the various reasons why reinsurance occurs; the contracts / methods of reinsurance; the bases / forms of reinsurance; and the factors which determine the retention levels of a reinsurer. South Africa is classified as a developing economy, is a member of the BRICS countries and has an emerging market economy. The empirical results should therefore also be valuable to other countries which are classified similarly

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

    Science.gov (United States)

    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.

  5. Short Term Airing by Natural Ventilation

    DEFF Research Database (Denmark)

    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...

  6. Short-term plasticity in auditory cognition.

    Science.gov (United States)

    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.

  7. Short-Term fo F2 Forecast: Present Day State of Art

    Science.gov (United States)

    Mikhailov, A. V.; Depuev, V. H.; Depueva, A. H.

    An analysis of the F2-layer short-term forecast problem has been done. Both objective and methodological problems prevent us from a deliberate F2-layer forecast issuing at present. An empirical approach based on statistical methods may be recommended for practical use. A forecast method based on a new aeronomic index (a proxy) AI has been proposed and tested over selected 64 severe storm events. The method provides an acceptable prediction accuracy both for strongly disturbed and quiet conditions. The problems with the prediction of the F2-layer quiet-time disturbances as well as some other unsolved problems are discussed

  8. Evaluation of Short Term Memory Span Function In Children

    OpenAIRE

    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 ...

  9. Short-term change detection for UAV video

    Science.gov (United States)

    Saur, Günter; Krüger, Wolfgang

    2012-11-01

    In the last years, there has been an increased use of unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. An important application in this context is change detection in UAV video data. Here we address short-term change detection, in which the time between observations ranges from several minutes to a few hours. We distinguish this task from video motion detection (shorter time scale) and from long-term change detection, based on time series of still images taken between several days, weeks, or even years. Examples for relevant changes we are looking for are recently parked or moved vehicles. As a pre-requisite, a precise image-to-image registration is needed. Images are selected on the basis of the geo-coordinates of the sensor's footprint and with respect to a certain minimal overlap. The automatic imagebased fine-registration adjusts the image pair to a common geometry by using a robust matching approach to handle outliers. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed length of shadows, and compression or transmission artifacts. To detect changes in image pairs we analyzed image differencing, local image correlation, and a transformation-based approach (multivariate alteration detection). As input we used color and gradient magnitude images. To cope with local misalignment of image structures we extended the approaches by a local neighborhood search. The algorithms are applied to several examples covering both urban and rural scenes. The local neighborhood search in combination with intensity and gradient magnitude differencing clearly improved the results. Extended image differencing performed better than both the correlation based approach and the multivariate alternation detection. The algorithms are adapted to be used in semi-automatic workflows for the ABUL video exploitation system of Fraunhofer

  10. In Search of Decay in Verbal Short-Term Memory

    Science.gov (United States)

    Berman, Marc G.; Jonides, John; Lewis, Richard L.

    2009-01-01

    Is forgetting in the short term due to decay with the mere passage of time, interference from other memoranda, or both? Past research on short-term memory has revealed some evidence for decay and a plethora of evidence showing that short-term memory is worsened by interference. However, none of these studies has directly contrasted decay and…

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

    Directory of Open Access Journals (Sweden)

    E. Faghihnia

    2014-01-01

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

  12. Comparison of the Impact of Wikipedia, UpToDate, and a Digital Textbook on Short-Term Knowledge Acquisition Among Medical Students: Randomized Controlled Trial of Three Web-Based Resources.

    Science.gov (United States)

    Scaffidi, Michael A; Khan, Rishad; Wang, Christopher; Keren, Daniela; Tsui, Cindy; Garg, Ankit; Brar, Simarjeet; Valoo, Kamesha; Bonert, Michael; de Wolff, Jacob F; Heilman, James; Grover, Samir C

    2017-10-31

    Web-based resources are commonly used by medical students to supplement curricular material. Three commonly used resources are UpToDate (Wolters Kluwer Inc), digital textbooks, and Wikipedia; there are concerns, however, regarding Wikipedia's reliability and accuracy. The aim of this study was to evaluate the impact of Wikipedia use on medical students' short-term knowledge acquisition compared with UpToDate and a digital textbook. This was a prospective, nonblinded, three-arm randomized trial. The study was conducted from April 2014 to December 2016. Preclerkship medical students were recruited from four Canadian medical schools. Convenience sampling was used to recruit participants through word of mouth, social media, and email. Participants must have been enrolled in their first or second year of medical school at a Canadian medical school. After recruitment, participants were randomized to one of the three Web-based resources: Wikipedia, UpToDate, or a digital textbook. During testing, participants first completed a multiple-choice questionnaire (MCQ) of 25 questions emulating a Canadian medical licensing examination. During the MCQ, participants took notes on topics to research. Then, participants researched topics and took written notes using their assigned resource. They completed the same MCQ again while referencing their notes. Participants also rated the importance and availability of five factors pertinent to Web-based resources. The primary outcome measure was knowledge acquisition as measured by posttest scores. The secondary outcome measures were participants' perceptions of importance and availability of each resource factor. A total of 116 medical students were recruited. Analysis of variance of the MCQ scores demonstrated a significant interaction between time and group effects (P<.001, η g 2 =0.03), with the Wikipedia group scoring higher on the MCQ posttest compared with the textbook group (P<.001, d=0.86). Access to hyperlinks, search

  13. Short-term perceptual learning in visual conjunction search.

    Science.gov (United States)

    Su, Yuling; Lai, Yunpeng; Huang, Wanyi; Tan, Wei; Qu, Zhe; Ding, Yulong

    2014-08-01

    Although some studies showed that training can improve the ability of cross-dimension conjunction search, less is known about the underlying mechanism. Specifically, it remains unclear whether training of visual conjunction search can successfully bind different features of separated dimensions into a new function unit at early stages of visual processing. In the present study, we utilized stimulus specificity and generalization to provide a new approach to investigate the mechanisms underlying perceptual learning (PL) in visual conjunction search. Five experiments consistently showed that after 40 to 50 min of training of color-shape/orientation conjunction search, the ability to search for a certain conjunction target improved significantly and the learning effects did not transfer to a new target that differed from the trained target in both color and shape/orientation features. However, the learning effects were not strictly specific. In color-shape conjunction search, although the learning effect could not transfer to a same-shape different-color target, it almost completely transferred to a same-color different-shape target. In color-orientation conjunction search, the learning effect partly transferred to a new target that shared same color or same orientation with the trained target. Moreover, the sum of transfer effects for the same color target and the same orientation target in color-orientation conjunction search was algebraically equivalent to the learning effect for trained target, showing an additive transfer effect. The different transfer patterns in color-shape and color-orientation conjunction search learning might reflect the different complexity and discriminability between feature dimensions. These results suggested a feature-based attention enhancement mechanism rather than a unitization mechanism underlying the short-term PL of color-shape/orientation conjunction search.

  14. Short-Term Saved Leave Scheme

    CERN Multimedia

    2007-01-01

    As announced at the meeting of the Standing Concertation Committee (SCC) on 26 June 2007 and in http://Bulletin No. 28/2007, the existing Saved Leave Scheme will be discontinued as of 31 December 2007. Staff participating in the Scheme will shortly receive a contract amendment stipulating the end of financial contributions compensated by save leave. Leave already accumulated on saved leave accounts can continue to be taken in accordance with the rules applicable to the current scheme. A new system of saved leave will enter into force on 1 January 2008 and will be the subject of a new implementation procedure entitled "Short-term saved leave scheme" dated 1 January 2008. At its meeting on 4 December 2007, the SCC agreed to recommend the Director-General to approve this procedure, which can be consulted on the HR Department’s website at the following address: https://cern.ch/hr-services/services-Ben/sls_shortterm.asp All staff wishing to participate in the new scheme a...

  15. Short-Term Saved Leave Scheme

    CERN Multimedia

    HR Department

    2007-01-01

    As announced at the meeting of the Standing Concertation Committee (SCC) on 26 June 2007 and in http://Bulletin No. 28/2007, the existing Saved Leave Scheme will be discontinued as of 31 December 2007. Staff participating in the Scheme will shortly receive a contract amendment stipulating the end of financial contributions compensated by save leave. Leave already accumulated on saved leave accounts can continue to be taken in accordance with the rules applicable to the current scheme. A new system of saved leave will enter into force on 1 January 2008 and will be the subject of a new im-plementation procedure entitled "Short-term saved leave scheme" dated 1 January 2008. At its meeting on 4 December 2007, the SCC agreed to recommend the Director-General to approve this procedure, which can be consulted on the HR Department’s website at the following address: https://cern.ch/hr-services/services-Ben/sls_shortterm.asp All staff wishing to participate in the new scheme ...

  16. Short-term energy outlook, April 1999

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1999-04-01

    The forecast period for this issue of the Outlook extends from April 1999 through December 2000. Data values for the first quarter 1999, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the April 1999 version of the Short-Term Integrated forecasting system (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 25 figs., 19 tabs.

  17. Continuity of Landsat observations: Short term considerations

    Science.gov (United States)

    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.

  18. Why do short term workers have high mortality?

    DEFF Research Database (Denmark)

    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...

  19. A short-term risk-benefit analysis of occasional and regular use of low-dose aspirin in primary prevention of vascular diseases: a nationwide population-based study.

    Science.gov (United States)

    Wu, I-Chen; Hsieh, Hui-Min; Wu, Ming-Tsang

    2015-01-09

    To calculate the short-term risk-benefit effect of occasional and regular use of low-dose aspirin (≤100 mg/day) in primary prevention. Two retrospective cohort studies. Taiwan. 63 788 and 24 910 patients of two nationwide population-based studies were examined. Two databases of 1 000 000 patients were randomly sampled from data of Taiwan's National Health Insurance (NHI) for years 1997-2000 (NHI 2000) and 2005 (NHI 2005). In NHI 2000, 63 788 patients 30-95 years of age were found not to have previously been prescribed aspirin before 1 January 2000, but to have first been prescribed low-dose aspirin after that date. They were also found to be at risk of first hospitalisation for any major vascular diseases including haemorrhage (major gastrointestinal haemorrhage or cerebral haemorrhage) and ischaemia (acute myocardial infarction or ischaemic stroke) after their first prescription. We also applied it to NHI 2005, and the number of eligible patients was 24 910. Patients prescribed low-dose aspirin for risk. Vascular diseases. In NHI 2000, the overall unadjusted rates of haemorrhage and ischaemia were 0.09% and 0.21%, respectively, for occasional users and 0.32% and 2.30%, respectively, for regular users. Adjusted net clinical risk of low-dose aspirin use between the two groups was 2.24% (95% CI 2.03% to 2.48%; ppreventing major vascular diseases in primary prevention. Prescribing regular low-dose aspirin for primary prevention should be done with caution. Future studies should explore the risk-benefit effect of long-term low-dose aspirin use in primary prevention. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

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

    Directory of Open Access Journals (Sweden)

    Suseelatha Annamareddi

    2013-01-01

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

  1. Short-term bioconcentration studies of Np in freshwater biota

    International Nuclear Information System (INIS)

    Poston, T.M.; Klopfer, D.C.; Simmons, M.A.

