A Neural Network Model of the Visual Short-Term Memory

DEFF Research Database (Denmark)

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

2009-01-01

In this paper a neural network model of Visual Short-Term Memory (VSTM) is presented. The model links closely with Bundesen’s (1990) well-established mathematical theory of visual attention. We evaluate the model’s ability to fit experimental data from a classical whole and partial report study...

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.

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.

Music Learning with Long Short Term Memory Networks

OpenAIRE

Colombo, Florian François

2015-01-01

Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long short-term memory (LSTM) artificial neural networks have been shown to be efficient in sequence learning tasks thanks to their inherent ability to bridge long time lags between input events and their target signals. Here, I investigate the po...

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

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Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

Surface ozone in China: present-day distribution and long-term changes

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Xu, X.; Lin, W.; Xu, W.

2017-12-01

Reliable knowledge of spatio-temporal variations of surface ozone is highly needed to assess the impacts of ozone on human health, ecosystem and climate. Although regional distributions and trends of surface ozone in European and North American countries have been well characterized, little is known about the variability of surface ozone in many other countries, including China, where emissions of ozone precursors have been changing rapidly in recent decades. Here we present the first comprehensive description of present-day (2013-2017) distribution and long-term changes of surface ozone in mainland China. Recent ozone measurements from China's air quality monitoring network (AQMN) are analyzed to show present-day distributions of a few ozone exposure metrics for urban environment. Long-term measurements of ozone at six background sites, a rural site and an urban are used to study the trends of ozone in background, rural and urban air, respectively. The average levels of ozone at the AQMN sites (mainly urban) are close to those found at many European and North American sites. However, ozone at most of the sites shows very large diurnal and seasonal variations so that ozone nonattainment can occur in many cities, particularly those in the North China Plain (NCP), the south of Northeast China (NEC), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Sichuan Basin-Chongqing region (SCB). In all these regions, particularly in the NCP, the maximum daily 8-h average (MDA8) ozone concentration can significantly exceed the national limit (75 ppb). High annual sum of ozone means over 35 ppb (SOMO35) exist mainly in the NCP, NEC and YRD, with regional averages over 4000 ppb·d. Surface ozone has significantly increased at Waliguan (a baseline site in western China) and Shangdianzi (a background site in the NCP), and decreased in winter and spring at Longfengshan (a background site in Northeast China). No clear trend can be derived from long-term measurements

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...

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

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Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Short-term memory of motor network performance via activity-dependent potentiation of Na+/K+ pump function.

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Zhang, Hong-Yan; Sillar, Keith T

2012-03-20

Brain networks memorize previous performance to adjust their output in light of past experience. These activity-dependent modifications generally result from changes in synaptic strengths or ionic conductances, and ion pumps have only rarely been demonstrated to play a dynamic role. Locomotor behavior is produced by central pattern generator (CPG) networks and modified by sensory and descending signals to allow for changes in movement frequency, intensity, and duration, but whether or how the CPG networks recall recent activity is largely unknown. In Xenopus frog tadpoles, swim bout duration correlates linearly with interswim interval, suggesting that the locomotor network retains a short-term memory of previous output. We discovered an ultraslow, minute-long afterhyperpolarization (usAHP) in network neurons following locomotor episodes. The usAHP is mediated by an activity- and sodium spike-dependent enhancement of electrogenic Na(+)/K(+) pump function. By integrating spike frequency over time and linking the membrane potential of spinal neurons to network performance, the usAHP plays a dynamic role in short-term motor memory. Because Na(+)/K(+) pumps are ubiquitously expressed in neurons of all animals and because sodium spikes inevitably accompany network activity, the usAHP may represent a phylogenetically conserved but largely overlooked mechanism for short-term memory of neural network function. Copyright © 2012 Elsevier Ltd. All rights reserved.

Short-term memory capacity in networks via the restricted isometry property.

Science.gov (United States)

Charles, Adam S; Yap, Han Lun; Rozell, Christopher J

2014-06-01

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica

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Tamas Wani

2014-03-01

Full Text Available Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events. Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.

Forecasting short-term data center network traffic load with convolutional neural networks

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Ordozgoiti, Bruno; Gómez-Canaval, Sandra

2018-01-01

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution. PMID:29408936

Forecasting short-term data center network traffic load with convolutional neural networks.

Science.gov (United States)

Mozo, Alberto; Ordozgoiti, Bruno; Gómez-Canaval, Sandra

2018-01-01

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

The effects of short- and long-term air pollutants on plant phenology and leaf characteristics

International Nuclear Information System (INIS)

Jochner, Susanne; Markevych, Iana; Beck, Isabelle; Traidl-Hoffmann, Claudia

2015-01-01

Pollution adversely affects vegetation; however, its impact on phenology and leaf morphology is not satisfactorily understood yet. We analyzed associations between pollutants and phenological data of birch, hazel and horse chestnut in Munich (2010) along with the suitability of leaf morphological parameters of birch for monitoring air pollution using two datasets: cumulated atmospheric concentrations of nitrogen dioxide and ozone derived from passive sampling (short-term exposure) and pollutant information derived from Land Use Regression models (long-term exposure). Partial correlations and stepwise regressions revealed that increased ozone (birch, horse chestnut), NO_2, NO_x and PM levels (hazel) were significantly related to delays in phenology. Correlations were especially high when rural sites were excluded suggesting a better estimation of long-term within-city pollution. In situ measurements of foliar characteristics of birch were not suitable for bio-monitoring pollution. Inconsistencies between long- and short-term exposure effects suggest some caution when interpreting short-term data collected within field studies. - Highlights: • We present results of a field survey examining pollution effects on vegetation. • Particularly ozone was significantly associated with delays in spring phenology. • Leaf morphology of birch was found to be inadequate for bio-monitoring pollution. • Inconsistencies between long-/short-term exposure effects suggest caution. - Pollutants were significantly associated with delays in spring phenology. However, inconsistencies between long- and short-term exposure effects suggest some caution when interpreting results.

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...

Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

Directory of Open Access Journals (Sweden)

Jaime Buitrago

2017-01-01

Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.

Critical neural networks with short- and long-term plasticity

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Michiels van Kessenich, L.; Luković, M.; de Arcangelis, L.; Herrmann, H. J.

2018-03-01

In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1 /f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.

Order recall in verbal short-term memory: The role of semantic networks.

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Poirier, Marie; Saint-Aubin, Jean; Mair, Ali; Tehan, Gerry; Tolan, Anne

2015-04-01

In their recent article, Acheson, MacDonald, and Postle (Journal of Experimental Psychology: Learning, Memory, and Cognition 37:44-59, 2011) made an important but controversial suggestion: They hypothesized that (a) semantic information has an effect on order information in short-term memory (STM) and (b) order recall in STM is based on the level of activation of items within the relevant lexico-semantic long-term memory (LTM) network. However, verbal STM research has typically led to the conclusion that factors such as semantic category have a large effect on the number of correctly recalled items, but little or no impact on order recall (Poirier & Saint-Aubin, Quarterly Journal of Experimental Psychology 48A:384-404, 1995; Saint-Aubin, Ouellette, & Poirier, Psychonomic Bulletin & Review 12:171-177, 2005; Tse, Memory 17:874-891, 2009). Moreover, most formal models of short-term order memory currently suggest a separate mechanism for order coding-that is, one that is separate from item representation and not associated with LTM lexico-semantic networks. Both of the experiments reported here tested the predictions that we derived from Acheson et al. The findings show that, as predicted, manipulations aiming to affect the activation of item representations significantly impacted order memory.

Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks.

Science.gov (United States)

Zazo, Ruben; Lozano-Diez, Alicia; Gonzalez-Dominguez, Javier; Toledano, Doroteo T; Gonzalez-Rodriguez, Joaquin

2016-01-01

Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (∼3s). In this contribution we present an open-source, end-to-end, LSTM RNN system running on limited computational resources (a single GPU) that outperforms a reference i-vector system on a subset of the NIST Language Recognition Evaluation (8 target languages, 3s task) by up to a 26%. This result is in line with previously published research using proprietary LSTM implementations and huge computational resources, which made these former results hardly reproducible. Further, we extend those previous experiments modeling unseen languages (out of set, OOS, modeling), which is crucial in real applications. Results show that a LSTM RNN with OOS modeling is able to detect these languages and generalizes robustly to unseen OOS languages. Finally, we also analyze the effect of even more limited test data (from 2.25s to 0.1s) proving that with as little as 0.5s an accuracy of over 50% can be achieved.

Strategic Ozone Sounding Networks: Review of Design and Accomplishments

Science.gov (United States)

Thompson, Anne M.; Oltmans, Samuel J.; Tarasick, David W.; von der Gathen, Peter; Smit, Herman G. J.; Witte, Jacquelyn C.

2011-01-01

Ozone soundings are used to integrate models, satellite, aircraft and ground-based measurements for better interpretation of ozone variability, including atmospheric losses (predominantly in the stratosphere) and pollution (troposphere). A well-designed network of ozonesonde stations gives information with high vertical and horizontal resolution on a number of dynamical and chemical processes, allowing us to answer questions not possible with aircraft campaigns or current satellite technology. Strategic ozonesonde networks are discussed for high, mid- and low latitude studies. The Match sounding network was designed specifically to follow ozone depletion within the polar vortex; the standard sites are at middle to high northern hemisphere latitudes and typically operate from December through mid-March. Three mid-latitude strategic networks (the IONS series) operated over North America in July-August 2004, March-May and August 2006, and April and June-July-2008. These were designed to address questions about tropospheric ozone budgets and sources, including stratosphere-troposphere transport, and to validate satellite instruments and models. A global network focusing on processes in the equatorial zone, SHADOZ (Southern Hemisphere Additional Ozonesondes), has operated since 1998 in partnership with NOAA, NASA and the Meteorological Services of host countries. Examples of important findings from these networks are described,

LANGUAGE REPETITION AND SHORT-TERM MEMORY: AN INTEGRATIVE FRAMEWORK

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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.

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.

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

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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.

Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Cadenas, Erasmo [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro, 5000, Mor., Mich. (Mexico); Rivera, Wilfrido [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)

2009-01-15

In this paper the short term wind speed forecasting in the region of La Venta, Oaxaca, Mexico, applying the technique of artificial neural network (ANN) to the hourly time series representative of the site is presented. The data were collected by the Comision Federal de Electricidad (CFE) during 7 years through a network of measurement stations located in the place of interest. Diverse configurations of ANN were generated and compared through error measures, guaranteeing the performance and accuracy of the chosen models. First a model with three layers and seven neurons was chosen, according to the recommendations of diverse authors, nevertheless, the results were not sufficiently satisfactory so other three models were developed, consisting of three layers and six neurons, two layers and four neurons and two layers and three neurons. The simplest model of two layers, with two input neurons and one output neuron, was the best for the short term wind speed forecasting, with mean squared error and mean absolute error values of 0.0016 and 0.0399, respectively. The developed model for short term wind speed forecasting showed a very good accuracy to be used by the Electric Utility Control Centre in Oaxaca for the energy supply. (author)

Ozone time scale decomposition and trend assessment from surface observations

Science.gov (United States)

Boleti, Eirini; Hueglin, Christoph; Takahama, Satoshi

2017-04-01

Emissions of ozone precursors have been regulated in Europe since around 1990 with control measures primarily targeting to industries and traffic. In order to understand how these measures have affected air quality, it is now important to investigate concentrations of tropospheric ozone in different types of environments, based on their NOx burden, and in different geographic regions. In this study, we analyze high quality data sets for Switzerland (NABEL network) and whole Europe (AirBase) for the last 25 years to calculate long-term trends of ozone concentrations. A sophisticated time scale decomposition method, called the Ensemble Empirical Mode Decomposition (EEMD) (Huang,1998;Wu,2009), is used for decomposition of the different time scales of the variation of ozone, namely the long-term trend, seasonal and short-term variability. This allows subtraction of the seasonal pattern of ozone from the observations and estimation of long-term changes of ozone concentrations with lower uncertainty ranges compared to typical methodologies used. We observe that, despite the implementation of regulations, for most of the measurement sites ozone daily mean values have been increasing until around mid-2000s. Afterwards, we observe a decline or a leveling off in the concentrations; certainly a late effect of limitations in ozone precursor emissions. On the other hand, the peak ozone concentrations have been decreasing for almost all regions. The evolution in the trend exhibits some differences between the different types of measurement. In addition, ozone is known to be strongly affected by meteorology. In the applied approach, some of the meteorological effects are already captured by the seasonal signal and already removed in the de-seasonalized ozone time series. For adjustment of the influence of meteorology on the higher frequency ozone variation, a statistical approach based on Generalized Additive Models (GAM) (Hastie,1990;Wood,2006), which corrects for meteorological

Hypothalamus-Related Resting Brain Network Underlying Short-Term Acupuncture Treatment in Primary Hypertension

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Hongyan Chen

2013-01-01

Full Text Available The present study attempted to explore modulated hypothalamus-seeded resting brain network underlying the cardiovascular system in primary hypertensive patients after short-term acupuncture treatment. Thirty right-handed patients (14 male were divided randomly into acupuncture and control groups. The acupuncture group received a continuous five-day acupuncture treatment and undertook three resting-state fMRI scans and 24-hour ambulatory blood pressure monitoring (ABPM as well as SF-36 questionnaires before, after, and one month after acupuncture treatment. The control group undertook fMRI scans and 24-hour ABPM. For verum acupuncture, average blood pressure (BP and heart rate (HR decreased after treatment but showed no statistical differences. There were no significant differences in BP and HR between the acupuncture and control groups. Notably, SF-36 indicated that bodily pain (P = 0.005 decreased and vitality (P = 0.036 increased after acupuncture compared to the baseline. The hypothalamus-related brain network showed increased functional connectivity with the medulla, brainstem, cerebellum, limbic system, thalamus, and frontal lobes. In conclusion, short-term acupuncture did not decrease BP significantly but appeared to improve body pain and vitality. Acupuncture may regulate the cardiovascular system through a complicated brain network from the cortical level, the hypothalamus, and the brainstem.