    1990-01-01

    Short-term laboratory exposures were conducted to determine the potential accumulation of Np in aquatic organisms. Concentration factors were highest in green algae. Daphnia magna, a filter-feeding crustacean, accumulated Np at levels one order of magnitude greater than the amphipod Gammarus sp., an omnivorous substrate feeder. Accumulation of Np in juvenile rainbow trout (Oncorhynchus mykiss) was highest in carcass (generally greater than 78% of the total body burden) and lowest in fillets. Recommended concentration factors for Np, based on fresh weight, were 300 for green algae, 100 for filter-feeding invertebrates, for nonfilter-feeding invertebrates, 10 for whole fish, and one for fish flesh

  2. Evaluating the quality of scenarios of short-term wind power generation

    International Nuclear Information System (INIS)

    Pinson, P.; Girard, R.

    2012-01-01

    Highlights: ► Presentation of the desirable properties of wind power generation scenarios. ► Description of various evaluation frameworks (univariate, multivariate, diagnostic). ► Highlighting of the properties of current approaches to scenario generation. ► Guidelines for future evaluation/benchmark exercises. -- Abstract: Scenarios of short-term wind power generation are becoming increasingly popular as input to multistage decision-making problems e.g. multivariate stochastic optimization and stochastic programming. The quality of these scenarios is intuitively expected to substantially impact the benefits from their use in decision-making. So far however, their verification is almost always focused on their marginal distributions for each individual lead time only, thus overlooking their temporal interdependence structure. The shortcomings of such an approach are discussed. Multivariate verification tools, as well as diagnostic approaches based on event-based verification are then presented. Their application to the evaluation of various sets of scenarios of short-term wind power generation demonstrates them as valuable discrimination tools.

  3. The IEA Model of Short-term Energy Security

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2011-07-01

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

  4. Short-term effect of two education methods on oral health among hearing impairment children

    Directory of Open Access Journals (Sweden)

    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.

  5. Absence of gender disparity in short-term clinical outcomes in patients with acute ST-segment elevation myocardial infarction undergoing sirolimus-eluting stent based primary coronary intervention: a report from Shanghai Acute Coronary Event (SACE) Registry.

    Science.gov (United States)

    Zhang, Qi; Qiu, Jian-Ping; Zhang, Rui-Yan; Li, Yi-Gang; He, Ben; Jin, Hui-Gen; Zhang, Jun-Feng; Wang, Xiao-Long; Jiang, Li; Liao, Min-Lei; Hu, Jian; Shen, Wei-Feng

    2010-04-05

    Randomized, controlled trials have demonstrated the superiority of sirolimus-eluting stent (SES) implantation during primary percutaneous coronary intervention (PCI), as opposed to bare-metal stents, in patients with ST-elevation myocardial infarction (STEMI). This study aimed to test the hypothesis that clinical benefits of SES treatment were independent of gender in this setting. A total of 2042 patients with STEMI undergoing SES-based primary PCI were prospectively enrolled into Shanghai Acute Coronary Event (SACE) registry (1574 men and 468 women). Baseline demographics, angiographic and PCI features, and in-hospital and 30-day major adverse cardiac events (MACE) were analyzed as a function of gender. Compared with men, women were older and more frequently had hypertension, diabetes, and hypercholesterolemia. Use of platelet glycoprotein IIb/IIIa receptor inhibitor (GPI, 65.5% vs. 62.2%, P = 0.10) and procedural success rate (95.0% vs. 94.2%, P = 0.52) were similar in both genders. In-hospital death and MACE occurred in 3.8% and 7.6%, and 4.5% and 8.1% in the male and female patients, respectively (all P > 0.05). At 30-day follow-up, survival (94.3% vs. 93.8%, P = 0.66) and MACE-free survival (90.2% vs. 89.3%, P = 0.52) did not significantly differ between men and women. After adjustment for differences in patient demographics, angiographic and procedural features, there were no significant difference in either in-hospital (OR = 0.77, 95%CI of 0.48 to 1.22, P = 0.30) or 30-day mortality (OR = 1.28, 95%CI of 0.73 to 2.23, P = 0.38) between women and men. Despite more advanced age and clustering of risk factors in women, female patients with STEMI treated by SES-based primary PCI had similar in-hospital and short-term clinical outcomes as their male counterparts.

  6. An accident diagnosis algorithm using long short-term memory

    Directory of Open Access Journals (Sweden)

    Jaemin Yang

    2018-05-01

    Full Text Available Accident diagnosis is one of the complex tasks for nuclear power plant (NPP operators. In abnormal or emergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address this problem. Among them, the recurrent neural network (RNN has performed well at analyzing time series data. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM, which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists of preprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed input variables are calculated to output the accident diagnosis results. The outputs are also postprocessed using softmax to determine the ranking of accident diagnosis results with probabilities. This algorithm was trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steam generator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstrate the feasibility of diagnosing NPP accidents. Keywords: Accident Diagnosis, Long Short-term Memory, Recurrent Neural Network, Softmax

  7. Hydroxychloroquine retinopathy after short-term therapy.

    Science.gov (United States)

    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

  8. Short-term plasticity as a neural mechanism supporting memory and attentional functions

    OpenAIRE

    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 ...

  9. A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment

    International Nuclear Information System (INIS)

    Jiang Chuanwen; Bompard, Etorre

    2005-01-01

    This paper proposes a short term hydroelectric plant dispatch model based on the rule of maximizing the benefit. For the optimal dispatch model, which is a large scale nonlinear planning problem with multi-constraints and multi-variables, this paper proposes a novel self-adaptive chaotic particle swarm optimization algorithm to solve the short term generation scheduling of a hydro-system better in a deregulated environment. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed approach introduces chaos mapping and an adaptive scaling term into the particle swarm optimization algorithm, which increases its convergence rate and resulting precision. The new method has been examined and tested on a practical hydro-system. The results are promising and show the effectiveness and robustness of the proposed approach in comparison with the traditional particle swarm optimization algorithm

  10. Understanding Short-Term Nonmigrating Tidal Variability in the Ionospheric Dynamo Region from SABER Using Information Theory and Bayesian Statistics

    Science.gov (United States)

    Kumari, K.; Oberheide, J.

    2017-12-01

    Nonmigrating tidal diagnostics of SABER temperature observations in the ionospheric dynamo region reveal a large amount of variability on time-scales of a few days to weeks. In this paper, we discuss the physical reasons for the observed short-term tidal variability using a novel approach based on Information theory and Bayesian statistics. We diagnose short-term tidal variability as a function of season, QBO, ENSO, and solar cycle and other drivers using time dependent probability density functions, Shannon entropy and Kullback-Leibler divergence. The statistical significance of the approach and its predictive capability is exemplified using SABER tidal diagnostics with emphasis on the responses to the QBO and solar cycle. Implications for F-region plasma density will be discussed.

  11. Questioning short-term memory and its measurement: Why digit span measures long-term associative learning.

    Science.gov (United States)

    Jones, Gary; Macken, Bill

    2015-11-01

    Traditional accounts of verbal short-term memory explain differences in performance for different types of verbal material by reference to inherent characteristics of the verbal items making up memory sequences. The role of previous experience with sequences of different types is ostensibly controlled for either by deliberate exclusion or by presenting multiple trials constructed from different random permutations. We cast doubt on this general approach in a detailed analysis of the basis for the robust finding that short-term memory for digit sequences is superior to that for other sequences of verbal material. Specifically, we show across four experiments that this advantage is not due to inherent characteristics of digits as verbal items, nor are individual digits within sequences better remembered than other types of individual verbal items. Rather, the advantage for digit sequences stems from the increased frequency, compared to other verbal material, with which digits appear in random sequences in natural language, and furthermore, relatively frequent digit sequences support better short-term serial recall than less frequent ones. We also provide corpus-based computational support for the argument that performance in a short-term memory setting is a function of basic associative learning processes operating on the linguistic experience of the rememberer. The experimental and computational results raise questions not only about the role played by measurement of digit span in cognition generally, but also about the way in which long-term memory processes impact on short-term memory functioning. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

  12. Robust Short-Term Memory without Synaptic Learning

    Science.gov (United States)

    Johnson, Samuel; Marro, J.; Torres, Joaquín J.

    2013-01-01

    Short-term memory in the brain cannot in general be explained the way long-term memory can – as a gradual modification of synaptic weights – since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings. PMID:23349664

  13. Robust short-term memory without synaptic learning.

    Directory of Open Access Journals (Sweden)

    Samuel Johnson

    Full Text Available Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds. The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.

  14. Robust short-term memory without synaptic learning.

    Science.gov (United States)

    Johnson, Samuel; Marro, J; Torres, Joaquín J

    2013-01-01

    Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.

  15. Temporal grouping effects in musical short-term memory.

    Science.gov (United States)

    Gorin, Simon; Mengal, Pierre; Majerus, Steve

    2018-07-01

    Recent theoretical accounts of verbal and visuo-spatial short-term memory (STM) have proposed the existence of domain-general mechanisms for the maintenance of serial order information. These accounts are based on the observation of similar behavioural effects across several modalities, such as temporal grouping effects. Across two experiments, the present study aimed at extending these findings, by exploring a STM modality that has received little interest so far, STM for musical information. Given its inherent rhythmic, temporal and serial organisation, the musical domain is of interest for investigating serial order STM processes such as temporal grouping. In Experiment 1, the data did not allow to determine the presence or the absence of temporal grouping effects. In Experiment 2, we observed that temporal grouping of tone sequences during encoding improves short-term recognition for serially presented probe tones. Furthermore, the serial position curves included micro-primacy and micro-recency effects, which are the hallmark characteristic of temporal grouping. Our results suggest that the encoding of serial order information in musical STM may be supported by temporal positional coding mechanisms similar to those reported in the verbal domain.

  16. A method for short term electricity spot price forecasting

    International Nuclear Information System (INIS)

    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

  17. A method for short term electricity spot price forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Koreneff, G; Seppaelae, A; Lehtonen, M; Kekkonen, V [VTT Energy, Espoo (Finland); Laitinen, E; Haekli, J [Vaasa Univ. (Finland); Antila, E [ABB Transmit Oy (Finland)

    1998-08-01

    In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market

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

    International Nuclear Information System (INIS)

    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

  19. The Role of Short-term Consolidation in Memory Persistence

    OpenAIRE

    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...

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

    Science.gov (United States)

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

    2017-02-01

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

  1. The Mind and Brain of Short-Term Memory

    OpenAIRE

    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...

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

    Directory of Open Access Journals (Sweden)

    Holger Hoppe

    2012-05-01

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

  3. Short-Term Market Risks Implied by Weekly Options

    DEFF Research Database (Denmark)

    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....

  4. Pro short-term procurement - Broker/trader

    International Nuclear Information System (INIS)

    Hoellen, E.E.