Neuroticism and conscientiousness respectively constrain and facilitate short-term plasticity within the working memory neural network.

Science.gov (United States)

Dima, Danai; Friston, Karl J; Stephan, Klaas E; Frangou, Sophia

2015-10-01

Individual differences in cognitive efficiency, particularly in relation to working memory (WM), have been associated both with personality dimensions that reflect enduring regularities in brain configuration, and with short-term neural plasticity, that reflects task-related changes in brain connectivity. To elucidate the relationship of these two divergent mechanisms, we tested the hypothesis that personality dimensions, which reflect enduring aspects of brain configuration, inform about the neurobiological framework within which short-term, task-related plasticity, as measured by effective connectivity, can be facilitated or constrained. As WM consistently engages the dorsolateral prefrontal (DLPFC), parietal (PAR), and anterior cingulate cortex (ACC), we specified a WM network model with bidirectional, ipsilateral, and contralateral connections between these regions from a functional magnetic resonance imaging dataset obtained from 40 healthy adults while performing the 3-back WM task. Task-related effective connectivity changes within this network were estimated using Dynamic Causal Modelling. Personality was evaluated along the major dimensions of Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. Only two dimensions were relevant to task-dependent effective connectivity. Neuroticism and Conscientiousness respectively constrained and facilitated neuroplastic responses within the WM network. These results suggest individual differences in cognitive efficiency arise from the interplay between enduring and short-term plasticity in brain configuration. © 2015 Wiley Periodicals, Inc.

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

International Nuclear Information System (INIS)

Tremblay, M.; Guillaud, C.

1996-01-01

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

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

OpenAIRE

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

2015-01-01

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

Applying long short-term memory recurrent neural networks to intrusion detection

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Ralf C. Staudemeyer

2015-07-01

Full Text Available We claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we experimented with various network topologies. We found networks with four memory blocks containing two cells each offer a good compromise between computational cost and detection performance. We applied forget gates and shortcut connections respectively. A learning rate of 0.1 and up to 1,000 epochs showed good results. We tested the performance on all features and on extracted minimal feature sets respectively. We evaluated different feature sets for the detection of all attacks within one network and also to train networks specialised on individual attack classes. Our results show that the LSTM classifier provides superior performance in comparison to results previously published results of strong static classifiers. With 93.82% accuracy and 22.13 cost, LSTM outperforms the winning entries of the KDD Cup ’99 challenge by far. This is due to the fact that LSTM learns to look back in time and correlate consecutive connection records. For the first time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.

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.)

Short-term effects of social encouragement on exercise behavior: insights from China's Wanbu network.

Science.gov (United States)

Wang, Liuan; Guo, Xitong; Wu, Tianshi; Lv, Lucheng; Zhang, Zhiwei

2017-07-01

The objective is to explore the short-term effects of social encouragement on exercise behavior in China. A longitudinal observational study. We collected longitudinal data on exercise and social interactions through public access to the Wanbu network, a large Chinese social network designed to encourage people to walk more. Our data set consisted of 5010 subjects who participated in the network between March 14, 2014, and September 4, 2015, and had at least one social interaction recorded. The data were analyzed using linear regression models relating the number of steps (NS) walked per day to the number of comments (NC), number of thumbs-up (NT), and number of posts (NP) received on the previous day, while adjusting for day of week, quarter of year, and a fixed or random subject effect, with or without a lag term (NS on the previous day) to account for serial correlation. We found that all three social interactions have positive effects on the next day's exercise level. The estimated effect sizes can be ordered as NT > NC > NP for each of the four models considered. The results also indicate that the participants walked less in the first quarter than in the other three quarters and more on weekdays than on weekends, with Monday being the most active day of a week. Social encouragement has positive short-term effects on exercise behavior. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

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

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Zhibin Yu

2017-08-01

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

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

Science.gov (United States)

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

2017-10-01

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

Coherent oscillatory networks supporting short-term memory retention.

Science.gov (United States)

Payne, Lisa; Kounios, John

2009-01-09

Accumulating evidence suggests that top-down processes, reflected by frontal-midline theta-band (4-8 Hz) electroencephalogram (EEG) oscillations, strengthen the activation of a memory set during short-term memory (STM) retention. In addition, the amplitude of posterior alpha-band (8-13 Hz) oscillations during STM retention is thought to reflect a mechanism that protects fragile STM activations from interference by gating bottom-up sensory inputs. The present study addressed two important questions about these phenomena. First, why have previous studies not consistently found memory set-size effects on frontal-midline theta? Second, how does posterior alpha participate in STM retention? To answer these questions, large-scale network connectivity during STM retention was examined by computing EEG wavelet coherence during the retention period of a modified Sternberg task using visually-presented letters as stimuli. The results showed (a) increasing theta-band coherence between frontal-midline and left temporal-parietal sites with increasing memory load, and (b) increasing alpha-band coherence between midline parietal and left temporal/parietal sites with increasing memory load. These findings support the view that theta-band coherence, rather than amplitude, is the key factor in selective top-down strengthening of the memory set and demonstrate that posterior alpha-band oscillations associated with sensory gating are involved in STM retention by participating in the STM network.

Long-term exposure to ambient ozone and mortality: a quantitative systematic review and meta-analysis of evidence from cohort studies.

Science.gov (United States)

Atkinson, R W; Butland, B K; Dimitroulopoulou, C; Heal, M R; Stedman, J R; Carslaw, N; Jarvis, D; Heaviside, C; Vardoulakis, S; Walton, H; Anderson, H R

2016-02-23

While there is good evidence for associations between short-term exposure to ozone and a range of adverse health outcomes, the evidence from narrative reviews for long-term exposure is suggestive of associations with respiratory mortality only. We conducted a systematic, quantitative evaluation of the evidence from cohort studies, reporting associations between long-term exposure to ozone and mortality. Cohort studies published in peer-reviewed journals indexed in EMBASE and MEDLINE to September 2015 and PubMed to October 2015 and cited in reviews/key publications were identified via search strings using terms relating to study design, pollutant and health outcome. Study details and estimate information were extracted and used to calculate standardised effect estimates expressed as HRs per 10 ppb increment in long-term ozone concentrations. 14 publications from 8 cohorts presented results for ozone and all-cause and cause-specific mortality. We found no evidence of associations between long-term annual O3 concentrations and the risk of death from all causes, cardiovascular or respiratory diseases, or lung cancer. 4 cohorts assessed ozone concentrations measured during the warm season. Summary HRs for cardiovascular and respiratory causes of death derived from 3 cohorts were 1.01 (95% CI 1.00 to 1.02) and 1.03 (95% CI 1.01 to 1.05) per 10 ppb, respectively. Our quantitative review revealed a paucity of independent studies regarding the associations between long-term exposure to ozone and mortality. The potential impact of climate change and increasing anthropogenic emissions of ozone precursors on ozone levels worldwide suggests further studies of the long-term effects of exposure to high ozone levels are warranted. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

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.

Effect of regional precursor emission controls on long-range ozone transport – Part 2: Steady-state changes in ozone air quality and impacts on human mortality

Directory of Open Access Journals (Sweden)

J. J. West

2009-08-01

Full Text Available Large-scale changes in ozone precursor emissions affect ozone directly in the short term, and also affect methane, which in turn causes long-term changes in ozone that affect surface ozone air quality. Here we assess the effects of changes in ozone precursor emissions on the long-term change in surface ozone via methane, as a function of the emission region, by modeling 10% reductions in anthropogenic nitrogen oxide (NO_x emissions from each of nine world regions. Reductions in NO_x emissions from all world regions increase methane and long-term surface ozone. While this long-term increase is small compared to the intra-regional short-term ozone decrease, it is comparable to or larger than the short-term inter-continental ozone decrease for some source-receptor pairs. The increase in methane and long-term surface ozone per ton of NO_x reduced is greatest in tropical and Southern Hemisphere regions, exceeding that from temperate Northern Hemisphere regions by roughly a factor of ten. We also assess changes in premature ozone-related human mortality associated with regional precursor reductions and long-range transport, showing that for 10% regional NO_x reductions, the strongest inter-regional influence is for emissions from Europe affecting mortalities in Africa. Reductions of NO_x in North America, Europe, the Former Soviet Union, and Australia are shown to reduce more mortalities outside of the source regions than within. Among world regions, NO_x reductions in India cause the greatest number of avoided mortalities per ton, mainly in India itself. Finally, by increasing global methane, NO_x reductions in one hemisphere tend to cause long-term increases in ozone concentration and mortalities in the opposite hemisphere. Reducing emissions of methane, and to a lesser extent carbon monoxide and non-methane volatile organic compounds, alongside NO_x reductions would

Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition

Directory of Open Access Journals (Sweden)

Jiasong Zhu

2018-06-01

Full Text Available Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest deep learning techniques. Unlike traditional traffic analysis methods which rely on low-resolution videos captured by road cameras, we capture 4K ( 3840 × 2178 traffic videos at a busy road intersection of a modern megacity by flying a unmanned aerial vehicle (UAV during the rush hours. We then manually annotate locations and types of road vehicles. The proposed method consists of the following three steps: (1 vehicle detection and type recognition based on deep neural networks; (2 vehicle tracking by data association and vehicle trajectory modeling; (3 vehicle behavior recognition by nearest neighbor search and by bidirectional long short-term memory network, respectively. This paper also presents experimental results of the proposed framework in comparison with state-of-the-art approaches on the 4K testing traffic video, which demonstrated the effectiveness and superiority of the proposed method.

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.

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.

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

Short-Term and Long-Term Forecasting for the 3D Point Position Changing by Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Eleni-Georgia Alevizakou

2018-03-01

Full Text Available Forecasting is one of the most growing areas in most sciences attracting the attention of many researchers for more extensive study. Therefore, the goal of this study is to develop an integrated forecasting methodology based on an Artificial Neural Network (ANN, which is a modern and attractive intelligent technique. The final result is to provide short-term and long-term forecasts for point position changing, i.e., the displacement or deformation of the surface they belong to. The motivation was the combination of two thoughts, the insertion of the forecasting concept in Geodesy as in the most scientific disciplines (e.g., Economics, Medicine and the desire to know the future position of any point on a construction or on the earth’s crustal. This methodology was designed to be accurate, stable and general for different kind of geodetic data. The basic procedure consists of the definition of the forecasting problem, the preliminary data analysis (data pre-processing, the definition of the most suitable ANN, its evaluation using the proper criteria and finally the production of forecasts. The methodology gives particular emphasis on the stages of the pre-processing and the evaluation. Additionally, the importance of the prediction intervals (PI is emphasized. A case study, which includes geodetic data from the year 2003 to the year 2016—namely X, Y, Z coordinates—is implemented. The data were acquired by 1000 permanent Global Navigation Satellite System (GNSS stations. During this case study, 2016 ANNs—with different hyper-parameters—are trained and tested for short-term forecasting and 2016 for long-term forecasting, for each of the GNSS stations. In addition, other conventional statistical forecasting methods are used for the same purpose using the same data set. Finally the most appropriate Non-linear Autoregressive Recurrent network (NAR or Non-linear Autoregressive with eXogenous inputs (NARX for the forecasting of 3D point

Neural networks engaged in short-term memory rehearsal are disrupted by irrelevant speech in human subjects.

Science.gov (United States)

Kopp, Franziska; Schröger, Erich; Lipka, Sigrid

2004-01-02

Rehearsal mechanisms in human short-term memory are increasingly understood in the light of both behavioural and neuroanatomical findings. However, little is known about the cooperation of participating brain structures and how such cooperations are affected when memory performance is disrupted. In this paper we use EEG coherence as a measure of synchronization to investigate rehearsal processes and their disruption by irrelevant speech in a delayed serial recall paradigm. Fronto-central and fronto-parietal theta (4-7.5 Hz), beta (13-20 Hz), and gamma (35-47 Hz) synchronizations are shown to be involved in our short-term memory task. Moreover, the impairment in serial recall due to irrelevant speech was preceded by a reduction of gamma band coherence. Results suggest that the irrelevant speech effect has its neural basis in the disruption of left-lateralized fronto-central networks. This stresses the importance of gamma band activity for short-term memory operations.

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

Science.gov (United States)

Wang, Jin-Hui; Zuo, Xi-Nian; Gohel, Suril; Milham, Michael P.; Biswal, Bharat B.; He, Yong

2011-01-01

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest. PMID:21818285

Association of short-term exposure to ground-level ozone and respiratory outpatient clinic visits in a rural location – Sublette County, Wyoming, 2008–2011

Energy Technology Data Exchange (ETDEWEB)

Pride, Kerry R., E-mail: hgp3@cdc.gov [Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, GA (United States); Wyoming Department of Health, 6101 Yellowstone Road, Suite 510, Cheyenne, WY 82002 (United States); Peel, Jennifer L. [Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523 (United States); Robinson, Byron F. [Scientific Education and Professional Development Program Office, Office of Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, 1600 Clifton Rd, NE, E-92, Atlanta, GA 30333 (United States); Busacker, Ashley [Field Support Branch, Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Wyoming Department of Health, 6101 Yellowstone Road, Suite 510, Cheyenne, WY 82002 (United States); Grandpre, Joseph [Chronic Disease Epidemiologist, Wyoming Department of Health, 6101 Yellowstone Road, Suite 510, Cheyenne, WY 82002 (United States); Bisgard, Kristine M. [Scientific Education and Professional Development Program Office, Office of Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, 600 Clifton Road, NE, E-92, Atlanta, GA 30333 (United States); Yip, Fuyuen Y. [Air Pollution and Respiratory Disease Branch, Centers for Disease Control and Prevention, 600 Clifton Rd, NE, E-92, Atlanta, GA 30333 (United States); Murphy, Tracy D. [Wyoming Department of Health, 101 Yellowstone Road, Suite 510, Cheyenne, WY 82002 (United States)

2015-02-15

Objective: Short-term exposure to ground-level ozone has been linked to adverse respiratory and other health effects; previous studies typically have focused on summer ground-level ozone in urban areas. During 2008–2011, Sublette County, Wyoming (population: ~10,000 persons), experienced periods of elevated ground-level ozone concentrations during the winter. This study sought to evaluate the association of daily ground-level ozone concentrations and health clinic visits for respiratory disease in this rural county. Methods: Clinic visits for respiratory disease were ascertained from electronic billing records of the two clinics in Sublette County for January 1, 2008–December 31, 2011. A time-stratified case-crossover design, adjusted for temperature and humidity, was used to investigate associations between ground-level ozone concentrations measured at one station and clinic visits for a respiratory health concern by using an unconstrained distributed lag of 0–3 days and single-day lags of 0 day, 1 day, 2 days, and 3 days. Results: The data set included 12,742 case-days and 43,285 selected control-days. The mean ground-level ozone observed was 47±8 ppb. The unconstrained distributed lag of 0–3 days was consistent with a null association (adjusted odds ratio [aOR]: 1.001; 95% confidence interval [CI]: 0.990–1.012); results for lags 0, 2, and 3 days were consistent with the null. However, the results for lag 1 were indicative of a positive association; for every 10-ppb increase in the 8-h maximum average ground-level ozone, a 3.0% increase in respiratory clinic visits the following day was observed (aOR: 1.031; 95% CI: 0.994–1.069). Season modified the adverse respiratory effects: ground-level ozone was significantly associated with respiratory clinic visits during the winter months. The patterns of results from all sensitivity analyzes were consistent with the a priori model. Conclusions: The results demonstrate an association of increasing ground

Association of short-term exposure to ground-level ozone and respiratory outpatient clinic visits in a rural location – Sublette County, Wyoming, 2008–2011

International Nuclear Information System (INIS)

Pride, Kerry R.; Peel, Jennifer L.; Robinson, Byron F.; Busacker, Ashley; Grandpre, Joseph; Bisgard, Kristine M.; Yip, Fuyuen Y.; Murphy, Tracy D.

2015-01-01

Objective: Short-term exposure to ground-level ozone has been linked to adverse respiratory and other health effects; previous studies typically have focused on summer ground-level ozone in urban areas. During 2008–2011, Sublette County, Wyoming (population: ~10,000 persons), experienced periods of elevated ground-level ozone concentrations during the winter. This study sought to evaluate the association of daily ground-level ozone concentrations and health clinic visits for respiratory disease in this rural county. Methods: Clinic visits for respiratory disease were ascertained from electronic billing records of the two clinics in Sublette County for January 1, 2008–December 31, 2011. A time-stratified case-crossover design, adjusted for temperature and humidity, was used to investigate associations between ground-level ozone concentrations measured at one station and clinic visits for a respiratory health concern by using an unconstrained distributed lag of 0–3 days and single-day lags of 0 day, 1 day, 2 days, and 3 days. Results: The data set included 12,742 case-days and 43,285 selected control-days. The mean ground-level ozone observed was 47±8 ppb. The unconstrained distributed lag of 0–3 days was consistent with a null association (adjusted odds ratio [aOR]: 1.001; 95% confidence interval [CI]: 0.990–1.012); results for lags 0, 2, and 3 days were consistent with the null. However, the results for lag 1 were indicative of a positive association; for every 10-ppb increase in the 8-h maximum average ground-level ozone, a 3.0% increase in respiratory clinic visits the following day was observed (aOR: 1.031; 95% CI: 0.994–1.069). Season modified the adverse respiratory effects: ground-level ozone was significantly associated with respiratory clinic visits during the winter months. The patterns of results from all sensitivity analyzes were consistent with the a priori model. Conclusions: The results demonstrate an association of increasing ground

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.

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.

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

Short-term wind power forecasting in Portugal by neural networks and wavelet transform

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)

2011-04-15

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (author)

Short-term depression and transient memory in sensory cortex.

Science.gov (United States)

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

2017-12-01

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

Short- and Medium-term Atmospheric Effects of Very Large Solar Proton Events

Science.gov (United States)

Jackman, Charles H.; Marsh, Daniel R.; Vitt, Francis M.; Garcia, Rolando R.; Fleming, Eric L.; Labow, Gordon J.; Randall, Cora E.; Lopez-Puertas, Manuel; Funke, Bernd

2007-01-01

Long-term variations in ozone have been caused by both natural and humankind related processes. In particular, the humankind or anthropogenic influence on ozone from chlorofluorocarbons and halons (chlorine and bromine) has led to international regulations greatly limiting the release of these substances. These anthropogenic effects on ozone are most important in polar regions and have been significant since the 1970s. Certain natural ozone influences are also important in polar regions and are caused by the impact of solar charged particles on the atmosphere. Such natural variations have been studied in order to better quantify the human influence on polar ozone. Large-scale explosions on the Sun near solar maximum lead to emissions of charged particles (mainly protons and electrons), some of which enter the Earth's magnetosphere and rain down on the polar regions. "Solar proton events" have been used to describe these phenomena since the protons associated with these solar events sometimes create a significant atmospheric disturbance. We have used the National Center for Atmospheric Research (NCAR) Whole Atmosphere Community Climate Model (WACCM) to study the short- and medium-term (days to a few months) influences of solar proton events between 1963 and 2005 on stratospheric ozone. The four largest events in the past 45 years (August 1972; October 1989; July 2000; and October-November 2003) caused very distinctive polar changes in layers of the Earth's atmosphere known as the stratosphere (12-50 km; -7-30 miles) and mesosphere (50-90 km; 30-55 miles). The solar protons connected with these events created hydrogen- and nitrogen- containing compounds, which led to the polar ozone destruction. The hydrogen-containing compounds have very short lifetimes and lasted for only a few days (typically the duration of the solar proton event). On the other hand, the nitrogen-containing compounds lasted much longer, especially in the Winter. The nitrogen oxides were predicted

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.

A novel analytical characterization for short-term plasticity parameters in spiking neural networks.

Science.gov (United States)

O'Brien, Michael J; Thibeault, Corey M; Srinivasa, Narayan

2014-01-01

Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse, suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random, asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search.

Attention supports verbal short-term memory via competition between dorsal and ventral attention networks.

Science.gov (United States)

Majerus, Steve; Attout, Lucie; D'Argembeau, Arnaud; Degueldre, Christian; Fias, Wim; Maquet, Pierre; Martinez Perez, Trecy; Stawarczyk, David; Salmon, Eric; Van der Linden, Martial; Phillips, Christophe; Balteau, Evelyne

2012-05-01

Interactions between the neural correlates of short-term memory (STM) and attention have been actively studied in the visual STM domain but much less in the verbal STM domain. Here we show that the same attention mechanisms that have been shown to shape the neural networks of visual STM also shape those of verbal STM. Based on previous research in visual STM, we contrasted the involvement of a dorsal attention network centered on the intraparietal sulcus supporting task-related attention and a ventral attention network centered on the temporoparietal junction supporting stimulus-related attention. We observed that, with increasing STM load, the dorsal attention network was activated while the ventral attention network was deactivated, especially during early maintenance. Importantly, activation in the ventral attention network increased in response to task-irrelevant stimuli briefly presented during the maintenance phase of the STM trials but only during low-load STM conditions, which were associated with the lowest levels of activity in the dorsal attention network during encoding and early maintenance. By demonstrating a trade-off between task-related and stimulus-related attention networks during verbal STM, this study highlights the dynamics of attentional processes involved in verbal STM.

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.

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.

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.

Variability of the stratospheric ozone in Colombia

International Nuclear Information System (INIS)

Aristizabal, Gloria Leon

2002-01-01

In this study has been examined the causes of ozone variations and the sign that represent in the short-term, seasonal, interannual, decadal and long-term variability. The analysis of NASA satellite data sets, obtained with the total ozone mapping spectrometer toms, for the period 1979-1999, they permit to deduce to the total column ozone that in Colombia, varies among 255 and 267 U.D., and presents synchronous variations with the quasi biennial oscillation (QBO) and in the series not any tendency with the time is recognized

Long-term meteorologically independent trend analysis of ozone air quality at an urban site in the greater Houston area.

Science.gov (United States)

Botlaguduru, Venkata S V; Kommalapati, Raghava R; Huque, Ziaul

2018-04-19

�± 0.005 ppb/yr for the overall period of 1990-2016. Implications Statement The effectiveness of air emission controls can be evaluated by developing long-term air quality trends independent of meteorological influences. KZ filter technique is a well-established method to separate an air quality time-series into: short-term, seasonal and long-term components. This paper applies the KZ filter technique to MDA8 ozone data between 1990-2016 at an urban site in the Greater Houston area and estimates the variance accounted for, by the primary meteorological control variables. Estimates for linear trends of MDA8 ozone are calculated and underlying causes are investigated to provide a guidance for further investigation into air quality management of the Greater Houston Area.

Tropospheric Ozone Assessment Report: Database and Metrics Data of Global Surface Ozone Observations

Directory of Open Access Journals (Sweden)

Martin G. Schultz

2017-10-01

Full Text Available In support of the first Tropospheric Ozone Assessment Report (TOAR a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of 'in-situ' hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of 'a posteriori' data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface

Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

Science.gov (United States)

Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

2017-03-01

Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Ozone modelling in Eastern Austria

Energy Technology Data Exchange (ETDEWEB)

Stohl, A.; Wotawa, G.; Kromp-Kolb, H. [Univ. of Agriculture, Vienna (Austria). Inst. of Meteorology and Physics; Winiwater, W. [Austrian Research Centre, Seibersdorf (Austria); Baumann, R.; Spangl, W. [Federal Environmental Agency, Vienna (Austria)

1995-12-31

High ozone concentrations are frequently observed in Eastern Austria, often exceeding local as well as international health standards, both for short-term as well as for long-term exposures. The maximum concentrations are produced in urban plumes, e.g. of the city of Vienna, whereas regional-scale transport and production of ozone is more important for the long-term concentrations. The Pannonian Ozone Project (POP) is an Austrian research initiative to model photochemical processes on a regional as well as on a local scale with a Lagrangian model to better understand the mechanisms leading to the high ozone concentrations and to develop abatement strategies. Up to now, focus has been on the regional scale. Aircraft, tethered balloon, tetroon and intensified ground measurements are carried out to validate the model. Although the major measurement campaign will be held in summer 1995, first results from a measurement campaign in summer 1994 are already available

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...

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

Directory of Open Access Journals (Sweden)

Jinxing Shen

2013-01-01

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

Reconciling long-term cultural diversity and short-term collective social behavior.

Science.gov (United States)

Valori, Luca; Picciolo, Francesco; Allansdottir, Agnes; Garlaschelli, Diego

2012-01-24

An outstanding open problem is whether collective social phenomena occurring over short timescales can systematically reduce cultural heterogeneity in the long run, and whether offline and online human interactions contribute differently to the process. Theoretical models suggest that short-term collective behavior and long-term cultural diversity are mutually excluding, since they require very different levels of social influence. The latter jointly depends on two factors: the topology of the underlying social network and the overlap between individuals in multidimensional cultural space. However, while the empirical properties of social networks are intensively studied, little is known about the large-scale organization of real societies in cultural space, so that random input specifications are necessarily used in models. Here we use a large dataset to perform a high-dimensional analysis of the scientific beliefs of thousands of Europeans. We find that interopinion correlations determine a nontrivial ultrametric hierarchy of individuals in cultural space. When empirical data are used as inputs in models, ultrametricity has strong and counterintuitive effects. On short timescales, it facilitates a symmetry-breaking phase transition triggering coordinated social behavior. On long timescales, it suppresses cultural convergence by restricting it within disjoint groups. Moreover, ultrametricity implies that these results are surprisingly robust to modifications of the dynamical rules considered. Thus the empirical distribution of individuals in cultural space appears to systematically optimize the coexistence of short-term collective behavior and long-term cultural diversity, which can be realized simultaneously for the same moderate level of mutual influence in a diverse range of online and offline settings.