    1990-01-01

    The author presents his opinion on the issue of short-term versus long-term procurement of uranium and enrichment and the impact on reliability of supply. The progression of the market has been one of increasing commoditization. Utility buyers have moved towards purchasing uranium on the spot market and linking long-term contracts to spot-market pricing. There is some logic to the argument that utilities and the industry in general would be best served by this approach. Inventories would be worked off much more quickly, and unnecessary supply would be shut off until prices recovered to profitable levels. The result would be a healthier market with no detriment to the reliability of supply

  5. Very Short-term Nonparametric Probabilistic Forecasting of Renewable Energy Generation - with Application to Solar Energy

    DEFF Research Database (Denmark)

    Golestaneh, Faranak; Pinson, Pierre; Gooi, Hoay Beng

    2016-01-01

    Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only...... approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power...... generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts...

  6. Probabilistic short-term volcanic hazard in phases of unrest: A case study for tephra fallout

    Science.gov (United States)

    Selva, Jacopo; Costa, Antonio; Sandri, Laura; Macedonio, Giovanni; Marzocchi, Warner

    2014-12-01

    During volcanic crises, volcanologists estimate the impact of possible imminent eruptions usually through deterministic modeling of the effects of one or a few preestablished scenarios. Despite such an approach may bring an important information to the decision makers, the sole use of deterministic scenarios does not allow scientists to properly take into consideration all uncertainties, and it cannot be used to assess quantitatively the risk because the latter unavoidably requires a probabilistic approach. We present a model based on the concept of Bayesian event tree (hereinafter named BET_VH_ST, standing for Bayesian event tree for short-term volcanic hazard), for short-term near-real-time probabilistic volcanic hazard analysis formulated for any potential hazardous phenomenon accompanying an eruption. The specific goal of BET_VH_ST is to produce a quantitative assessment of the probability of exceedance of any potential level of intensity for a given volcanic hazard due to eruptions within restricted time windows (hours to days) in any area surrounding the volcano, accounting for all natural and epistemic uncertainties. BET_VH_ST properly assesses the conditional probability at each level of the event tree accounting for any relevant information derived from the monitoring system, theoretical models, and the past history of the volcano, propagating any relevant epistemic uncertainty underlying these assessments. As an application example of the model, we apply BET_VH_ST to assess short-term volcanic hazard related to tephra loading during Major Emergency Simulation Exercise, a major exercise at Mount Vesuvius that took place from 19 to 23 October 2006, consisting in a blind simulation of Vesuvius reactivation, from the early warning phase up to the final eruption, including the evacuation of a sample of about 2000 people from the area at risk. The results show that BET_VH_ST is able to produce short-term forecasts of the impact of tephra fall during a rapidly

  7. Numerical simulation of the environmental impact of hydraulic fracturing of tight/shale gas reservoirs on near-surface groundwater: Background, base cases, shallow reservoirs, short-term gas, and water transport

    Science.gov (United States)

    Reagan, Matthew T; Moridis, George J; Keen, Noel D; Johnson, Jeffrey N

    2015-01-01

    Hydrocarbon production from unconventional resources and the use of reservoir stimulation techniques, such as hydraulic fracturing, has grown explosively over the last decade. However, concerns have arisen that reservoir stimulation creates significant environmental threats through the creation of permeable pathways connecting the stimulated reservoir with shallower freshwater aquifers, thus resulting in the contamination of potable groundwater by escaping hydrocarbons or other reservoir fluids. This study investigates, by numerical simulation, gas and water transport between a shallow tight-gas reservoir and a shallower overlying freshwater aquifer following hydraulic fracturing operations, if such a connecting pathway has been created. We focus on two general failure scenarios: (1) communication between the reservoir and aquifer via a connecting fracture or fault and (2) communication via a deteriorated, preexisting nearby well. We conclude that the key factors driving short-term transport of gas include high permeability for the connecting pathway and the overall volume of the connecting feature. Production from the reservoir is likely to mitigate release through reduction of available free gas and lowering of reservoir pressure, and not producing may increase the potential for release. We also find that hydrostatic tight-gas reservoirs are unlikely to act as a continuing source of migrating gas, as gas contained within the newly formed hydraulic fracture is the primary source for potential contamination. Such incidents of gas escape are likely to be limited in duration and scope for hydrostatic reservoirs. Reliable field and laboratory data must be acquired to constrain the factors and determine the likelihood of these outcomes. Key Points: Short-term leakage fractured reservoirs requires high-permeability pathways Production strategy affects the likelihood and magnitude of gas release Gas release is likely short-term, without additional driving forces PMID

  8. Gummed-up memory: chewing gum impairs short-term recall.

    Science.gov (United States)

    Kozlov, Michail D; Hughes, Robert W; Jones, Dylan M

    2012-01-01

    Several studies have suggested that short-term memory is generally improved by chewing gum. However, we report the first studies to show that chewing gum impairs short-term memory for both item order and item identity. Experiment 1 showed that chewing gum reduces serial recall of letter lists. Experiment 2 indicated that chewing does not simply disrupt vocal-articulatory planning required for order retention: Chewing equally impairs a matched task that required retention of list item identity. Experiment 3 demonstrated that manual tapping produces a similar pattern of impairment to that of chewing gum. These results clearly qualify the assertion that chewing gum improves short-term memory. They also pose a problem for short-term memory theories asserting that forgetting is based on domain-specific interference given that chewing does not interfere with verbal memory any more than tapping. It is suggested that tapping and chewing reduce the general capacity to process sequences.

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

    International Nuclear Information System (INIS)

    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

  10. Short-term memories with a stochastic perturbation

    International Nuclear Information System (INIS)

    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

  11. Short-term energy outlook, annual supplement 1994

    International Nuclear Information System (INIS)

    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

  12. Pediatric polytrauma : Short-term and long-term outcomes

    NARCIS (Netherlands)

    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

  13. Comparison of Sugammadex and Neostigmine in Short Term Surgery

    Directory of Open Access Journals (Sweden)

    Fatih Koc

    2014-03-01

    Full Text Available Aim: This study compared the efficacy and cost effectivines of sugammadex and neostigmine for reversal of neuromuscular blockade induced by rocuronium for short term elective surgery. Material and Method: After written informed consent, 33 patients aged 18%u201365, ASA I-III, who were undergoing short term surgery (

  14. Short-Term Group Treatment for Adult Children of Alcoholics.

    Science.gov (United States)

    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)

  15. Short-term energy outlook annual supplement, 1993

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1993-08-06

    The Short-Term Energy Outlook Annual Supplement (supplement) is published once a year as a complement to the Short-Term Energy Outlook (Outlook), Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts.

  16. Short-Term Robustness of Production Management Systems : New Methodology

    NARCIS (Netherlands)

    Kleijnen, J.P.C.; Gaury, E.G.A.

    2000-01-01

    This paper investigates the short-term robustness of production planning and control systems. This robustness is defined here as the systems ability to maintain short-term service probabilities (i.e., the probability that the fill rate remains within a prespecified range), in a variety of

  17. Short-Term Reciprocity in Late Parent-Child Relationships

    Science.gov (United States)

    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…

  18. [Short-term memory characteristics of vibration intensity tactile perception on human wrist].

    Science.gov (United States)

    Hao, Fei; Chen, Li-Juan; Lu, Wei; Song, Ai-Guo

    2014-12-25

    In this study, a recall experiment and a recognition experiment were designed to assess the human wrist's short-term memory characteristics of tactile perception on vibration intensity, by using a novel homemade vibrotactile display device based on the spatiotemporal combination vibration of multiple micro vibration motors as a test device. Based on the obtained experimental data, the short-term memory span, recognition accuracy and reaction time of vibration intensity were analyzed. From the experimental results, some important conclusions can be made: (1) The average short-term memory span of tactile perception on vibration intensity is 3 ± 1 items; (2) The greater difference between two adjacent discrete intensities of vibrotactile stimulation is defined, the better average short-term memory span human wrist gets; (3) There is an obvious difference of the average short-term memory span on vibration intensity between the male and female; (4) The mechanism of information extraction in short-term memory of vibrotactile display is to traverse the scanning process by comparison; (5) The recognition accuracy and reaction time performance of vibrotactile display compares unfavourably with that of visual and auditory. The results from this study are important for designing vibrotactile display coding scheme.

  19. Short-term plasticity as a neural mechanism supporting memory and attentional functions.

    Science.gov (United States)

    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.

  20. Short term forecasting of petroleum product demand in France

    International Nuclear Information System (INIS)

    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)

  1. Grey-identification model based wind power generation short-term prediction%基于灰色-辨识模型的风电功率短期预测

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

      为了准确预测风电机组的输出功率,针对实际风场,给出一种基于灰色 GM(1,1)模型和辨识模型的风电功率预测建模方法,采用残差修正的方法对风速进行预测,得出准确的风速预测序列。同时为了提高风电功率预测的精度,引入 FIR-MA迭代辨识模型,从分段函数的角度对风电场实际风速-风电功率曲线进行拟合,取得合适的 FIR-MA 模型。利用该模型对额定容量为850 kW 的风电机组进行建模,采用平均绝对误差和均方根误差,以及单点误差作为评价指标,与风电场的实测数据进行比较分析。仿真结果表明,基于灰色-辨识模型的风电机组输出功率预测方法是有效和实用的,该模型能够很好地预测风电机组的实时输出功率,从而提高风电场输出功率预测的精确性。%To predict the output power of wind turbine accurately, based on the GM (1, 1) model and the identification method, a wind power generation short-term prediction method is presented for the real wind farm. The revision of residual error is applied to forecast the wind speed and get the accurate predicted wind speed series. Then, in order to increase the prediction precision of wind power, the FIR-MA iterative identification model is adopted to fit the real relationship between sequential wind speed and wind power and get the proper FIR-MA model. By modeling the wind turbine whose rated capacity is 850 kW, this paper compares the predicted wind generation power with the observed data using mean absolute percentage error, root mean square error and single point error as its evaluation indexes. The simulation shows the effectiveness and the practical applicability of the presented method, which can predict the real time generation power of wind turbineness and raise the accuracy of the wind power prediction. Finally, the simulation using the actual data from wind farm in China proves the efficiency of the

  2. Pediatric laparoscopic sleeve gastrectomy in Turkey: Short-term results.

    Science.gov (United States)

    Ates, Ufuk; Ergun, Ergun; Gollu, Gulnur; Sozduyar, Sumeyye; Can, Ozlem Selvi; Yagmurlu, Aydin

    2018-05-01

    Obesity is one of the most rapidly increasing health problems in children. Laparoscopic sleeve gastrectomy (LSG) is one of the best treatment options and is feasible and safe in children. The aim of this study was to present the short-term results of a laparoscopic sleeve gastrectomy series in children. Children who underwent LSG in 2014-2017 were included in the study. Charts were investigated retrospectively and short-term weight loss was analyzed. Patients who had surgery in 2014-2017 were included in the study. There were six girls and two boys, and the median age was 15 years (range, 11-18 years). Mean weight was 159.25 ± 19.78 kg, and mean body mass index was 61.05 ± 8.5 kg/m 2 . Mean operation time was 70 min (range, 65-90 min), mean hospital stay was 5.1 days (range, 3-7 days), and mean follow up was 19.2 months (range, 1-43 months). Of these patients, five had hypertension and were under medication and two of these five also had hyperinsulinemia. One of the five children had Bardet-Biedl syndrome and one had bronchial asthma. After operation, medication was stopped in four of the eight children. At the time of writing, six patients were doing well without postoperative complications, or the need for reoperation. Even though the follow-up period was short and the number of patients was small, LSG was a feasible and promising surgical method for morbidly obese children. A multidisciplinary approach and lifelong behavior therapy are key steps for success. © 2018 Japan Pediatric Society.