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 ...

A New Delay Connection for Long Short-Term Memory Networks.

Science.gov (United States)

Wang, Jianyong; Zhang, Lei; Chen, Yuanyuan; Yi, Zhang

2017-12-17

Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters. First, it allows the output of the DCLSTM unit to maintain LSTM, which is absent in the LSTM unit. Second, the proposed delay connection helps to bridge the error signals to previous time steps and allows it to be back-propagated across several layers without vanishing too quickly. To evaluate the performance of the proposed delay connections, the DCLSTM model with and without peephole connections was compared with four state-of-the-art recurrent model on two sequence classification tasks. DCLSTM model outperformed the other models with higher accuracy and F1[Formula: see text]score. Furthermore, the networks with multiple stacked DCLSTM layers and the standard LSTM layer were evaluated on Penn Treebank (PTB) language modeling. The DCLSTM model achieved lower perplexity (PPL)/bit-per-character (BPC) than the standard LSTM model. The experiments demonstrate that the learning of the DCLSTM models is more stable and efficient.

Ozone and atmospheric pollution at synoptic scale: the monitoring network Paes

International Nuclear Information System (INIS)

Gheusi, F.; Chevalier, A.; Delmas, R.; Athier, G.; Bouchou, P.; Cousin, J.M.; Meyerfeld, Y.; Laj, P.; Sellegri, K.; Ancellet, G.

2007-01-01

Ozone as an environmental concern extends beyond the questions usually covered by media - stratospheric ozone depletion and urban pollution peaks. Strong expositions to this pollutant are frequent even far from pollution sources, and the background tropospheric content of ozone has been growing fivefold over the last century. In response to this concern at the French national scale, formerly independent monitoring stations have been coordinated since 2004 in a structured network: Paes (French acronym for atmospheric pollution at synoptic scale). The data are put in free access online. (authors)

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.

Simulation of stratospheric water vapor trends: impact on stratospheric ozone chemistry

Directory of Open Access Journals (Sweden)

A. Stenke

2005-01-01

Full Text Available A transient model simulation of the 40-year time period 1960 to 1999 with the coupled climate-chemistry model (CCM ECHAM4.L39(DLR/CHEM shows a stratospheric water vapor increase over the last two decades of 0.7 ppmv and, additionally, a short-term increase after major volcanic eruptions. Furthermore, a long-term decrease in global total ozone as well as a short-term ozone decline in the tropics after volcanic eruptions are modeled. In order to understand the resulting effects of the water vapor changes on lower stratospheric ozone chemistry, different perturbation simulations were performed with the CCM ECHAM4.L39(DLR/CHEM feeding the water vapor perturbations only to the chemistry part. Two different long-term perturbations of lower stratospheric water vapor, +1 ppmv and +5 ppmv, and a short-term perturbation of +2 ppmv with an e-folding time of two months were applied. An additional stratospheric water vapor amount of 1 ppmv results in a 5–10% OH increase in the tropical lower stratosphere between 100 and 30 hPa. As a direct consequence of the OH increase the ozone destruction by the HOx cycle becomes 6.4% more effective. Coupling processes between the HOx-family and the NOx/ClOx-family also affect the ozone destruction by other catalytic reaction cycles. The NOx cycle becomes 1.6% less effective, whereas the effectiveness of the ClOx cycle is again slightly enhanced. A long-term water vapor increase does not only affect gas-phase chemistry, but also heterogeneous ozone chemistry in polar regions. The model results indicate an enhanced heterogeneous ozone depletion during antarctic spring due to a longer PSC existence period. In contrast, PSC formation in the northern hemisphere polar vortex and therefore heterogeneous ozone depletion during arctic spring are not affected by the water vapor increase, because of the less PSC activity. Finally, this study shows that 10% of the global total ozone decline in the transient model run

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)

Tropospheric ozone column retrieval at northern mid-latitudes from the Ozone Monitoring Instrument by means of a neural network algorithm

Directory of Open Access Journals (Sweden)

P. Sellitto

2011-11-01

Full Text Available Monitoring tropospheric ozone from space is of critical importance in order to gain more thorough knowledge on phenomena affecting air quality and the greenhouse effect. Deriving information on tropospheric ozone from UV/VIS nadir satellite spectrometers is difficult owing to the weak sensitivity of the measured radiance spectra to variations of ozone in the troposphere. Here we propose an alternative method of analysis to retrieve tropospheric ozone columns from Ozone Monitoring Instrument radiances by means of a neural network algorithm. An extended set of ozone sonde measurements at northern mid-latitudes for the years 2004–2008 has been considered as the training and test data set. The design of the algorithm is extensively discussed. Our retrievals are compared to both tropospheric ozone residuals and optimal estimation retrievals over a similar independent test data set. Results show that our algorithm has comparable accuracy with respect to both correlative methods and its performance is slightly better over a subset containing only European ozone sonde stations. Possible sources of errors are analyzed. Finally, the capabilities of our algorithm to derive information on boundary layer ozone are studied and the results critically discussed.

Drift-corrected Odin-OSIRIS ozone product: algorithm and updated stratospheric ozone trends

Directory of Open Access Journals (Sweden)

A. E. Bourassa

2018-01-01

Full Text Available A small long-term drift in the Optical Spectrograph and Infrared Imager System (OSIRIS stratospheric ozone product, manifested mostly since 2012, is quantified and attributed to a changing bias in the limb pointing knowledge of the instrument. A correction to this pointing drift using a predictable shape in the measured limb radiance profile is implemented and applied within the OSIRIS retrieval algorithm. This new data product, version 5.10, displays substantially better both long- and short-term agreement with Microwave Limb Sounder (MLS ozone throughout the stratosphere due to the pointing correction. Previously reported stratospheric ozone trends over the time period 1984–2013, which were derived by merging the altitude–number density ozone profile measurements from the Stratospheric Aerosol and Gas Experiment (SAGE II satellite instrument (1984–2005 and from OSIRIS (2002–2013, are recalculated using the new OSIRIS version 5.10 product and extended to 2017. These results still show statistically significant positive trends throughout the upper stratosphere since 1997, but at weaker levels that are more closely in line with estimates from other data records.

Ozone pollution and ozone biomonitoring in European cities Part II. Ozone-induced plant injury and its relationship with descriptors of ozone pollution

DEFF Research Database (Denmark)

Klumpp, A.; Ansel, W.; Klumpp, G.

2006-01-01

within local networks were relatively small, but seasonal and inter-annual differences were strong due to the variability of meteorological conditions and related ozone concentrations. The 2001 data revealed a significant relationship between foliar injury degree and various descriptors of ozone...... pollution such as mean value, AOT20 and AOT40. Examining individual sites of the local monitoring networks separately, however, yielded noticeable differences. Some sites showed no association between ozone pollution and ozone-induced effects, whereas others featured almost linear relationships...

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

Improved merit order and augmented Lagrange Hopfield network for short term hydrothermal scheduling

International Nuclear Information System (INIS)

Vo Ngoc Dieu; Ongsakul, Weerakorn

2009-01-01

This paper proposes an improved merit order (IMO) combined with an augmented Lagrangian Hopfield network (ALHN) for solving short term hydrothermal scheduling (HTS) with pumped-storage hydro plants. The proposed IMO-ALHN consists of a merit order based on the average production cost of generating units enhanced by heuristic search algorithm for finding unit scheduling and a continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation for solving constrained economic dispatch (CED). The proposed method is applied to solve the HTS problem in five stages including thermal, hydro and pumped-storage unit commitment by IMO and heuristic search, constraint violations repairing by heuristic search and CED by ALHN. The proposed method is tested on the 24-bus IEEE RTS with 32 units including 4 fuel-constrained, 4-hydro, and 2 pumped-storage units scheduled over a 24-h period. Test results indicate that the proposed IMO-ALHN is efficient for hydrothermal systems with various constraints.

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

Atmospheric chemistry of short-chain haloolefins: photochemical ozone creation potentials (POCPs), global warming potentials (GWPs), and ozone depletion potentials (ODPs).

Science.gov (United States)

Wallington, T J; Sulbaek Andersen, M P; Nielsen, O J

2015-06-01

Short-chain haloolefins are being introduced as replacements for saturated halocarbons. The unifying chemical feature of haloolefins is the presence of a CC double bond which causes the atmospheric lifetimes to be significantly shorter than for the analogous saturated compounds. We discuss the atmospheric lifetimes, photochemical ozone creation potentials (POCPs), global warming potentials (GWPs), and ozone depletion potentials (ODPs) of haloolefins. The commercially relevant short-chain haloolefins CF3CFCH2 (1234yf), trans-CF3CHCHF (1234ze(Z)), CF3CFCF2 (1216), cis-CF3CHCHCl (1233zd(Z)), and trans-CF3CHCHCl (1233zd(E)) have short atmospheric lifetimes (days to weeks), negligible POCPs, negligible GWPs, and ODPs which do not differ materially from zero. In the concentrations expected in the environment their atmospheric degradation products will have a negligible impact on ecosystems. CF3CFCH2 (1234yf), trans-CF3CHCHF (1234ze(Z)), CF3CFCF2 (1216), cis-CF3CHCHCl (1233zd(Z)), and trans-CF3CHCHCl (1233zd(E)) are environmentally acceptable. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using Long-Short-Term-Memory Recurrent Neural Networks to Predict Aviation Engine Vibrations

Science.gov (United States)

ElSaid, AbdElRahman Ahmed

This thesis examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The different networks are trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a "memory" of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 1, 5, 10, and 20 seconds in the future, with 2.84% 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation

International Nuclear Information System (INIS)

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-01-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13–24 h prediction tasks (MAPE = 31.47%). - Highlights: • Regional air pollutant concentration shows an obvious spatiotemporal correlation. • Our prediction model presents superior performance. • Climate data and metadata can significantly

Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

Science.gov (United States)

Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

2017-03-01

Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

Science.gov (United States)

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia

Science.gov (United States)

Chang, K. L.; Petropavlovskikh, I. V.; Cooper, O. R.; Schultz, M.; Wang, T.

2017-12-01

Surface ozone is a greenhouse gas and pollutant detrimental to human health and crop and ecosystem productivity. The Tropospheric Ozone Assessment Report (TOAR) is designed to provide the research community with an up-to-date observation-based overview of tropospheric ozone's global distribution and trends. The TOAR Surface Ozone Database contains ozone metrics at thousands of monitoring sites around the world, densely clustered across mid-latitude North America, western Europe and East Asia. Calculating regional ozone trends across these locations is challenging due to the uneven spacing of the monitoring sites across urban and rural areas. To meet this challenge we conducted a spatial and temporal trend analysis of several TOAR ozone metrics across these three regions for summertime (April-September) 2000-2014, using the generalized additive mixed model (GAMM). Our analysis indicates that East Asia has the greatest human and plant exposure to ozone pollution among investigating regions, with increasing ozone levels through 2014. The results also show that ozone mixing ratios continue to decline significantly over eastern North America and Europe, however, there is less evidence for decreases of daytime average ozone at urban sites. The present-day spatial coverage of ozone monitors in East Asia (South Korea and Japan) and eastern North America is adequate for estimating regional trends by simply taking the average of the individual trends at each site. However the European network is more sparsely populated across its northern and eastern regions and therefore a simple average of the individual trends at each site does not yield an accurate regional trend. This analysis demonstrates that the GAMM technique can be used to assess the regional representativeness of existing monitoring networks, indicating those networks for which a regional trend can be obtained by simply averaging the trends of all individual sites and those networks that require a more

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

Short- and long-term variability of spectral solar UV irradiance at Thessaloniki, Greece: effects of changes in aerosols, total ozone and clouds

Directory of Open Access Journals (Sweden)

I. Fountoulakis

2016-03-01

Full Text Available In this study, we discuss the short- and the long-term variability of spectral UV irradiance at Thessaloniki, Greece, using a long, quality-controlled data set from two Brewer spectrophotometers. Long-term changes in spectral UV irradiance at 307.5, 324 and 350 nm for the period 1994–2014 are presented for different solar zenith angles and discussed in association with changes in total ozone column (TOC, aerosol optical depth (AOD and cloudiness observed in the same period. Positive changes in annual mean anomalies of UV irradiance, ranging from 2 to 6 % per decade, have been detected both for clear- and all-sky conditions. The changes are generally greater for larger solar zenith angles and for shorter wavelengths. For clear-skies, these changes are, in most cases, statistically significant at the 95 % confidence limit. Decreases in the aerosol load and weakening of the attenuation by clouds lead to increases in UV irradiance in the summer, of 7–9 % per decade for 64° solar zenith angle. The increasing TOC in winter counteracts the effect of decreasing AOD for this particular season, leading to small, statistically insignificant, negative long-term changes in irradiance at 307.5 nm. Annual mean UV irradiance levels are increasing from 1994 to 2006 and remain relatively stable thereafter, possibly due to the combined changes in the amount and optical properties of aerosols. However, no statistically significant corresponding turning point has been detected in the long-term changes of AOD. The absence of signatures of changes in AOD in the short-term variability of irradiance in the UV-A may have been caused by changes in the single scattering albedo of aerosols, which may counteract the effects of changes in AOD on irradiance. The anti-correlation between the year-to-year variability of the irradiance at 307.5 nm and TOC is clear and becomes clearer as the AOD decreases.