  3. The Delicate Analysis of Short-Term Load Forecasting

    Science.gov (United States)

    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.

  4. Pro short-term procurement - U.S. utility

    International Nuclear Information System (INIS)

    Thompson, R.D.

    1990-01-01

    The author expresses the opinion that rather than focusing market discussions around short-term versus long-term procurement strategies, the parties need to be focusing on how long it is going to take to get to a predominantly market-based price both in uranium and enrichment. Long-term contracts are going to be around and will always be an important part of buyers' and sellers' strategies. It is evident that the annual term contract price renegotiations around the world are resulting in continually lower prices. When these price negotiations finally arrive in the range of the market price, a commodity market that resembles other energy commodity markets can be obtained

  5. Online Short-term Solar Power Forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

    2011-01-01

    This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours.......This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours....

  6. International Short-Term Countermeasures Survey - 2012 Update

    International Nuclear Information System (INIS)

    Ingham, Grant

    2013-01-01

    Nuclear emergency planning, preparedness, response, and management, in general, are essential elements of any country's nuclear power programme. Part of nuclear emergency planning and preparedness is the implementation of national emergency plans, including detailed procedures for the implementation of short-term countermeasures, before during, and after the release of radioactive substances. The timely and appropriate implementation of short-term countermeasures, such as sheltering, evacuation, and iodine prophylaxis, can, in case of a nuclear emergency with a release of radioactive material, considerably reduce the doses to the public in the vicinity of the nuclear installation. Although international guidelines exist, national procedures and practices may differ due to different national habits, cultural specificity, and societal needs. Different national procedures and practices may, however, in the case of a radioactive release affecting two neighbouring countries, lead to different decisions in the implementation of countermeasures. In order to better understand existing approaches and to facilitate the comparison of national practices, the NEA decided to launch a questionnaire on current practices regarding short-term countermeasures, updating a similar survey performed in 1994 and 2003, as countries' practices have since evolved and been modified. In 2012, it was decided to reevaluate the country approaches in light of the early lessons learnt from the Fukushima Daiichi NPP accident. The information collected may be used to understand the basis for decisions in various countries, and, if deemed appropriate, as a basis for international harmonisation. This may also assist member countries to explain to the public affected by an emergency why the decisions in neighbouring countries may vary. This report summarises the information given by member countries and includes nine sections to explore the different aspects, covering the following topics: member

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

    Directory of Open Access Journals (Sweden)

    Oumei Cheng

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

  8. Short-term hydro-thermal scheduling using particle swarm optimization method

    International Nuclear Information System (INIS)

    Yu, Binghui; Yuan, Xiaohui; Wang, Jinwen

    2007-01-01

    The approaches based on different particle swarm optimization (PSO) techniques are applied to solve the short-term hydro-thermal scheduling problem. In the proposed methods, many constraints of the hydro-thermal system, such as power balance, water balance, reservoir volume limits and the operation limits of hydro and thermal plants, are considered. The feasibility of the proposed algorithm is demonstrated through an example system, and the results are compared with the results of a genetic algorithm and evolutionary programming approaches. The experimental results show that all the PSO algorithms have the ability to achieve nearly global solutions, but a local version of PSO with inertia weight appears to be the best amongst all the PSOs in terms of high quality solution

  9. Verbal and musical short-term memory: Variety of auditory disorders after stroke.

    Science.gov (United States)

    Hirel, Catherine; Nighoghossian, Norbert; Lévêque, Yohana; Hannoun, Salem; Fornoni, Lesly; Daligault, Sébastien; Bouchet, Patrick; Jung, Julien; Tillmann, Barbara; Caclin, Anne

    2017-04-01

    Auditory cognitive deficits after stroke may concern language and/or music processing, resulting in aphasia and/or amusia. The aim of the present study was to assess the potential deficits of auditory short-term memory for verbal and musical material after stroke and their underlying cerebral correlates with a Voxel-based Lesion Symptom Mapping approach (VLSM). Patients with an ischemic stroke in the right (N=10) or left (N=10) middle cerebral artery territory and matched control participants (N=14) were tested with a detailed neuropsychological assessment including global cognitive functions, music perception and language tasks. All participants then performed verbal and musical auditory short-term memory (STM) tasks that were implemented in the same way for both materials. Participants had to indicate whether series of four words or four tones presented in pairs, were the same or different. To detect domain-general STM deficits, they also had to perform a visual STM task. Behavioral results showed that patients had lower performance for the STM tasks in comparison with control participants, regardless of the material (words, tones, visual) and the lesion side. The individual patient data showed a double dissociation between some patients exhibiting verbal deficits without musical deficits or the reverse. Exploratory VLSM analyses suggested that dorsal pathways are involved in verbal (phonetic), musical (melodic), and visual STM, while the ventral auditory pathway is involved in musical STM. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Drought analysis and short-term forecast in the Aison River Basin (Greece

    Directory of Open Access Journals (Sweden)

    S. Kavalieratou

    2012-05-01

    Full Text Available A combined regional drought analysis and forecast is elaborated and applied to the Aison River Basin (Greece. The historical frequency, duration and severity were estimated using the standardized precipitation index (SPI computed on variable time scales, while short-term drought forecast was investigated by means of 3-D loglinear models. A quasi-association model with homogenous diagonal effect was proposed to fit the observed frequencies of class transitions of the SPI values computed on the 12-month time scale. Then, an adapted submodel was selected for each data set through the backward elimination method. The analysis and forecast of the drought class transition probabilities were based on the odds of the expected frequencies, estimated by these submodels, and the respective confidence intervals of these odds. The parsimonious forecast models fitted adequately the observed data. Results gave a comprehensive insight on drought behavior, highlighting a dominant drought period (1988–1991 with extreme drought events and revealing, in most cases, smooth drought class transitions. The proposed approach can be an efficient tool in regional water resources management and short-term drought warning, especially in irrigated districts.

  11. Verbal Short-Term Memory Span in Speech-Disordered Children: Implications for Articulatory Coding in Short-Term Memory.

    Science.gov (United States)

    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)

  12. Short-Term Solar Collector Power Forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Perers, Bengt

    2011-01-01

    This paper describes a new approach to online forecasting of power output from solar thermal collectors. The method is suited for online forecasting in many applications and in this paper it is applied to predict hourly values of power from a standard single glazed large area flat plate collector...... enabling tracking of changes in the system and in the surrounding conditions, such as decreasing performance due to wear and dirt, and seasonal changes such as leaves on trees. This furthermore facilitates remote monitoring and check of the system....

  13. Quantitative analysis of UV-A shock and short term stress using iTRAQ, pseudo selective reaction monitoring (pSRM) and GC-MS based metabolite analysis of the cyanobacterium Nostoc punctiforme ATCC 29133.

    Science.gov (United States)

    Wase, Nishikant; Pham, Trong Khoa; Ow, Saw Yen; Wright, Phillip C

    2014-09-23

    A quantitative proteomics and metabolomics analysis was performed using iTRAQ, HPLC and GC-MS in the filamentous cyanobacterium Nostoc punctiforme ATCC 29133 to understand the effect of short and long term UV-A exposure. Changes in the proteome were measured for short-term stress (4-24h) using iTRAQ. Changes in the photosynthetic pigments and intracellular metabolites were observed at exposures of up to 7days (pigments) and up to 11days (intracellular metabolites). To assess iTRAQ measurement quality, pseudo selected reaction monitoring (pSRM) was used, with this confirming underestimation of protein abundance levels by iTRAQ. Our results suggest that short term UV-A radiation lowers the abundance of PS-I and PS-II proteins. We also observed an increase in abundance of intracellular redox homeostasis proteins and plastocyanin. Additionally, we observed statistically significant changes in scytonemin, Chlorophyll A, astaxanthin, zeaxanthin, and β-carotene. Assessment of intracellular metabolites showed significant changes in several, suggesting their potential role in the Nostoc's stress mitigation strategy. Cyanobacteria under UV-A radiation have reduced growth due to intensive damage to essential functions, but the organism shows a defense response by remodeling bioenergetics pathway, induction of the UV protection compound scytonemin and increased levels of proline and tyrosine as a mitigation response. The effect of UV-A radiation on the proteome and intracellular metabolites of N. punctiforme ATCC 29133 including photosynthetic pigments has been described. We also verify the expression of 13 iTRAQ quantified protein using LC-pSRM. Overall we observed that UV-A radiation has a drastic effect on the photosynthetic machinery, photosynthetic pigments and intracellular amino acids. As a mitigation strategy against UV-A radiation, proline, glycine, and tyrosine were accumulated. Copyright © 2014. Published by Elsevier B.V.

  14. Evaluation of Short Term Memory Span Function In Children

    Directory of Open Access Journals (Sweden)

    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.

  15. Audit of long-term and short-term liabilities

    Directory of Open Access Journals (Sweden)

    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.

  16. Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Yannan; Hou, Zhangshuan; Meng, Da; Samaan, Nader A.; Makarov, Yuri V.; Huang, Zhenyu

    2016-07-17

    In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

  17. The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke

    OpenAIRE

    Leff, Alexander P.; Schofield, Thomas M.; Crinion, Jennifer T.; Seghier, Mohamed L.; Grogan, Alice; Green, David W.; Price, Cathy J.

    2009-01-01

    Competing theories of short-term memory function make specific predictions about the functional anatomy of auditory short-term memory and its role in language comprehension. We analysed high-resolution structural magnetic resonance images from 210 stroke patients and employed a novel voxel based analysis to test the relationship between auditory short-term memory and speech comprehension. Using digit span as an index of auditory short-term memory capacity we found that the structural integrit...

  18. Online short-term solar power forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

    2009-01-01

    This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 hours. The data used is fifteen......-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...

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  2. Short term load forecasting: two stage modelling

    Directory of Open Access Journals (Sweden)

    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.

  3. A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model

    International Nuclear Information System (INIS)

    Wang, Jianzhou; Hu, Jianming

    2015-01-01

    With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT (Empirical Wavelet Transform) is employed to extract meaningful information from a wind speed series by designing an appropriate wavelet filter bank. The GPR (Gaussian Process Regression) model is utilized to combine independent forecasts generated by various forecasting engines (ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM)) in a nonlinear way rather than the commonly used linear way. The proposed approach provides more probabilistic information for wind speed predictions besides improving the forecasting accuracy for single-value predictions. The effectiveness of the proposed approach is demonstrated with wind speed data from two wind farms in China. The results indicate that the individual forecasting engines do not consistently forecast short-term wind speed for the two sites, and the proposed combination method can generate a more reliable and accurate forecast. - Highlights: • The proposed approach can make probabilistic modeling for wind speed series. • The proposed approach adapts to the time-varying characteristic of the wind speed. • The hybrid approach can extract the meaningful components from the wind speed series. • The proposed method can generate adaptive, reliable and more accurate forecasting results. • The proposed model combines four independent forecasting engines in a nonlinear way.

  4. Short term solar radiation forecasting: Island versus continental sites

    International Nuclear Information System (INIS)

    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.

  5. Short-term effects of simultaneous cardiovascular workout and ...

    African Journals Online (AJOL)

    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 ...

  6. Short-term treatment outcomes of children starting antiretroviral ...

    African Journals Online (AJOL)

    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.

  7. Short-Term Memory in Habituation and Dishabituation

    Science.gov (United States)

    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)

  8. Short-term outcome of patients with closed comminuted femoral ...

    African Journals Online (AJOL)

    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 ...

  9. Short term variations in particulate matter in Mahi river estuary

    Digital Repository Service at National Institute of Oceanography (India)

    Bhosle, N.B.; Rokade, M.A.; Zingde, M.D.

    The particulate matter (PM) collected from Mahi River Estuary was analysed for organic carbon (POC), nitrogen (PON), and chlorophyll a (Chl a). The concentration of PM, POC, PON and Chl a showed short term variations. Average surface concentration...

  10. The nature of forgetting from short-term memory

    OpenAIRE

    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?

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

    Directory of Open Access Journals (Sweden)

    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.

  12. An adaptive short-term prediction scheme for wind energy storage management

    International Nuclear Information System (INIS)

    Blonbou, Ruddy; Monjoly, Stephanie; Dorville, Jean-Francois

    2011-01-01

    Research highlights: → We develop a real time algorithm for grid-connected wind energy storage management. → The method aims to guarantee, with ±5% error margin, the power sent to the grid. → Dynamic scheduling of energy storage is based on short-term energy prediction. → Accurate predictions reduce the need in storage capacity. -- Abstract: Efficient forecasting scheme that includes some information on the likelihood of the forecast and based on a better knowledge of the wind variations characteristics along with their influence on power output variation is of key importance for the optimal integration of wind energy in island's power system. In the Guadeloupean archipelago (French West-Indies), with a total wind power capacity of 25 MW; wind energy can represent up to 5% of the instantaneous electricity production. At this level, wind energy contribution can be equivalent to the current network primary control reserve, which causes balancing difficult. The share of wind energy is due to grow even further since the objective is set to reach 118 MW by 2020. It is an absolute evidence for the network operator that due to security concerns of the electrical grid, the share of wind generation should not increase unless solutions are found to solve the prediction problem. The University of French West-Indies and Guyana has developed a short-term wind energy prediction scheme that uses artificial neural networks and adaptive learning procedures based on Bayesian approach and Gaussian approximation. This paper reports the results of the evaluation of the proposed approach; the improvement with respect to the simple persistent prediction model was globally good. A discussion on how such a tool combined with energy storage capacity could help to smooth the wind power variation and improve the wind energy penetration rate into island utility network is also proposed.

  13. Short-term Consumer Benefits of Dynamic Pricing

    OpenAIRE

    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...

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

    OpenAIRE

    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...

  15. An ethics curriculum for short-term global health trainees

    OpenAIRE

    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...

  16. Short-term mechanisms influencing volumetric brain dynamics

    Directory of Open Access Journals (Sweden)

    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

  17. Frequency-specific insight into short-term memory capacity

    OpenAIRE

    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.

  18. Short-term memory for scenes with affective content

    OpenAIRE

    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...

  19. Narcissism and the Strategic Pursuit of Short-Term Mating

    DEFF Research Database (Denmark)

    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....

  20. Short-term memory and dual task performance

    Science.gov (United States)

    Regan, J. E.

    1982-01-01

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

  1. Short-term economics of virtual power plants

    International Nuclear Information System (INIS)

    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.

  2. THE INTEGRATED SHORT-TERM STATISTICAL SURVEYS: EXPERIENCE OF NBS IN MOLDOVA

    Directory of Open Access Journals (Sweden)

    Oleg CARA

    2012-07-01

    Full Text Available The users’ rising need for relevant, reliable, coherent, timely data for the early diagnosis of the economic vulnerability and of the turning points in the business cycles, especially during a financial and economic crisis, asks for a prompt answer, coordinated by statistical institutions. High quality short term statistics are of special interest for the emerging market economies, such as the Moldavian one, being extremely vulnerable when facing economic recession. Answering to the challenges of producing a coherent and adequate image of the economic activity, by using the system of indicators and definitions efficiently applied at the level of the European Union, the National Bureau of Statistics (NBS of the Republic of Moldova has launched the development process of an integrated system of short term statistics (STS based on the advanced international experience.Thus, in 2011, BNS implemented the integrated statistical survey on STS based on consistent concepts, harmonized with the EU standards. The integration of the production processes, which were previously separated, is based on a common technical infrastructure, standardized procedures and techniques for data production. The achievement of this complex survey with holistic approach has allowed the consolidation of the statistical data quality, comparable at European level and the signifi cant reduction of information burden on business units, especially of small size.The reformation of STS based on the integrated survey has been possible thanks to the consistent methodological and practical support given to NBS by the National Institute of Statistics (INS of Romania, for which we would like to thank to our Romanian colleagues.

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

    Science.gov (United States)

    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.

  4. Tactile short-term memory in sensory-deprived individuals.

    Science.gov (United States)

    Papagno, Costanza; Minniti, Giovanna; Mattavelli, Giulia C; Mantovan, Lara; Cecchetto, Carlo

    2017-02-01

    To verify whether loosing a sense or two has consequences on a spared sensory modality, namely touch, and whether these consequences depend on practice or are biologically determined, we investigated 13 deafblind participants, 16 deaf participants, 15 blind participants, and 13 matched normally sighted and hearing controls on a tactile short-term memory task, using checkerboard matrices of increasing length in which half of the squares were made up of a rough texture and half of a smooth one. Time of execution of a fixed matrix, number of correctly reproduced matrices, largest matrix correctly reproduced and tactile span were recorded. The three groups of sensory-deprived individuals did not differ in any measure, while blind and deaf participants outscored controls in all parameters except time of execution; the difference approached significance for deafblind people compared to controls only in one measure, namely correctly reproduced matrices. In blind and deafblind participants, performance negatively correlated with age of Braille acquisition, the older being the subject when acquiring Braille, the lower the performance, suggesting that practice plays a role. However, the fact that deaf participants, who did not share tactile experience, performed similarly to blind participants and significantly better than controls highlights that practice cannot be the only contribution to better tactile memory.

  5. Slave systems in verbal short-term memory.

    Science.gov (United States)

    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.

  6. Slave systems in verbal short-term memory

    Science.gov (United States)

    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

  7. Short-term carbon partitioning fertilizer responses vary among two full-sib loblolly pine clones

    Science.gov (United States)

    Jeremy P. Stovall; John R. Seiler; Thomas R. Fox

    2012-01-01

    We investigated the effects of fertilizer application on the partitioning of gross primary productivity (GPP) between contrasting full-sib clones of Pinus taeda (L.). Our objective was to determine if fertilizer growth responses resulted from similar short-term changes to partitioning. A modeling approach incorporating respiratory carbon (C) fluxes,...

  8. Experimental Study of Short-Term Training in Social Cognition in Pre-Schoolers

    Science.gov (United States)

    Houssa, Marine; Nader-Grosbois, Nathalie; Jacobs, Emilie

    2014-01-01

    Using an experimental approach, our study examined the differentiated effects on pre-schoolers' social cognition of two short-term social information processing (SIP) and Theory of Mind (ToM) training sessions dealing with emotions and beliefs. The links between ToM, SIP, and social adjustment or externalizing behavior were examined. 47…

  9. Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

    Science.gov (United States)

    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.

  10. Some risks related to the short-term trading of natural gas

    International Nuclear Information System (INIS)

    Ahmed El Hachemi Mazighi

    2004-01-01

    Traditionally guided by long-term contracts, the international natural gas trade is experiencing new methods of operating, based on the short term and more flexibility. Today, indeed, the existence of uncommitted quantities of natural gas, combined with gas price discrepancies among different regions of the world, gives room for the expansion of the spot-trading of gas. The main objective of this paper is to discuss three fundamental risks related to the short-term trading of natural gas: volume risk, price risk and infrastructure risk. The defenders Of globalisation argue that the transition from the long-term to the short-term trading of natural gas is mainly a question of access to gas reserves, decreasing costs of gas liquefaction, the building of liquefied natural gas (LNG) fleets and regasification facilities and third-party access to the infrastructure. This process needs to be as short as possible, so that the risks related to the transition process will disappear rapidly. On the other hand, the detractors of globalisation put the emphasis on the complexity of the gas value chain and on the fact that eliminating long- term contracts increases the risks inherent to the international natural gas business. In this paper, we try to untangle and assess the risks related to the short-term trading of natural gas. Our main conclusions are: the short-term trading of gas is far from riskless; volume risk requires stock-building in both consuming and producing countries. (author)

  11. The Interdependence of Long- and Short-Term Components in Unmasked Repetition Priming: An Indication of Shared Resources.

    Science.gov (United States)

    Merema, Matt R; Speelman, Craig P

    2015-01-01

    It has been suggested that unmasked repetition priming is composed of distinct long-and short-term priming components. The current study sought to clarify the relationship between these components by examining the relationship between them. A total of 60 people (45 females, 15 males) participated in a computer-based lexical decision task designed to measure levels of short-term priming across different levels of long-term priming. The results revealed an interdependent relationship between the two components, whereby an increase in long-term priming prompted a decrease in short-term priming. Both long-term and short-term priming were accurately captured by a single power function over seven minutes post repetition, suggesting the two components may draw on the same resources. This interdependence between long- and short-term priming may serve to improve fluency in reading.

  12. The Interdependence of Long- and Short-Term Components in Unmasked Repetition Priming: An Indication of Shared Resources.

    Directory of Open Access Journals (Sweden)

    Matt R Merema

    Full Text Available It has been suggested that unmasked repetition priming is composed of distinct long-and short-term priming components. The current study sought to clarify the relationship between these components by examining the relationship between them. A total of 60 people (45 females, 15 males participated in a computer-based lexical decision task designed to measure levels of short-term priming across different levels of long-term priming. The results revealed an interdependent relationship between the two components, whereby an increase in long-term priming prompted a decrease in short-term priming. Both long-term and short-term priming were accurately captured by a single power function over seven minutes post repetition, suggesting the two components may draw on the same resources. This interdependence between long- and short-term priming may serve to improve fluency in reading.