Short-Term Memory and Its Biophysical Model

Science.gov (United States)

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

1996-12-01

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

Total Ozone Data From a European Network 1951-1957

Science.gov (United States)

Brönnimann, S.; Brönnimann, S.; Farmer, S.

2001-12-01

Soon after its foundation in 1948, the International Ozone Commission (IOC) established a total ozone network in Europe, together with the Gassiot Committee of the Royal Socitey, UNESCO, the London Meteorological Office and national services. The network was built-up in 1950 with Dobson spectrophotometers equipped with photomultipliers, which were calibrated in Oxford before shipping to the stations. In 1957, some of the stations became part of the network of the IGY, and these data can be found today at the WOUDC. The earlier data were compiled and archived in Oxford by the secretary of the IOC, Charles Normand, but have never been published and only rarely appeared in the scientific literature [Normand, QJRMS 67 (1951) 474 and QJRMS 69 (1953) 39]. The copies of the data sheets stored at UK Met Office [MO/19/3/9 Part I] comprise daily values from the following stations/time periods: Aarhus (DK, 6/52-12/59, Dobson #41), Aldergrove (UK, 6/52-4/57, #35?), Arosa (CH, 6/52-12/58 #15), Cagliari/Elmas (IT, 12/54-5/59, #48), Camborne (UK, 1/52-12/59, #32), Eskdalemuir (UK, 9/57-12/59, #35), Hemsby (UK, 6/52-9/55), Lerwick (UK, 6/52-12/59, #7), Magny les Hameaux (FR, 1/55-9/57, #49?), Messina (IT, 7/54-6/58, #46), Oxford (UK, 6/52-12/59, #1), Paris/Montsouris (FR, 10/57-8/58, #49), Reykjavik (IS, 6/52-10/59, #50), Rome/Vigna di Valle (IT, 4/54-12/59 #47), Santa Maria/Azores (ES, 2/53-7/56, #13), Spitzbergen (NO, 11/50-7/58, #8), Tromsoe (NO, 6/52-5/59, #14), Uccle (BE, 6/52-12/58, #40), and Uppsala (SE, 6/52-12/58, #30). These data could be useful to supplement the currently available total ozone measurement series. Together with existing meteorological data, they enable us to study the relation between atmospheric circulation and total ozone in a chemically largely unperturbed time period. The daily values from 1951 to 1957 have now been digitized. Using appropriate statistical methods, the quality of each series will be addressed. The data will be homogenized and re

Variational data assimilation for the optimized ozone initial state and the short-time forecasting

Directory of Open Access Journals (Sweden)

S.-Y. Park

2016-03-01

Full Text Available In this study, we apply the four-dimensional variational (4D-Var data assimilation to optimize initial ozone state and to improve the predictability of air quality. The numerical modeling systems used for simulations of atmospheric condition and chemical formation are the Weather Research and Forecasting (WRF model and the Community Multiscale Air Quality (CMAQ model. The study area covers the capital region of South Korea, where the surface measurement sites are relatively evenly distributed. The 4D-Var code previously developed for the CMAQ model is modified to consider background error in matrix form, and various numerical tests are conducted. The results are evaluated with an idealized covariance function for the appropriateness of the modified codes. The background error is then constructed using the NMC method with long-term modeling results, and the characteristics of the spatial correlation scale related to local circulation are analyzed. The background error is applied in the 4D-Var research, and a surface observational assimilation is conducted to optimize the initial concentration of ozone. The statistical results for the 12 h assimilation periods and the 120 observatory sites show a 49.4 % decrease in the root mean squared error (RMSE, and a 59.9 % increase in the index of agreement (IOA. The temporal variation of spatial distribution of the analysis increments indicates that the optimized initial state of ozone concentration is transported to inland areas by the clockwise-rotating local circulation during the assimilation windows. To investigate the predictability of ozone concentration after the assimilation window, a short-time forecasting is carried out. The ratios of the RMSE (root mean squared error with assimilation versus that without assimilation are 8 and 13 % for the +24 and +12 h, respectively. Such a significant improvement in the forecast accuracy is obtained solely by using the optimized initial state. The potential

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.

New Directions: Ozone-initiated reaction products indoors may be more harmful than ozone itself

Science.gov (United States)

Weschler, Charles J.

2004-10-01

Epidemiological studies have found associations between ozone concentrations measured at outdoor monitoring stations and certain adverse health outcomes. As a recent example, Gent et al. (2003, Journal of the American Medical Association 290, 1859-1867) have observed an association between ozone levels and respiratory symptoms as well as the use of maintenance medication by 271 asthmatic children living in Connecticut and the Springfield area of Massachusetts. In another example, Gilliland et al. (2001, Epidemiology 12, 43-54) detected an association between short-term increases in ozone levels and increased absences among 4th grade students from 12 southern California communities during the period from January to June 1996. Although children may spend a significant amount of time outdoors, especially during periods when ozone levels are elevated, they spend a much larger fraction of their time indoors. I hypothesize that exposure to the products of ozone-initiated indoor chemistry is more directly responsible for the health effects observed in the cited epidemiological studies than is exposure to outdoor ozone itself.

THE EFFECT OF OZONE ON BELOW-GROUND CARBON ALLOCATION IN WHEAT

Science.gov (United States)

Short term 14CO2 pulse and chase experiments were conducted in order to investigate the effect ozone on below-ground carbon allocation in spring wheat seedlings (Triticum aestivumL. ?ANZA'). Wheat seedlings were grown in a sand-hydroponic system and exposed to either high ozone ...

New dynamic NNORSY ozone profile climatology

Science.gov (United States)

Kaifel, A. K.; Felder, M.; Declercq, C.; Lambert, J.-C.

2012-01-01

Climatological ozone profile data are widely used as a-priori information for total ozone using DOAS type retrievals as well as for ozone profile retrieval using optimal estimation, for data assimilation or evaluation of 3-D chemistry-transport models and a lot of other applications in atmospheric sciences and remote sensing. For most applications it is important that the climatology represents not only long term mean values but also the links between ozone and dynamic input parameters. These dynamic input parameters should be easily accessible from auxiliary datasets or easily measureable, and obviously should have a high correlation with ozone. For ozone profile these parameters are mainly total ozone column and temperature profile data. This was the outcome of a user consultation carried out in the framework of developing a new, dynamic ozone profile climatology. The new ozone profile climatology is based on the Neural Network Ozone Retrieval System (NNORSY) widely used for ozone profile retrieval from UV and IR satellite sounder data. NNORSY allows implicit modelling of any non-linear correspondence between input parameters (predictors) and ozone profile target vector. This paper presents the approach, setup and validation of a new family of ozone profile climatologies with static as well as dynamic input parameters (total ozone and temperature profile). The neural network training relies on ozone profile measurement data of well known quality provided by ground based (ozonesondes) and satellite based (SAGE II, HALOE, and POAM-III) measurements over the years 1995-2007. In total, four different combinations (modes) for input parameters (date, geolocation, total ozone column and temperature profile) are available. The geophysical validation spans from pole to pole using independent ozonesonde, lidar and satellite data (ACE-FTS, AURA-MLS) for individual and time series comparisons as well as for analysing the vertical and meridian structure of different modes of

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.

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...

Global Ozone Distribution relevant to Human Health: Metrics and present day levels from the Tropospheric Ozone Assessment Report (TOAR)

Science.gov (United States)

Fleming, Z. L.; Doherty, R. M.; von Schneidemesser, E.; Cooper, O. R.; Malley, C.; Colette, A.; Xu, X.; Pinto, J. P.; Simpson, D.; Schultz, M. G.; Hamad, S.; Moola, R.; Solberg, S.; Feng, Z.

2017-12-01

Using stations from the TOAR surface ozone database, this study quantifies present-day global and regional distributions of five ozone metrics relevant for both short-term and long-term human exposure. These metrics were explored at ozone monitoring sites globally, and re-classified for this project as urban or non-urban using population densities and night-time lights. National surface ozone limit values are usually related to an annual number of exceedances of daily maximum 8-hour running mean (MDA8), with many countries not even having any ozone limit values. A discussion and comparison of exceedances in the different ozone metrics, their locations and the seasonality of exceedances provides clues as to the regions that potentially have more serious ozone health implications. Present day ozone levels (2010-2014) have been compared globally and show definite geographical differences (see Figure showing the annual 4th highest MDA8 for present day ozone for all non-urban stations). Higher ozone levels are seen in western compared to eastern US, and between southern and northern Europe, and generally higher levels in east Asia. The metrics reflective of peak concentrations show highest values in western North America, southern Europe and East Asia. A number of the metrics show similar distributions of North-South gradients, most prominent across Europe and Japan. The interquartile range of the regional ozone metrics was largest in East Asia, higher for urban stations in Asia but higher for non-urban stations in Europe and North America. With over 3000 monitoring stations included in this analysis and despite the higher densities of monitoring stations in Europe, north America and East Asia, this study provides the most comprehensive global picture to date of surface ozone levels in terms of health-relevant metrics.

Status of the Southern Carpathian forests in the long-term ecological research network

Science.gov (United States)

Ovidiu Badea; Andrzej Bytnerowicz; Diana Silaghi; Stefan Neagu; Ion Barbu; Carmen Iacoban; Corneliu Iacob; Gheorghe Guiman; Elena Preda; Ioan Seceleanu; Marian Oneata; Ion Dumitru; Viorela Huber; Horia Iuncu; Lucian Dinca; Stefan Leca; Ioan Taut

2012-01-01

Air pollution, bulk precipitation, throughfall, soil condition, foliar nutrients, as well as forest health and growth were studied in 2006–2009 in a long-term ecological research (LTER) network in the Bucegi Mountains, Romania. Ozone (O 3 ) was high indicating a potential for phytotoxicity. Ammonia (NH 3 ) concentrations rose to levels that could contribute to...

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.

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.

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.

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.

Combined Ozone Retrieval From METOP Sensors Using META-Training Of Deep Neural Networks

Science.gov (United States)

Felder, Martin; Sehnke, Frank; Kaifel, Anton

2013-12-01

The newest installment of our well-proven Neural Net- work Ozone Retrieval System (NNORSY) combines the METOP sensors GOME-2 and IASI with cloud information from AVHRR. Through the use of advanced meta- learning techniques like automatic feature selection and automatic architecture search applied to a set of deep neural networks, having at least two or three hidden layers, we have been able to avoid many technical issues normally encountered during the construction of such a joint retrieval system. This has been made possible by harnessing the processing power of modern consumer graphics cards with high performance graphic processors (GPU), which decreases training times by about two orders of magnitude. The system was trained on data from 2009 and 2010, including target ozone profiles from ozone sondes, ACE- FTS and MLS-AURA. To make maximum use of tropospheric information in the spectra, the data were partitioned into several sets of different cloud fraction ranges with the GOME-2 FOV, on which specialized retrieval networks are being trained. For the final ozone retrieval processing the different specialized networks are combined. The resulting retrieval system is very stable and does not show any systematic dependence on solar zenith angle, scan angle or sensor degradation. We present several sensitivity studies with regard to cloud fraction and target sensor type, as well as the performance in several latitude bands and with respect to independent validation stations. A visual cross-comparison against high-resolution ozone profiles from the KNMI EUMETSAT Ozone SAF product has also been performed and shows some distinctive features which we will briefly discuss. Overall, we demonstrate that a complex retrieval system can now be constructed with a minimum of ma- chine learning knowledge, using automated algorithms for many design decisions previously requiring expert knowledge. Provided sufficient training data and computation power of GPUs is available, the

Effects of short-term abatement measures on peak ozone concentrations during summer smog episodes in the Netherlands

NARCIS (Netherlands)

Smeets CJPP; Beck JP; LLO

2002-01-01

De afgelopen jaren werden de drempelwaarden voor ozon op grondniveau, zoals vastgelegd in de huidige Richtlijn 92/72/EEC, veelvuldig overschreden in alle landen van de Europese Unie. De EU verplicht alle deelnemende landen om een onderzoek te doen naar het ozon reductie potentieel van korte

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

Science.gov (United States)

Tsiouris, Κostas Μ; Pezoulas, Vasileios C; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I

2018-05-17

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Opportunistic Routing Mechanism Combined with Long-Term and Short-Term Metrics for WMN

Directory of Open Access Journals (Sweden)

Weifeng Sun

2014-01-01

Full Text Available WMN (wireless mesh network is a useful wireless multihop network with tremendous research value. The routing strategy decides the performance of network and the quality of transmission. A good routing algorithm will use the whole bandwidth of network and assure the quality of service of traffic. Since the routing metric ETX (expected transmission count does not assure good quality of wireless links, to improve the routing performance, an opportunistic routing mechanism combined with long-term and short-term metrics for WMN based on OLSR (optimized link state routing and ETX is proposed in this paper. This mechanism always chooses the highest throughput links to improve the performance of routing over WMN and then reduces the energy consumption of mesh routers. The simulations and analyses show that the opportunistic routing mechanism is better than the mechanism with the metric of ETX.