  13. Impaired short-term memory for pitch in congenital amusia.

    Science.gov (United States)

    Tillmann, Barbara; Lévêque, Yohana; Fornoni, Lesly; Albouy, Philippe; Caclin, Anne

    2016-06-01

    Congenital amusia is a neuro-developmental disorder of music perception and production. The hypothesis is that the musical deficits arise from altered pitch processing, with impairments in pitch discrimination (i.e., pitch change detection, pitch direction discrimination and identification) and short-term memory. The present review article focuses on the deficit of short-term memory for pitch. Overall, the data discussed here suggest impairments at each level of processing in short-term memory tasks; starting with the encoding of the pitch information and the creation of the adequate memory trace, the retention of the pitch traces over time as well as the recollection and comparison of the stored information with newly incoming information. These impairments have been related to altered brain responses in a distributed fronto-temporal network, associated with decreased connectivity between these structures, as well as in abnormalities in the connectivity between the two auditory cortices. In contrast, amusic participants׳ short-term memory abilities for verbal material are preserved. These findings show that short-term memory deficits in congenital amusia are specific to pitch, suggesting a pitch-memory system that is, at least partly, separated from verbal memory. This article is part of a Special Issue entitled SI: Auditory working memory. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. Circadian modulation of short-term memory in Drosophila.

    Science.gov (United States)

    Lyons, Lisa C; Roman, Gregg

    2009-01-01

    Endogenous biological clocks are widespread regulators of behavior and physiology, allowing for a more efficient allocation of efforts and resources over the course of a day. The extent that different processes are regulated by circadian oscillators, however, is not fully understood. We investigated the role of the circadian clock on short-term associative memory formation using a negatively reinforced olfactory-learning paradigm in Drosophila melanogaster. We found that memory formation was regulated in a circadian manner. The peak performance in short-term memory (STM) occurred during the early subjective night with a twofold performance amplitude after a single pairing of conditioned and unconditioned stimuli. This rhythm in memory is eliminated in both timeless and period mutants and is absent during constant light conditions. Circadian gating of sensory perception does not appear to underlie the rhythm in short-term memory as evidenced by the nonrhythmic shock avoidance and olfactory avoidance behaviors. Moreover, central brain oscillators appear to be responsible for the modulation as cryptochrome mutants, in which the antennal circadian oscillators are nonfunctional, demonstrate robust circadian rhythms in short-term memory. Together these data suggest that central, rather than peripheral, circadian oscillators modulate the formation of short-term associative memory and not the perception of the stimuli.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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.

  17. A New Strategy for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Yi Yang

    2013-01-01

    Full Text Available Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.

  18. Rhythmic Haptic Stimuli Improve Short-Term Attention.

    Science.gov (United States)

    Zhang, Shusheng; Wang, Dangxiao; Afzal, Naqash; Zhang, Yuru; Wu, Ruilin

    2016-01-01

    Brainwave entrainment using rhythmic visual and/or auditory stimulation has shown its efficacy in modulating neural activities and cognitive ability. In the presented study, we aim to investigate whether rhythmic haptic stimulation could enhance short-term attention. An experiment with sensorimotor rhythm (SMR) increasing protocol was performed in which participants were presented sinusoidal vibrotactile stimulus of 15 Hz on their palm. Test of Variables of Attention (T.O.V.A.) was performed before and after the stimulating session. Electroencephalograph (EEG) was recorded across the stimulating session and the two attention test sessions. SMR band power manifested a significant increase after stimulation. Results of T.O.V.A. tests indicated an improvement in the attention of participants who had received the stimulation compared to the control group who had not received the stimulation. The D prime score of T.O.V.A. reveals that participants performed better in perceptual sensitivity and sustaining attention level compared to their baseline performance before the stimulating session. These findings highlight the potential value of using haptics-based brainwave entrainment for cognitive training.

  19. Implications of short-term financial outlook for Canadian producers

    International Nuclear Information System (INIS)

    Shiry, J.

    1997-01-01

    The short-term outlook for the Western Canadian natural gas industry was reviewed. Based on the dramatic growth in the demand for gas, and the explosive growth of the industry in response to export opportunities, the outlook for the remainder of this decade remains good, notwithstanding low internal returns, below-average returns on equity, and increased competition for U.S. markets. The competition will come from offshore wells in the US Gulf, from offshore wells of Sable Island, and from offshore wells in the Gulf of Mexico. Despite the increasing sources of supply gas prices are actually expected to improve slightly in 1997 and beyond, giving rise to cautious optimism. Nevertheless, more favorable tax treatment is urgently required to head off the likelihood of investment dollars moving to Africa, the Middle East, South America, and most especially China and Russia. Once those countries open up for business in the not-too-distant future, the likelihood of them offering substantially better tax treatment than what is available in Canada could do serious damage to domestic gas industry development. 12 figs

  20. VAN method of short-term earthquake prediction shows promise

    Science.gov (United States)

    Uyeda, Seiya

    Although optimism prevailed in the 1970s, the present consensus on earthquake prediction appears to be quite pessimistic. However, short-term prediction based on geoelectric potential monitoring has stood the test of time in Greece for more than a decade [VarotsosandKulhanek, 1993] Lighthill, 1996]. The method used is called the VAN method.The geoelectric potential changes constantly due to causes such as magnetotelluric effects, lightning, rainfall, leakage from manmade sources, and electrochemical instabilities of electrodes. All of this noise must be eliminated before preseismic signals are identified, if they exist at all. The VAN group apparently accomplished this task for the first time. They installed multiple short (100-200m) dipoles with different lengths in both north-south and east-west directions and long (1-10 km) dipoles in appropriate orientations at their stations (one of their mega-stations, Ioannina, for example, now has 137 dipoles in operation) and found that practically all of the noise could be eliminated by applying a set of criteria to the data.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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)

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

    Science.gov (United States)

    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

  4. The pedagogy of Short-Term Study-Abroad Programs

    Directory of Open Access Journals (Sweden)

    Jude Gonsalvez

    2013-10-01

    Full Text Available This paper focuses on establishing guidelines on the pedagogy of short term study abroad programs. This study follows 33 students who participated in a short-term study-abroad program to India with the researcher from 2006 through 2011. The study relies heavily on the student reflections and expressions as they experienced them. It is qualitative in nature. Focus groups were the main method of data collection, where participants were invited to reflect, express, and share their experiences with one another. This provided an opportunity for the participants to come together, relive their experiences, and help provide information as to how and what type of an influence this short-term study-abroad program provided.

  5. Verbal short-term memory and vocabulary learning in polyglots.

    Science.gov (United States)

    Papagno, C; Vallar, G

    1995-02-01

    Polyglot and non-polyglot Italian subjects were given tests assessing verbal (phonological) and visuo-spatial short-term and long-term memory, general intelligence, and vocabulary knowledge in their native language. Polyglots had a superior level of performance in verbal short-term memory tasks (auditory digit span and nonword repetition) and in a paired-associate learning test, which assessed the subjects' ability to acquire new (Russian) words. By contrast, the two groups had comparable performance levels in tasks assessing general intelligence, visuo-spatial short-term memory and learning, and paired-associate learning of Italian words. These findings, which are in line with neuropsychological and developmental evidence, as well as with data from normal subjects, suggest a close relationship between the capacity of phonological memory and the acquisition of foreign languages.

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

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...... of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset...

  7. [Impulsiveness Among Short-Term Prisoners with Antisocial Personality Disorder].

    Science.gov (United States)

    Lang, Fabian U; Otte, Stefanie; Vasic, Nenad; Jäger, Markus; Dudeck, Manuela

    2015-07-01

    The study aimed to investigate the correlation between impulsiveness and the antisocial personality disorder among short-term prisoners. The impulsiveness was diagnosed by the Barratt Impulsiveness Scale (BIS). Short-term prisoners with antisocial personality disorder scored significant higher marks on the BIS total scale than those without any personality disorder. In detail, they scored higher marks on each subscale regarding attentional, motor and nonplanning impulsiveness. Moderate and high effects were calculated. It is to be considered to regard impulsivity as a conceptual component of antisociality. © Georg Thieme Verlag KG Stuttgart · New York.

  8. Short-term memory binding deficits in Alzheimer's disease

    OpenAIRE

    Parra, Mario; Abrahams, S.; Fabi, K.; Logie, R.; Luzzi, S.; Della Sala, Sergio

    2009-01-01

    Alzheimer's disease impairs long term memories for related events (e.g. faces with names) more than for single events (e.g. list of faces or names). Whether or not this associative or ‘binding’ deficit is also found in short-term memory has not yet been explored. In two experiments we investigated binding deficits in verbal short-term memory in Alzheimer's disease. Experiment 1 : 23 patients with Alzheimer's disease and 23 age and education matched healthy elderly were recruited. Participants...

  9. Financial determinants of corporate reputation: A short-term approach

    Directory of Open Access Journals (Sweden)

    Anna Blajer-Gołębiewska

    2016-12-01

    Full Text Available When observing companies listed on stock exchanges, it can be noticed that the gap between a company’s book value (BV and its market value (MV is often significant. The fact that investors are willing to pay more for companies’ assets is often explained using the concept of corporate reputation – an intangible additional asset of a company, which is worth to paying for.

  10. Some risks related to the short-term trading of natural gas

    International Nuclear Information System (INIS)

    Mazighi, Ahmed El Hachemi

    2004-01-01

    Traditionally guided by long-term contracts, the international natural gas trade is experiencing new methods of operating, based on the short term and more flexibility. Today, indeed, the existence of uncommitted quantities of natural gas, combined with gas price discrepancies among different regions of the world, gives room for the expansion of the spot-trading of gas. The main objective of this paper is to discuss three fundamental risks related to the short-term trading of natural gas: volume risk, price risk and infrastructure risk. The defenders of globalisation argue that the transition from the long-term to the short-term trading of natural gas is mainly a question of access to gas reserves, decreasing costs of gas liquefaction, the building of liquefied natural gas (LNG) fleets and regasification facilities and third-party access to the infrastructure. This process needs to be as short as possible, so that the risks related to the transition process will disappear rapidly. On the other hand, the detractors of globalisation put the emphasis on the complexity of the gas value chain and on the fact that eliminating long-term contracts increases the risks inherent to the international natural gas business. In this paper, we try to untangle and assess the risks related to the short-term trading of natural gas. Our main conclusions are: the short-term trading of gas is far from riskless; volume risk requires stock-building in both consuming and producing countries; price risk, through the high volatility for gas, induces an increase in options prices; there is no evidence to suggest that money-lenders' appetite for financing gas infrastructure projects will continue in a short-term trading system. This would be a threat to consumers' security of supply. (Author)

  11. Performance Funding Policy Effects on Community College Outcomes: Are Short-Term Certificates on the Rise?

    Science.gov (United States)

    Li, Amy Y.; Kennedy, Alec I.

    2018-01-01

    Objective: Performance funding (PF) policies allocate a portion of state funding to colleges based on student outcomes. This study is the first to account for policy type and design differences, and explores the impact of performance funding on three levels of credential completions: short-term certificates, medium-term certificates, and…

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

    Science.gov (United States)

    Botvinick, Matthew M.; Plaut, David C.

    2006-01-01

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

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

    NARCIS (Netherlands)

    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

  14. Course and Short-Term Outcomes of Separation Anxiety Disorder in a Community Sample of Twins.

    Science.gov (United States)

    Foley, Debra L.; Pickles, Andrew; Maes, Hermine M.; Silberg, Judy L.; Eaves, Lindon J.