An opportunistic routing mechanism combined with long-term and short-term metrics for WMN.

Science.gov (United States)

Sun, Weifeng; Wang, Haotian; Piao, Xianglan; Qiu, Tie

2014-01-01

WMN (wireless mesh network) is a useful wireless multihop network with tremendous research value. The routing strategy decides the performance of network and the quality of transmission. A good routing algorithm will use the whole bandwidth of network and assure the quality of service of traffic. Since the routing metric ETX (expected transmission count) does not assure good quality of wireless links, to improve the routing performance, an opportunistic routing mechanism combined with long-term and short-term metrics for WMN based on OLSR (optimized link state routing) and ETX is proposed in this paper. This mechanism always chooses the highest throughput links to improve the performance of routing over WMN and then reduces the energy consumption of mesh routers. The simulations and analyses show that the opportunistic routing mechanism is better than the mechanism with the metric of ETX.

Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition

Directory of Open Access Journals (Sweden)

YuKang Jia

2017-01-01

Full Text Available Long Short-Term Memory (LSTM is a kind of Recurrent Neural Networks (RNN relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers. As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is 99.8%. As for DLSTM, the recognition rate can reach 100% because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

Science.gov (United States)

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

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

Energy Technology Data Exchange (ETDEWEB)

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

1990-01-01

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

North American Tropospheric Ozone Profiles from IONS (INTEX Ozonesonde Network Study, 2004, 2006): Ozone Budgets, Polution Statistics, Satellite Retrievals

Science.gov (United States)

Dougherty, M.; Thompson, A. M.; Witte, J. C.; Miller, S. K.; Oltmans, S. J.; Cooper, O. R.; Tarasick, D. W.; Chatfield, R. B.; Taubman, B. F.; Joseph, E.; Baumgardner, D.; Merrill, J. T.; Morris, G. A.; Rappenglueck, B.; Lefer, B.; Forbes, G.; Newchurch, M. J.; Schmidlin, F. J.; Pierce, R. B.; Leblanc, T.; Dubey, M.; Minschwaner, K.

2007-12-01

During INTEX-B (both Milagro and IMPEX phases in Spring 2006) and during the summer TEXAQS- 2006/GOMACCS period, the INTEX Ozonesonde Network Study (IONS-06) coordinated ozonesonde launches over North America for Aura overpasses. IONS-06 supported aircraft operations and provided profiles for ozone budgets and pollution transport, satellite validation and evaluation of models. In contrast to IONS-04, IONS-06 had a greater range (all but one 2004 IONS site plus a dozen in California, New Mexico, Mexico City, Barbados and southwestern Canada), yielding more than 700 profiles. Tropospheric pollution statistics to guide Aura satellite retrievals and contrasts in UT-LS (upper tropospheric-lower stratospheric) ozone between 2004 and 2006 are presented. With IONS-04 dominated by low-pressure conditions over northeastern North America, UT ozone originated 25% from the stratosphere [Thompson et al., 2007a,b] with significant amounts from aged or relatively fresh pollution and lightning [Cooper et al., 2006; Morris et al., 2006]. Both IONS-04 and IONS-06 summer periods displayed a persistent UT ozone maximum [Cooper et al., 2007] over the south-central US. March 2006 IONS sondes over Mexico manifested persistent UT/LS gravity wave influence and more sporadic pollution. Regional and seasonal contrasts in IONS-06 ozone distributions are described. intexb/ions06.html

Short-Term Memory for Space and Time Flexibly Recruit Complementary Sensory-Biased Frontal Lobe Attention Networks.

Science.gov (United States)

Michalka, Samantha W; Kong, Lingqiang; Rosen, Maya L; Shinn-Cunningham, Barbara G; Somers, David C

2015-08-19

The frontal lobes control wide-ranging cognitive functions; however, functional subdivisions of human frontal cortex are only coarsely mapped. Here, functional magnetic resonance imaging reveals two distinct visual-biased attention regions in lateral frontal cortex, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), anatomically interdigitated with two auditory-biased attention regions, transverse gyrus intersecting precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS). Intrinsic functional connectivity analysis demonstrates that sPCS and iPCS fall within a broad visual-attention network, while tgPCS and cIFS fall within a broad auditory-attention network. Interestingly, we observe that spatial and temporal short-term memory (STM), respectively, recruit visual and auditory attention networks in the frontal lobe, independent of sensory modality. These findings not only demonstrate that both sensory modality and information domain influence frontal lobe functional organization, they also demonstrate that spatial processing co-localizes with visual processing and that temporal processing co-localizes with auditory processing in lateral frontal cortex. Copyright © 2015 Elsevier Inc. All rights reserved.

Short-term memory for space and time flexibly recruit complementary sensory-biased frontal lobe attention networks

Science.gov (United States)

Michalka, Samantha W.; Kong, Lingqiang; Rosen, Maya L.; Shinn-Cunningham, Barbara G.; Somers, David C.

2015-01-01

Summary The frontal lobes control wide-ranging cognitive functions; however, functional subdivisions of human frontal cortex are only coarsely mapped. Here, functional magnetic resonance imaging reveals two distinct visual-biased attention regions in lateral frontal cortex, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), anatomically interdigitated with two auditory-biased attention regions, transverse gyrus intersecting precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS). Intrinsic functional connectivity analysis demonstrates that sPCS and iPCS fall within a broad visual-attention network, while tgPCS and cIFS fall within a broad auditory-attention network. Interestingly, we observe that spatial and temporal short-term memory (STM), respectively, recruit visual and auditory attention networks in the frontal lobe, independent of sensory modality. These findings not only demonstrate that both sensory modality and information domain influence frontal lobe functional organization, they also demonstrate that spatial processing co-localizes with visual processing and that temporal processing co-localizes with auditory processing in lateral frontal cortex. PMID:26291168

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

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

International Nuclear Information System (INIS)

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

2014-01-01

Memristor is considered to be a natural electrical synapse because of its distinct memory property and nanoscale. In recent years, more and more similar behaviors are observed between memristors and biological synapse, e.g., short-term memory (STM) and long-term memory (LTM). The traditional mathematical models are unable to capture the new emerging behaviors. In this article, an updated phenomenological model based on the model of the Hewlett–Packard (HP) Labs has been proposed to capture such new behaviors. The new dynamical memristor model with an improved ion diffusion term can emulate the synapse behavior with forgetting effect, and exhibit the transformation between the STM and the LTM. Further, this model can be used in building new type of neural networks with forgetting ability like biological systems, and it is verified by our experiment with Hopfield neural network. - Highlights: • We take the Fick diffusion and the Soret diffusion into account in the ion drift theory. • We develop a new model based on the old HP model. • The new model can describe the forgetting effect and the spike-rate-dependent property of memristor. • The new model can solve the boundary effect of all window functions discussed in [13]. • A new Hopfield neural network with the forgetting ability is built by the new memristor model

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

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

DEFF Research Database (Denmark)

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

2009-01-01

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

An overview af SAGE I and II ozone measurements

Science.gov (United States)

Mccormick, M. P.; Zawodny, J. M.; Veiga, R. E.; Larsen, J. C.; Wang, P. H.

1989-01-01

The stratospheric Aerosol and Gas Experiments (SAGE) I and II measure Mie, Rayleigh, and gaseous extinction profiles using the solar occultation technique. These global measurements yield ozone profiles with a vertical resolution of 1 km which have been routinely obtained for the periods from February 1979 to November 1981 (SAGE I) and October 1984 to the present (SAGE II). The long-term periodic behavior of the measured ozone is presented as well as case studies of the observed short-term spatial and temporal variability. A linear regression shows annual, semiannual, and quasi-biennial oscillation features at various altitudes and latitudes which, in general, agree with past work. Also, ozone, aerosol, and water vapor data are described for the Antarctic springtime, showing large variation relative to the vortex. Cross-sections in latitude and altitude and polar plots at various altitudes clearly delineate the ozone hole vertically and areally.

Short-term versus long-term market opportunities and financial constraints

International Nuclear Information System (INIS)

Ferrari, Angelo

1999-01-01

This presentation discusses gas developments in Europe, the European Gas Directive, short term vs. long term, and Snam's new challenges. The European gas market is characterized by (1) The role of gas in meeting the demand for energy, which varies greatly from one country to another, (2) A growing market, (3) Decreasing role of domestic production, and (4) Increasing imports. Within the European Union, the Gas Directive aims to transform single national markets into one integrated European market by introducing third party access to the network for eligible clients as a means of increasing the competition between operators. The Gas Directive would appear to modify the form of the market rather than its size, and in particular the sharing of responsibility and risk among operators. The market in the future will offer operators the possibility to exploit opportunities deriving mainly from demands for increased flexibility. Opportunities linked to entrepreneurial initiatives require long-term investments characteristic of the gas business. Risks and opportunities must be balanced evenly between different operators. If everyone takes on their own risks and responsibilities, this means a wider distribution of the risks of long-term vs. short-term, currently borne by the gas companies that are integrated, into a market that tends to favour the short-term. A gradual liberalization process should allow incumbent operators to gradually diversify their activities in new gas market areas or enter new business activities. They could move beyond their local and European boundaries in pursuit of an international dimension. The market will have to make the transition from the national to the European dimension: as an example, Snam covers 90% of the Italian market, but its share of an integrated European market will be about 15%

The functional neuroanatomy of multitasking: combining dual tasking with a short term memory task.

Science.gov (United States)

Deprez, Sabine; Vandenbulcke, Mathieu; Peeters, Ron; Emsell, Louise; Amant, Frederic; Sunaert, Stefan

2013-09-01

Insight into the neural architecture of multitasking is crucial when investigating the pathophysiology of multitasking deficits in clinical populations. Presently, little is known about how the brain combines dual-tasking with a concurrent short-term memory task, despite the relevance of this mental operation in daily life and the frequency of complaints related to this process, in disease. In this study we aimed to examine how the brain responds when a memory task is added to dual-tasking. Thirty-three right-handed healthy volunteers (20 females, mean age 39.9 ± 5.8) were examined with functional brain imaging (fMRI). The paradigm consisted of two cross-modal single tasks (a visual and auditory temporal same-different task with short delay), a dual-task combining both single tasks simultaneously and a multi-task condition, combining the dual-task with an additional short-term memory task (temporal same-different visual task with long delay). Dual-tasking compared to both individual visual and auditory single tasks activated a predominantly right-sided fronto-parietal network and the cerebellum. When adding the additional short-term memory task, a larger and more bilateral frontoparietal network was recruited. We found enhanced activity during multitasking in components of the network that were already involved in dual-tasking, suggesting increased working memory demands, as well as recruitment of multitask-specific components including areas that are likely to be involved in online holding of visual stimuli in short-term memory such as occipito-temporal cortex. These results confirm concurrent neural processing of a visual short-term memory task during dual-tasking and provide evidence for an effective fMRI multitasking paradigm. © 2013 Elsevier Ltd. All rights reserved.

Global long-term ozone trends derived from different observed and modelled data sets

Science.gov (United States)

Coldewey-Egbers, M.; Loyola, D.; Zimmer, W.; van Roozendael, M.; Lerot, C.; Dameris, M.; Garny, H.; Braesicke, P.; Koukouli, M.; Balis, D.

2012-04-01

The long-term behaviour of stratospheric ozone amounts during the past three decades is investigated on a global scale using different observed and modelled data sets. Three European satellite sensors GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/METOP are combined and a merged global monthly mean total ozone product has been prepared using an inter-satellite calibration approach. The data set covers the 16-years period from June 1995 to June 2011 and it exhibits an excellent long-term stability, which is required for such trend studies. A multiple linear least-squares regression algorithm using different explanatory variables is applied to the time series and statistically significant positive trends are detected in the northern mid latitudes and subtropics. Global trends are also estimated using a second satellite-based Merged Ozone Data set (MOD) provided by NASA. For few selected geographical regions ozone trends are additionally calculated using well-maintained measurements of individual Dobson/Brewer ground-based instruments. A reasonable agreement in the spatial patterns of the trends is found amongst the European satellite, the NASA satellite, and the ground-based observations. Furthermore, two long-term simulations obtained with the Chemistry-Climate Models E39C-A provided by German Aerospace Center and UMUKCA-UCAM provided by University of Cambridge are analysed.

Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

Directory of Open Access Journals (Sweden)

Yun-Long Kong

2018-03-01

Full Text Available A satellite image time series (SITS contains a significant amount of temporal information. By analysing this type of data, the pattern of the changes in the object of concern can be explored. The natural change in the Earth’s surface is relatively slow and exhibits a pronounced pattern. Some natural events (for example, fires, floods, plant diseases, and insect pests and human activities (for example, deforestation and urbanisation will disturb this pattern and cause a relatively profound change on the Earth’s surface. These events are usually referred to as disturbances. However, disturbances in ecosystems are not easy to detect from SITS data, because SITS contain combined information on disturbances, phenological variations and noise in remote sensing data. In this paper, a novel framework is proposed for online disturbance detection from SITS. The framework is based on long short-term memory (LSTM networks. First, LSTM networks are trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Last, the predicted data are compared with real data, and the noticeable deviations reveal disturbances. Experimental results using 16-day compositions of the moderate resolution imaging spectroradiometer (MOD13Q1 illustrate the effectiveness and stability of the proposed approach for online disturbance detection.

Validation of Copernicus Height-resolved Ozone data Products from Sentinel-5P TROPOMI using global sonde and lidar networks (CHEOPS-5P)

Science.gov (United States)

Keppens, Arno; Lambert, Jean-Christopher; Hubert, Daan; Verhoelst, Tijl; Granville, José; Ancellet, Gérard; Balis, Dimitris; Delcloo, Andy; Duflot, Valentin; Godin-Beekmann, Sophie; Koukouli, Marilisa; Leblanc, Thierry; Stavrakou, Trissevgeni; Steinbrecht, Wolfgang; Stübi, Réné; Thompson, Anne

2017-04-01

Monitoring of and research on air quality, stratospheric ozone and climate change require global and long-term observation of the vertical distribution of atmospheric ozone, at ever-improving resolution and accuracy. Global tropospheric and stratospheric ozone profile measurement capabilities from space have therefore improved substantially over the last decades. Being a part of the space segment of the Copernicus Atmosphere and Climate Services that is currently under implementation, the upcoming Sentinel-5 Precursor (S5P) mission with its imaging spectrometer TROPOMI (Tropospheric Monitoring Instrument) is dedicated to the measurement of nadir atmospheric radiance and solar irradiance in the UV-VIS-NIR-SWIR spectral range. Ozone profile and tropospheric ozone column data will be retrieved from these measurements by use of several complementary retrieval methods. The geophysical validation of the enhanced height-resolved ozone data products, as well as support to the continuous evolution of the associated retrieval algorithms, is a key objective of the CHEOPS-5P project, a contributor to the ESA-led S5P Validation Team (S5PVT). This work describes the principles and implementation of the CHEOPS-5P quality assessment (QA) and validation system. The QA/validation methodology relies on the analysis of S5P retrieval diagnostics and on comparisons of S5P data with reference ozone profile measurements. The latter are collected from ozonesonde, stratospheric lidar and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch, including the NDACC global and SHADOZ tropical networks. After adaptation of the Multi-TASTE versatile satellite validation environment currently operational in the context of ESA's CCI, EUMETSAT O3M-SAF, and CEOS and SPARC initiatives, a list of S5P data Quality Indicators (QI) will be derived from complementary investigations: (1) data content and information content studies of the S5P data retrievals

New methodology for Ozone Depletion Potentials of short-lived compounds: n-Propyl bromide as an example

Science.gov (United States)

Wuebbles, Donald J.; Patten, Kenneth O.; Johnson, Matthew T.; Kotamarthi, Rao

2001-07-01

A number of the compounds proposed as replacements for substances controlled under the Montreal Protocol have extremely short atmospheric lifetimes, on the order of days to a few months. An important example is n-propyl bromide (also referred to as 1-bromopropane, CH2BrCH2CH3 or simplified as 1-C3H7Br or nPB). This compound, useful as a solvent, has an atmospheric lifetime of less than 20 days due to its reaction with hydroxyl. Because nPB contains bromine, any amount reaching the stratosphere has the potential to affect concentrations of stratospheric ozone. The definition of Ozone Depletion Potentials (ODP) needs to be modified for such short-lived compounds to account for the location and timing of emissions. It is not adequate to treat these chemicals as if they were uniformly emitted at all latitudes and longitudes as normally done for longer-lived gases. Thus, for short-lived compounds, policymakers will need a table of ODP values instead of the single value generally provided in past studies. This study uses the MOZART2 three-dimensional chemical-transport model in combination with studies with our less computationally expensive two-dimensional model to examine potential effects of nPB on stratospheric ozone. Multiple facets of this study examine key questions regarding the amount of bromine reaching the stratosphere following emission of nPB. Our most significant findings from this study for the purposes of short-lived replacement compound ozone effects are summarized as follows. The degradation of nPB produces a significant quantity of bromoacetone which increases the amount of bromine transported to the stratosphere due to nPB. However, much of that effect is not due to bromoacetone itself, but instead to inorganic bromine which is produced from tropospheric oxidation of nPB, bromoacetone, and other degradation products and is transported above the dry and wet deposition processes of the model. The MOZART2 nPB results indicate a minimal correction of the

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

Directory of Open Access Journals (Sweden)

Ping-Huan Kuo

2018-01-01

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

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.

Production of ozone using nanosecond short pulsed power

OpenAIRE

Shimomura, N.; Wakimoto, M.; Togo, H.; Namihira, Takao; Akiyama, Hidenori; ナミヒラ, タカオ; アキヤマ, ヒデノリ; 浪平, 隆男; 秋山, 秀典

2003-01-01

Production of ozone is one of the most typical industrial and commercial applications of electrical discharge. The demand of ozone will be increasing for wholesome and environment-friendly sterilizations. The production of ozone using the pulsed power discharge will apply electron accelerations around the head of streamer discharge. The breakdowns in reactor, however, often limit the efficient production. The pulse shape should be controlled for dimension of the reactor. On the other hand, th...

Simulation of Halocarbon Production and Emissions and Effects on Ozone Depletion

Science.gov (United States)

Holmes; Ellis

1997-09-01

/ This paper describes an integrated model that simulates future halocarbon production/emissions and potential ozone depletion. Applications and historical production levels for various halocarbons are discussed first. A framework is then presented for modeling future halocarbon impacts incorporating differences in underlying demands, applications, regulatory mandates, and environmental characteristics. The model is used to simulate the potential impacts of several prominent issues relating to halocarbon production, regulation, and environmental interactions, notably: changes in agricultural methyl bromide use, increases in effectiveness of bromine for ozone depletion, modifications to the elimination schedule for HCFCs, short-term expansion of CFC demand in low use compliance countries, and delays in Russian Federation compliance. Individually, each issue does not unequivocally represent a significant likely increase in long-term atmospheric halogen loading and stratospheric ozone depletion. In combination, however, these impacts could increase peak halogen concentrations and long-term integral halogen loading, resulting in higher levels of stratospheric ozone depletion and longer exposure to increased levels of UV radiation.KEY WORDS: Halocarbons; Ozone depletion; Montreal Protocol; Integrated assessment

Modifiers of short-term effects of ozone on mortality in eastern Massachusetts - A case-crossover analysis at individual level

Directory of Open Access Journals (Sweden)

Schwartz Joel

2010-01-01

Full Text Available Abstract Background Substantial epidemiological studies demonstrate associations between exposure to ambient ozone and mortality. A few studies simply examine the modification of this ozone effect by individual characteristics and socioeconomic status, but socioeconomic status was usually coded at the city level. Methods This study used a case-crossover design to examine whether impacts of ozone on mortality were modified by socioeconomic status coded at the tract or characteristics at an individual level in eastern Massachusetts, US for a period May-September, 1995-2002, with a total of 157,197 non-accident deaths aging 35 years or older. We used moving averages of maximal 8-hour concentrations of ozone monitored at 8 stationary stations as personal exposure. Results A 10 ppb increase in the four-day moving average of maximal 8-hour ozone was associated with 1.68% (95% confidence interval (CI: 0.51%, 2.87%, 1.96% (95% CI: -1.83%, 5.90%, 8.28% (95% CI: 0.66%, 16.48%, 0.44% (95% CI: -1.45%, 2.37%, -0.83% (95% CI: -2.94%, 1.32%, -1.09% (95% CI: -4.27%, 2.19% and 6.5% (95% CI: 1.74%, 11.49% changes in all natural deaths, respiratory disorders, diabetes, cardiovascular diseases, heart diseases, acute myocardial infarction and stroke, respectively. We did not find any evidence that the associations were significantly modified by socioeconomic status or individual characteristics although small differences of estimates across subpopulations were demonstrated. Conclusions Exposure to ozone was associated with specific cause mortality in Eastern Massachusetts during May-September, 1995-2002. There was no evidence that effects of ozone on mortality were significantly modified by socioeconomic status and individual characteristics.

Tropospheric Ozone as a Short-lived Chemical Climate Forcer

Science.gov (United States)

Pickering, Kenneth E.

2012-01-01

Tropospheric ozone is the third most important greenhouse gas according to the most recent IPCC assessment. However, tropospheric ozone is highly variable in both space and time. Ozone that is located in the vicinity of the tropopause has the greatest effect on climate forcing. Nitrogen oxides (NOx) are the most important precursors for ozone In most of the troposphere. Therefore, pollution that is lofted upward in thunderstorm updrafts or NOx produced by lightning leads to efficient ozone production in the upper troposphere, where ozone is most important climatically. Global and regional model estimates of the impact of North American pollution and lightning on ozone radiative forcing will be presented. It will be shown that in the Northern Hemisphere summer, the lightning effect on ozone radiative forcing can dominate over that of pollution, and that the radiative forcing signal from North America extends well into Europe and North Africa. An algorithm for predicting lightning flash rates and estimating lightning NOx emissions is being incorporated into the NASA GEOS-5 Chemistry and Climate Model. Changes in flash rates and emissions over an ENSO cycle and in future climates will be assessed, along with the resulting changes in upper tropospheric ozone. Other research on the production of NOx per lightning flash and its distribution in the vertical based on cloud-resolving modeling and satellite observations will be presented. Distributions of NO2 and O3 over the Middle East from the OMI instrument on NASA's Aura satellite will also be shown.

Quantifying TOLNet Ozone Lidar Accuracy During the 2014 DISCOVER-AQ and FRAPPE Campaigns

Science.gov (United States)

Wang, Lihua; Newchurch, Michael J.; Alvarez, Raul J., II; Berkoff, Timothy A.; Brown, Steven S.; Carrion, William; De Young, Russell J.; Johnson, Bryan J.; Ganoe, Rene; Gronoff, Guillaume;

Short term respiratory health effects of ambient air pollution: results of the APHEA project in Paris.

OpenAIRE

Dab, W; Medina, S; Quénel, P; Le Moullec, Y; Le Tertre, A; Thelot, B; Monteil, C; Lameloise, P; Pirard, P; Momas, I; Ferry, R; Festy, B

1996-01-01

STUDY OBJECTIVE: To quantify the short term respiratory health effects of ambient air pollution in the Paris area. DESIGN: Time series analysis of daily pollution levels using Poisson regression. SETTING: Paris, 1987-92. MEASUREMENTS AND MAIN RESULTS: Air pollution was monitored by measurement of black smoke (BS) (15 monitoring stations), sulphur dioxide (SO2), nitrogen dioxide (NO2), particulate matter less than 13 microns in diameter (PM13), and ozone (O3) (4 stations). Daily mortality and ...

Unsupervised learning in neural networks with short range synapses

Science.gov (United States)

Brunnet, L. G.; Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.

2013-01-01

Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

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.

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

Morphological study of the effects of ozone on rat lung. I. Short-term exposure

International Nuclear Information System (INIS)

Hiroshima, K.; Kohno, T.; Owada, H.; Hayashi, Y.

1987-01-01

In order to determine the effects of ozone on lungs and the course of cell renewal after damage, young male rats were exposed to 3 ppm of ozone for 4 hr. They were killed at 1, 6, 12, and 18 hr and 1, 2, 3, 4, 7, and 14 days after exposure. One hour before the killing, dividing cells were labeled with tritiated thymidine. Type 1 cells of centriacinar location and bronchiolar cells were severely damaged after exposure. Labeling indices of type 2 cells and bronchiolar nonciliated cells increased 1 day after exposure. Hyperplasia of type 2 cells and bronchiolar nonciliated cells was observed 2 and 3 days after exposure. Ciligenesis of bronchiolar ciliated cells occurred 4 days after exposure. Our study shows that injured type 1 cells are repaired by proliferation of type 2 cells and that injured bronchiolar ciliated and Clara cells are repaired by proliferation of bronchiolar nonciliated cells. These undifferentiated cells are probably progenitors of ciliated cells and Clara cells, and some nonciliated cells are in a transitional form between nonciliated and type 2 cells

Proposed standardized definitions for vertical resolution and uncertainty in the NDACC lidar ozone and temperature algorithms - Part 2: Ozone DIAL uncertainty budget

Science.gov (United States)

Leblanc, Thierry; Sica, Robert J.; van Gijsel, Joanna A. E.; Godin-Beekmann, Sophie; Haefele, Alexander; Trickl, Thomas; Payen, Guillaume; Liberti, Gianluigi

2016-08-01

A standardized approach for the definition, propagation, and reporting of uncertainty in the ozone differential absorption lidar data products contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. One essential aspect of the proposed approach is the propagation in parallel of all independent uncertainty components through the data processing chain before they are combined together to form the ozone combined standard uncertainty. The independent uncertainty components contributing to the overall budget include random noise associated with signal detection, uncertainty due to saturation correction, background noise extraction, the absorption cross sections of O3, NO2, SO2, and O2, the molecular extinction cross sections, and the number densities of the air, NO2, and SO2. The expression of the individual uncertainty components and their step-by-step propagation through the ozone differential absorption lidar (DIAL) processing chain are thoroughly estimated. All sources of uncertainty except detection noise imply correlated terms in the vertical dimension, which requires knowledge of the covariance matrix when the lidar signal is vertically filtered. In addition, the covariance terms must be taken into account if the same detection hardware is shared by the lidar receiver channels at the absorbed and non-absorbed wavelengths. The ozone uncertainty budget is presented as much as possible in a generic form (i.e., as a function of instrument performance and wavelength) so that all NDACC ozone DIAL investigators across the network can estimate, for their own instrument and in a straightforward manner, the expected impact of each reviewed uncertainty component. In addition, two actual examples of full uncertainty budget are provided, using nighttime measurements from the tropospheric ozone DIAL located at the Jet Propulsion Laboratory (JPL) Table Mountain Facility, California, and nighttime measurements from the JPL

Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks

International Nuclear Information System (INIS)

Fatemi, M.H.