    2004-01-01

    Objective: To assess the course and short-term outcomes associated with separation anxiety disorder (SAD) in a community setting. Method: The subjects were 161 of 2,061 8- to 17-year-old twins with SAD from a community-based twin study. Subjects were born between 1974 and 1983. Subjects and parents were personally interviewed about the subject's…

  15. Nordic Experiences: Participants' Expectations and Experiences of Short-Term Study Abroad Programs

    Science.gov (United States)

    Rahikainen, Katariina; Hakkarainen, Kai

    2013-01-01

    The purpose of this study was to investigate Finnish high school students' and teachers' perceptions of the effects of short-term Nordic study abroad programs in which they had participated. The data presented were based on a "mixed-methods strategy." The data set consisted of responses from 158 students and 92 teachers to a specifically…

  16. Short-Term Effects of Playing Computer Games on Attention

    Science.gov (United States)

    Tahiroglu, Aysegul Yolga; Celik, Gonca Gul; Avci, Ayse; Seydaoglu, Gulsah; Uzel, Mehtap; Altunbas, Handan

    2010-01-01

    Objective: 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. Method: 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.…

  17. Exogenous Attention Influences Visual Short-Term Memory in Infants

    Science.gov (United States)

    Ross-Sheehy, Shannon; Oakes, Lisa M.; Luck, Steven J.

    2011-01-01

    Two experiments examined the hypothesis that developing visual attentional mechanisms influence infants' Visual Short-Term Memory (VSTM) in the context of multiple items. Five- and 10-month-old infants (N = 76) received a change detection task in which arrays of three differently colored squares appeared and disappeared. On each trial one square…

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

    Science.gov (United States)

    Kang, Min-Suk; Choi, Joongrul

    2015-07-01

    We used a visual illusion called motion repulsion as a model system for investigating competition between two mental representations. Subjects were asked to remember two random-dot-motion displays presented in sequence and then to report the motion directions for each. Remembered motion directions were shifted away from the actual motion directions, an effect similar to the motion repulsion observed during perception. More important, the item retrieved second showed greater repulsion than the item retrieved first. This suggests that earlier retrieval exerted greater inhibition on the other item being held in short-term memory. This retrieval-induced motion repulsion could be explained neither by reduced cognitive resources for maintaining short-term memory nor by continued inhibition between short-term memory representations. These results indicate that retrieval of memory representations inhibits other representations in short-term memory. We discuss mechanisms of retrieval-induced inhibition and their implications for the structure of memory. © The Author(s) 2015.

  19. The Precategorical Nature of Visual Short-Term Memory

    Science.gov (United States)

    Quinlan, Philip T.; Cohen, Dale J.

    2016-01-01

    We conducted a series of recognition experiments that assessed whether visual short-term memory (VSTM) is sensitive to shared category membership of to-be-remembered (tbr) images of common objects. In Experiment 1 some of the tbr items shared the same basic level category (e.g., hand axe): Such items were no better retained than others. In the…

  20. SHORT-TERM MEMORY IS INDEPENDENT OF BRAIN PROTEIN SYNTHESIS

    Energy Technology Data Exchange (ETDEWEB)

    Davis, Hasker P.; Rosenzweig, Mark R.; Jones, Oliver W.

    1980-09-01

    Male Swiss albino CD-1 mice given a single injection of a cerebral protein synthesis inhibitor, anisomycin (ANI) (1 mg/animal), 20 min prior to single trial passive avoidance training demonstrated impaired retention at tests given 3 hr, 6 hr, 1 day, and 7 days after training. Retention was not significantly different from saline controls when tests were given 0.5 or 1.5 hr after training. Prolonging inhibition of brain protein synthesis by giving either 1 or 2 additional injections of ANI 2 or 2 and 4 hr after training did not prolong short-term retention performance. The temporal development of impaired retention in ANI treated mice could not be accounted for by drug dosage, duration of protein synthesis inhibition, or nonspecific sickness at test. In contrast to the suggestion that protein synthesis inhibition prolongs short-term memory (Quinton, 1978), the results of this experiment indicate that short-term memory is not prolonged by antibiotic drugs that inhibit cerebral protein synthesis. All evidence seems consistent with the hypothesis that short-term memory is protein synthesis independent and that the establishment of long-term memory depends upon protein synthesis during or shortly after training. Evidence for a role of protein synthesis in memory maintenance is discussed.

  1. Short-term robustness of production management systems

    NARCIS (Netherlands)

    Kleijnen, J.P.C.; Gaury, E.G.A.

    1998-01-01

    Short-term performance of a production management system for make-to-stock factories may be quantified through the service rate per shift; long-term performance through the average monthly work in process (WIP). This may yield, for example, that WIP is minimized, while the probability of the service

  2. 22 CFR 62.21 - Short-term scholars.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Short-term scholars. 62.21 Section 62.21 Foreign Relations DEPARTMENT OF STATE PUBLIC DIPLOMACY AND EXCHANGES EXCHANGE VISITOR PROGRAM Specific... programs, confer on common problems and projects, and promote professional relationships and communications...

  3. Short-term energy outlook annual supplement, 1993

    International Nuclear Information System (INIS)

    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

  4. Short-term feeding strategies and pork quality

    NARCIS (Netherlands)

    Geesink, G.H.; Buren, van R.G.C.; Savenije, B.; Verstegen, M.W.A.; Ducro, B.J.; Palen, van der J.G.P.; Hemke, G.

    2004-01-01

    Two experiments were done to determine whether short-term supplementation (5 days pre-slaughter) with magnesium acetate, or a combination of magnesium acetate, tryptophan, vitamin E and vitamin C would improve pork quality. In the first experiment the pigs (Pietrain x Yorkshire, n = 96) were fed a

  5. Decay uncovered in nonverbal short-term memory.

    Science.gov (United States)

    Mercer, Tom; McKeown, Denis

    2014-02-01

    Decay theory posits that memory traces gradually fade away over the passage of time unless they are actively rehearsed. Much recent work exploring verbal short-term memory has challenged this theory, but there does appear to be evidence for trace decay in nonverbal auditory short-term memory. Numerous discrimination studies have reported a performance decline as the interval separating two tones is increased, consistent with a decay process. However, most of this tone comparison research can be explained in other ways, without reference to decay, and these alternative accounts were tested in the present study. In Experiment 1, signals were employed toward the end of extended retention intervals to ensure that listeners were alert to the presence and frequency content of the memoranda. In Experiment 2, a mask stimulus was employed in an attempt to distinguish between a highly detailed sensory trace and a longer-lasting short-term memory, and the distinctiveness of the stimuli was varied. Despite these precautions, slow-acting trace decay was observed. It therefore appears that the mere passage of time can lead to forgetting in some forms of short-term memory.

  6. Managing Transit Ridership with Short-Term Economic Incentives

    Science.gov (United States)

    1982-08-01

    It is the purpose of this booklet to give the reader an overview of the variety, : type, and nature of short-term economic incentive programs that have been : introduced by transit properties over the past few years. 3054k, 55p.

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

    Science.gov (United States)

    Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

  8. Short-term outcomes following laparoscopic resection for colon cancer.

    LENUS (Irish Health Repository)

    Kavanagh, Dara O

    2011-03-01

    Laparoscopic resection for colon cancer has been proven to have a similar oncological efficacy compared to open resection. Despite this, it is performed by a minority of colorectal surgeons. The aim of our study was to evaluate the short-term clinical, oncological and survival outcomes in all patients undergoing laparoscopic resection for colon cancer.

  9. Scalable data-driven short-term traffic prediction

    NARCIS (Netherlands)

    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

  10. Improving creativity performance by short-term meditation

    Science.gov (United States)

    2014-01-01

    Background One form of meditation intervention, the integrative body-mind training (IBMT) has been shown to improve attention, reduce stress and change self-reports of mood. In this paper we examine whether short-term IBMT can improve performance related to creativity and determine the role that mood may play in such improvement. Methods Forty Chinese undergraduates were randomly assigned to short-term IBMT group or a relaxation training (RT) control group. Mood and creativity performance were assessed by the Positive and Negative Affect Schedule (PANAS) and Torrance Tests of Creative Thinking (TTCT) questionnaire respectively. Results As predicted, the results indicated that short-term (30 min per day for 7 days) IBMT improved creativity performance on the divergent thinking task, and yielded better emotional regulation than RT. In addition, cross-lagged analysis indicated that both positive and negative affect may influence creativity in IBMT group (not RT group). Conclusions Our results suggested that emotion-related creativity-promoting mechanism may be attributed to short-term meditation. PMID:24645871

  11. Labeling, Rehearsal, and Short-Term Memory in Retarded Children

    Science.gov (United States)

    Hagen, John W.; And Others

    1974-01-01

    A short-term memory task was used to explore the effects of verbal labeling and rehearsal on serial-position recall in mildly retarded 9-to 11-year-old children. Results support the view that verbal skills affect recall in mildly retarded children similarly to normal children. (Author/SDH)

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

    Energy Technology Data Exchange (ETDEWEB)

    1993-11-05

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the fourth quarter of 1993 through the fourth quarter of 1994. Values for the third quarter of 1993, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications.

  13. Short-term mechanisms influencing volumetric brain dynamics

    NARCIS (Netherlands)

    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.

  14. Short-term variations of radiocarbon during the last century

    International Nuclear Information System (INIS)

    Burchuladze, A.A.; Pagava, S.V.; Jurina, V.; Povinec, P.; Usacev, S.

    1982-01-01

    Radiocarbon variations related to the 11-year solar cycle during the last century are discussed. Previous investigations on short term 14 C variations in tree rings are compared with 14 C measurements in Georgian wine samples. The amplitude of 14 C variations as obtained by various authors ranges from 0.2 to about 1%. (author)

  15. Proactive Interference in Short-Term Recognition and Recall Memory

    Science.gov (United States)

    Dillon, Richard F.; Petrusic, William M.

    1972-01-01

    Purpose of study was to (a) compare the rate of increase of proactive interference over the first few trials under recall and recognition memory test conditions, (2) determine the effects of two types of distractors on short-term recognition, and (3) test memory after proactive interference had reached a stable level under each of three test…

  16. Short-Term Effects of Televised Aggression on Children's Behavior.

    Science.gov (United States)

    Liebert, Robert M.; Baron, Robert A.

    Recently collected data appear to warrant advancing some tentative conslusions concerning the short-term effects of violence in television on children: 1) children are exposed to a substantial amount of violent content on television, and they can remember and learn from such exposure; 2) correlational studies have disclosed a regular association…

  17. Short term clinical outcome of children with rotavirus infection at ...

    African Journals Online (AJOL)

    Background: Rotavirus infection is the single most common cause of acute gastroenteritis in children under five years of age. Rotavirus gastroenteritis has a high morbidity and mortality in children in Kenya. Objectives: To determine the short term clinical outcome for children admitted to Kenyatta National Hospital with ...

  18. Short-term effects of radiation in gliolalstoma spheroids

    DEFF Research Database (Denmark)

    Petterson, Stine Asferg; Jakobsen, Ida Pind; Jensen, Stine Skov

    2016-01-01

    was to investigate the short-term effects of radiation of spheroids containing tumor-initiating stem-like cells. We used a patient-derived glioblastoma stem cell enriched culture (T76) and the standard glioblastoma cell line U87. Primary spheroids were irradiated with doses between 2 and 50 Gy and assessed after two...

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

    International Nuclear Information System (INIS)

    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)

  20. Insulin Resistance Induced by Short term Fructose Feeding may not ...

    African Journals Online (AJOL)

    Fructose feeding causes insulin resistance and invariably Non-Insulin Dependent Diabetes Mellitus (NIDDM) in rats and genetically predisposed humans. The effect of insulin resistance induced by short term fructose feeding on fertility in female rats was investigated using the following parameters: oestrous phase and ...

  1. Histopathologic characteristics and short-term outcomes of ...

    African Journals Online (AJOL)

    Introduction: Colorectal carcinoma (CRC) is generally a disease of persons older than 40 years. Concerning younger patients, controversies still exist regarding features and prognosis of CRC. We performed this study to characterise CRC in young patients (≤40 years) as well as to evaluate short-term outcome in ...

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

    International Nuclear Information System (INIS)

    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

  3. Panorama 2013 - Short term trends in the gas industry

    International Nuclear Information System (INIS)

    Lecarpentier, Armelle

    2012-10-01

    The outlook for gas industry development in the short term is clouded by uncertainties (impact of the economic slowdown, competition between energies, price fluctuations, etc.). However, as in 2012, many favorable factors in terms of natural gas supply and demand point to sustained and sustainable growth of this energy. (author)

  4. Orienting attention to objects in visual short-term memory

    NARCIS (Netherlands)

    Dell'Acqua, Roberto; Sessa, Paola; Toffanin, Paolo; Luria, Roy; Joliccoeur, Pierre

    We measured electroencephalographic activity during visual search of a target object among objects available to perception or among objects held in visual short-term memory (VSTM). For perceptual search, a single shape was shown first (pre-cue) followed by a search-array and the task was to decide

  5. SHORT-TERM EFFECT OF DIESEL OIL ON PHYTOPLANKTON

    African Journals Online (AJOL)

    PROF. EKWEME

    Short-term effect of Nigerian diesel oil was tested on the phytoplankton species in Great Kwa River ... aquatic environment. Plant life is the basis of all food web in nature and hence constitutes the makes this fundamental contribution by photosynthesis, utilizing radiant energy to .... (2 cells/ml) re-colonized the area. The three ...

  6. Are there multiple visual short-term memory stores?

    NARCIS (Netherlands)

    Sligte, I.G.; Scholte, H.S.; Lamme, V.A.F.

    2008-01-01

    Background: Classic work on visual short-term memory (VSTM) suggests that people store a limited amount of items for subsequent report. However, when human observers are cued to shift attention to one item in VSTM during retention, it seems as if there is a much larger representation, which keeps

  7. Short Term Group Counseling of Visually Impaired People by Telephone.

    Science.gov (United States)

    Jaureguy, Beth M.; Evans, Ron L.

    1983-01-01

    Short term group counseling via the telephone resulted in marked increases in activities of daily living among 12 legally blind veterans. Many subjects' personal coping goals were met as well, and social involvement also increased. No significant changes in levels of depression or agitation were noted. (CL)

  8. Relationship between short-term sexual strategies and sexual jealousy.

    Science.gov (United States)

    Mathes, Eugene W

    2005-02-01

    In a classic study, Buss, Larson, Westen, and Semmelroth reported that men were more distressed by the thought of a partner's sexual infidelity (sexual jealousy) and women were more distressed by the thought of a partner's emotional infidelity (emotional jealousy). Initially, Buss and his associates explained these results by suggesting that men are concerned about uncertainty of paternity, that is, the possibility of raising another man's child while believing the child is their own. However, later they explained the results in terms of men's preference for short-term sexual strategies. The purpose of this research was to test the explanation of short-term sexual strategies. Men and women subjects were instructed to imagine themselves in a relationship which was either short-term (primarily sexual) or long-term (involving commitment) and then respond to Buss's jealousy items. It was hypothesized that, when both men and women imagined a short-term relationship, they would be more threatened by a partner's sexual infidelity, and, when they imagined a long-term relationship, they would be more threatened by a partner's emotional infidelity. Support was found for this hypothesis.

  9. High-intensity exercise and recovery during short-term ...

    African Journals Online (AJOL)

    Objective. To determine the effect of short-term creatine supplementation plus a protein-carbohydrate formula on high-intensity exercise performance and recovery. Design. A repeated-measures, experimental study, employing a randomised, double-blind, placebo-controlled, group comparison design was used.

  10. Can Metabolic Factors be used Prognostically for Short.Term ...

    African Journals Online (AJOL)

    to be promising short.term mortality markers in HIV patients apart from established factors like low CD4 counts, co.morbid conditions, and opportunistic infections like M. tuberculosis infection. This study warrants further studies with a larger sample size to establish HDL and triglyceride as markers of disease progression and ...

  11. Short term and medium term power distribution load forecasting by neural networks

    International Nuclear Information System (INIS)

    Yalcinoz, T.; Eminoglu, U.

    2005-01-01

    Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

  12. Short-term memory development: differences in serial position curves between age groups and latent classes.

    Science.gov (United States)

    Koppenol-Gonzalez, Gabriela V; Bouwmeester, Samantha; Vermunt, Jeroen K

    2014-10-01

    In studies on the development of cognitive processes, children are often grouped based on their ages before analyzing the data. After the analysis, the differences between age groups are interpreted as developmental differences. We argue that this approach is problematic because the variance in cognitive performance within an age group is considered to be measurement error. However, if a part of this variance is systematic, it can provide very useful information about the cognitive processes used by some children of a certain age but not others. In the current study, we presented 210 children aged 5 to 12 years with serial order short-term memory tasks. First we analyze our data according to the approach using age groups, and then we apply latent class analysis to form latent classes of children based on their performance instead of their ages. We display the results of the age groups and the latent classes in terms of serial position curves, and we discuss the differences in results. Our findings show that there are considerable differences in performance between the age groups and the latent classes. We interpret our findings as indicating that the latent class analysis yielded a much more meaningful way of grouping children in terms of cognitive processes than the a priori grouping of children based on their ages. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Remedial measures at the short-term regulated rivers

    International Nuclear Information System (INIS)

    Soimakallio, H.

    1995-01-01

    Building up and producing hydro power causes environmental effects, which are directed at the geomorfology, hydrology, water quality, organisms and landscape of the water system. To reduce and eliminate these various effects there are available an abundance of technical remedial measures, many of which contribute to several effects at the same time. In Finland a lot of remedial measures have been carried out at voluntary or obligatory bases. The information concerning remedial measures implemented in large build-up rivers were collected as a part of the study of the effects of the short-term regulation of hydro power plants. Material for the study was collected via literature, postal inquiry and terrain visits. Measures handled in the study were protection and reinforcement of shores, boating projects, submerged weirs, improvement of water turnover, fishery, clearing of peat rafts and stubs, landscaping, maintaining ice roads and shaping river banks. Nowadays planning and implementation of the remedial measures varies greatly depending on the nature and extent of the project. Large projects, which are more expensive, are naturally planned more carefully and comprehensively than simple routine measures. Also the quality of follow-up of the sites changes and the main portion of the information is received through terrain checks and direct feed-back from the users of the water system. In the future there is a need for model plans of the different routine measures. Also a systemic method to evaluate and compare different actions is needed to help decision making and to solve possible conflicts between different interests. Fishery, which is generally managed well, must in the future utilize better possibilities offered by other measures. According to the study there is no particular need to develop the follow-up systems. However, if the follow-up information is going to be used to develop the measures further, more systematic systems are needed for follow-up. (author)

  14. Short-term load forecasting of power system

    Science.gov (United States)

    Xu, Xiaobin

    2017-05-01

    In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

  15. Short term scheduling of multiple grid-parallel PEM fuel cells for microgrid applications

    Energy Technology Data Exchange (ETDEWEB)

    El-Sharkh, M.Y.; Rahman, A.; Alam, M.S. [Dept. of Electrical and Computer Engineering, University of South Alabama, Mobile, AL 36688 (United States)

    2010-10-15

    This paper presents a short term scheduling scheme for multiple grid-parallel PEM fuel cell power plants (FCPPs) connected to supply electrical and thermal energy to a microgrid community. As in the case of regular power plants, short term scheduling of FCPP is also a cost-based optimization problem that includes the cost of operation, thermal power recovery, and the power trade with the local utility grid. Due to the ability of the microgrid community to trade power with the local grid, the power balance constraint is not applicable, other constraints like the real power operating limits of the FCPP, and minimum up and down time are therefore used. To solve the short term scheduling problem of the FCPPs, a hybrid technique based on evolutionary programming (EP) and hill climbing technique (HC) is used. The EP is used to estimate the optimal schedule and the output power from each FCPP. The HC technique is used to monitor the feasibility of the solution during the search process. The short term scheduling problem is used to estimate the schedule and the electrical and thermal power output of five FCPPs supplying a maximum power of 300 kW. (author)

  16. Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

    Science.gov (United States)

    Rußwurm, M.; Körner, M.

    2017-05-01

    Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  17. MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    M. Rußwurm

    2017-05-01

    Full Text Available Land cover classification (LCC is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN, with a classical non-temporal convolutional neural network (CNN model and an additional support vector machine (SVM baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

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

    Science.gov (United States)

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

    2008-01-01

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

  19. Security threats to automotive CAN networks-Practical examples and selected short-term countermeasures

    International Nuclear Information System (INIS)

    Hoppe, Tobias; Kiltz, Stefan; Dittmann, Jana

    2011-01-01

    The IT security of automotive systems is an evolving area of research. To analyse the current situation and the potentially growing tendency of arising threats we performed several practical tests on recent automotive technology. With a focus on automotive systems based on CAN bus technology, this article summarises the results of four selected tests performed on the control systems for the window lift, warning light and airbag control system as well as the central gateway. These results are supplemented in this article by a classification of these four attack scenarios using the established CERT taxonomy and an analysis of underlying security vulnerabilities, and especially, potential safety implications. With respect to the results of these tests, in this article we further discuss two selected countermeasures to address basic weaknesses exploited in our tests. These are adaptations of intrusion detection (discussing three exemplary detection patterns) and IT-forensic measures (proposing proactive measures based on a forensic model). This article discusses both looking at the four attack scenarios introduced before, covering their capabilities and restrictions. While these reactive approaches are short-term measures, which could already be added to today's automotive IT architecture, long-term concepts also are shortly introduced, which are mainly preventive but will require a major redesign. Beneath a short overview on respective research approaches, we discuss their individual requirements, potential and restrictions.

  20. An adaptive random search for short term generation scheduling with network constraints.

    Directory of Open Access Journals (Sweden)

    J A Marmolejo

    Full Text Available This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.