2006-01-01

Ozone tropospheric degradation of organic compound is very important in environmental chemistry. The lifetime of organic chemicals in the atmosphere can be calculated from the knowledge of the rate constant of their reaction with free radicals such as OH and NO 3 or O 3 . In the present work, the rate constant for the tropospheric degradation of 137 organic compounds by reaction with ozone, the least widely and successfully modeled degradation process, are predicted by quantitative structure activity relationships modeling based on a variety of theoretical descriptors, which screened and selected by genetic algorithm variable subset selection procedure. These descriptors which can be used as inputs for generated artificial neural networks are; HOMO-LUMO gap, number of double bonds, number of single bonds, maximum net charge on C atom, minimum (>0.1) bond order of C atom and Minimum e-e repulsion of H atom. After generation, optimization and training of artificial neural network, network was used for the prediction of log KO 3 for the validation set. The root mean square error for the neural network calculated log KO 3 for training, prediction and validation set are 0.357, 0.460 and 0.481, respectively, which are smaller than those obtained by multiple linear regressions model (1.217, 0.870 and 0.968, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ozone tropospheric degradations rate constant of organic compounds

Quasi-biennial oscillation in atmospheric ozone, and its possible consequences for damaging UV-B radiation and for determination of long-term ozone trends

Energy Technology Data Exchange (ETDEWEB)

Gruzdev, A N [Russian Academy of Sciences, Moscow (Russian Federation). Inst. of Atmospheric Physics

1996-12-31

The quasi-biennial oscillation (QBO) in ozone is supposed to be related to the QBO of zonal wind in the tropical stratosphere, with an approximate period of 29 months. Generally speaking, mechanisms of QBO-related effects in the extratropical atmosphere should depend on season and region, resulting in other periodicities (e.g., a 20-month periodicity) due to nonlinear interaction between the `pure` QBO and an annual cycle. Seasonal and regional dependences of QBO-related effects in ozone not only influence the regime of ozone variability itself, but can have important consequences, for example, for interannual changes in biologically active UV-B radiation and for determination of long-term ozone trends. This work is concerned with these problems

Quasi-biennial oscillation in atmospheric ozone, and its possible consequences for damaging UV-B radiation and for determination of long-term ozone trends

Energy Technology Data Exchange (ETDEWEB)

Gruzdev, A.N. [Russian Academy of Sciences, Moscow (Russian Federation). Inst. of Atmospheric Physics

1995-12-31

The quasi-biennial oscillation (QBO) in ozone is supposed to be related to the QBO of zonal wind in the tropical stratosphere, with an approximate period of 29 months. Generally speaking, mechanisms of QBO-related effects in the extratropical atmosphere should depend on season and region, resulting in other periodicities (e.g., a 20-month periodicity) due to nonlinear interaction between the `pure` QBO and an annual cycle. Seasonal and regional dependences of QBO-related effects in ozone not only influence the regime of ozone variability itself, but can have important consequences, for example, for interannual changes in biologically active UV-B radiation and for determination of long-term ozone trends. This work is concerned with these problems

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.

The long term agroecosystem research network - shared research strategy

Science.gov (United States)

Jean L. Steiner; Timothy Strickland; Peter J.A. Kleinman; Kris Havstad; Thomas B. Moorman; M.Susan Moran; Phil Hellman; Ray B. Bryant; David Huggins; Greg McCarty

2016-01-01

While current weather patterns and rapidly accelerated changes in technology often focus attention on short-term trends in agriculture, the fundamental demands on modern agriculture to meet society food, feed, fuel and fiber production while providing the foundation for a healthy environment requires long-term perspective. The Long- Term Agroecoystem Research Network...

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

KAUST Repository

Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo M.; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

2016-01-01

Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

KAUST Repository

Onesto, Valentina

2016-05-10

Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

Directory of Open Access Journals (Sweden)

Valentina Onesto

2016-01-01

Full Text Available Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

Long-term associative learning predicts verbal short-term memory performance

OpenAIRE

Jones, Gary; Macken, Bill

2017-01-01

Studies using tests such as digit span and nonword repetition have implicated short-term memory across a range of developmental domains. Such tests ostensibly assess specialized processes for the short-term manipulation and maintenance of information that are often argued to enable long-term learning. However, there is considerable evidence for an influence of long-term linguistic learning on performance in short-term memory tasks that brings into question the role of a specialized short-term...

Long-term associative learning predicts verbal short-term memory performance.

Science.gov (United States)

Jones, Gary; Macken, Bill

2018-02-01

Studies using tests such as digit span and nonword repetition have implicated short-term memory across a range of developmental domains. Such tests ostensibly assess specialized processes for the short-term manipulation and maintenance of information that are often argued to enable long-term learning. However, there is considerable evidence for an influence of long-term linguistic learning on performance in short-term memory tasks that brings into question the role of a specialized short-term memory system separate from long-term knowledge. Using natural language corpora, we show experimentally and computationally that performance on three widely used measures of short-term memory (digit span, nonword repetition, and sentence recall) can be predicted from simple associative learning operating on the linguistic environment to which a typical child may have been exposed. The findings support the broad view that short-term verbal memory performance reflects the application of long-term language knowledge to the experimental setting.

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

Energy Technology Data Exchange (ETDEWEB)

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

2017-05-17

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

Significant increase of surface ozone at a rural site, north of eastern China

Directory of Open Access Journals (Sweden)

Z. Ma

2016-03-01

Full Text Available Ozone pollution in eastern China has become one of the top environmental issues. Quantifying the temporal trend of surface ozone helps to assess the impacts of the anthropogenic precursor reductions and the likely effects of emission control strategies implemented. In this paper, ozone data collected at the Shangdianzi (SDZ regional atmospheric background station from 2003 to 2015 are presented and analyzed to obtain the variation in the trend of surface ozone in the most polluted region of China, north of eastern China or the North China Plain. A modified Kolmogorov–Zurbenko (KZ filter method was performed on the maximum daily average 8 h (MDA8 concentrations of ozone to separate the contributions of different factors from the variation of surface ozone and remove the influence of meteorological fluctuations on surface ozone. Results reveal that the short-term, seasonal and long-term components of ozone account for 36.4, 57.6 and 2.2 % of the total variance, respectively. The long-term trend indicates that the MDA8 has undergone a significant increase in the period of 2003–2015, with an average rate of 1.13 ± 0.01 ppb year−1 (R2 = 0.92. It is found that meteorological factors did not significantly influence the long-term variation of ozone and the increase may be completely attributed to changes in emissions. Furthermore, there is no significant correlation between the long-term O3 and NO2 trends. This study suggests that emission changes in VOCs might have played a more important role in the observed increase of surface ozone at SDZ.

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

Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging

Directory of Open Access Journals (Sweden)

Maihemuti Maimaiti

2017-11-01

Full Text Available Uyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS tagging. However, POS tagging performance suffers from error propagation of morphological analyzers. To address this problem, we propose a few models for POS tagging: conditional random fields (CRF, long short-term memory (LSTM, bidirectional LSTM networks (BI-LSTM, LSTM networks with a CRF layer, and BI-LSTM networks with a CRF layer. These models do not depend on stemming and word disambiguation for Uyghur and combine hand-crafted features with neural network models. State-of-the-art performance on Uyghur POS tagging is achieved on test data sets using the proposed approach: 98.41% accuracy on 15 labels and 95.74% accuracy on 64 labels, which are 2.71% and 4% improvements, respectively, over the CRF model results. Using engineered features, our model achieves further improvements of 0.2% (15 labels and 0.48% (64 labels. The results indicate that the proposed method could be an effective approach for POS tagging in other morphologically rich languages.

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…

Short-Term Memory in Orthogonal Neural Networks

Science.gov (United States)

White, Olivia L.; Lee, Daniel D.; Sompolinsky, Haim

2004-04-01

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.

Short-term memory in orthogonal neural networks

International Nuclear Information System (INIS)

White, Olivia L.; Lee, Daniel D.; Sompolinsky, Haim

2004-01-01

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size

Source attribution of tropospheric ozone

Science.gov (United States)

Butler, T. M.

2015-12-01

Tropospheric ozone is a harmful pollutant with adverse effects on human health and ecosystems. As well as these effects, tropospheric ozone is also a powerful greenhouse gas, with an anthropogenic radiative forcing one quarter of that of CO2. Along with methane and atmospheric aerosol, tropospheric ozone belongs to the so-called Short Lived Climate forcing Pollutants, or SLCP. Recent work has shown that efforts to reduce concentrations of SLCP in the atmosphere have the potential to slow the rate of near-term climate change, while simultaneously improving public health and reducing crop losses. Unlike many other SLCP, tropospehric ozone is not directly emitted, but is instead influenced by two distinct sources: transport of air from the ozone-rich stratosphere; and photochemical production in the troposphere from the emitted precursors NOx (oxides of nitrogen), CO (Carbon Monoxide), and VOC (volatile organic compounds, including methane). Better understanding of the relationship between ozone production and the emissions of its precursors is essential for the development of targeted emission reduction strategies. Several modeling methods have been employed to relate the production of tropospheric ozone to emissions of its precursors; emissions perturbation, tagging, and adjoint sensitivity methods all deliver complementary information about modelled ozone production. Most studies using tagging methods have focused on attribution of tropospheric ozone production to emissions of NOx, even though perturbation methods have suggested that tropospheric ozone is also sensitive to VOC, particularly methane. In this study we describe the implementation into a global chemistry-climate model of a scheme for tagging emissions of NOx and VOC with an arbitrary number of labels, which are followed through the chemical reactions of tropospheric ozone production in order to perform attribution of tropospehric ozone to its emitted precursors. Attribution is performed to both

Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

International Nuclear Information System (INIS)

Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.

2004-01-01

This paper proposes a comprehensive model for the adaptive short-term electricity price forecasting using Artificial Neural Networks (ANN) in the restructured power markets. The model consists: price simulation, price forecasting, and performance analysis. The factors impacting the electricity price forecasting, including time factors, load factors, reserve factors, and historical price factor are discussed. We adopted ANN and proposed a new definition for the MAPE using the median to study the relationship between these factors and market price as well as the performance of the electricity price forecasting. The reserve factors are included to enhance the performance of the forecasting process. The proposed model handles the price spikes more efficiently due to considering the median instead of the average. The IEEE 118-bus system and California practical system are used to demonstrate the superiority of the proposed model. (author)

Reconciliation of Halogen-Induced Ozone Loss with the Total-Column Ozone Record

Science.gov (United States)

Shepherd, T. G.; Plummer, D. A.; Scinocca, J. F.; Hegglin, M. I.; Fioletov, V. E.; Reader, M. C.; Remsberg, E.; von Clarmann, T.; Wang, H. J.

2014-01-01

The observed depletion of the ozone layer from the 1980s onwards is attributed to halogen source gases emitted by human activities. However, the precision of this attribution is complicated by year-to-year variations in meteorology, that is, dynamical variability, and by changes in tropospheric ozone concentrations. As such, key aspects of the total-column ozone record, which combines changes in both tropospheric and stratospheric ozone, remain unexplained, such as the apparent absence of a decline in total-column ozone levels before 1980, and of any long-term decline in total-column ozone levels in the tropics. Here we use a chemistry-climate model to estimate changes in halogen-induced ozone loss between 1960 and 2010; the model is constrained by observed meteorology to remove the eects of dynamical variability, and driven by emissions of tropospheric ozone precursors to separate out changes in tropospheric ozone. We show that halogen-induced ozone loss closely followed stratospheric halogen loading over the studied period. Pronounced enhancements in ozone loss were apparent in both hemispheres following the volcanic eruptions of El Chichon and, in particular, Mount Pinatubo, which significantly enhanced stratospheric aerosol loads. We further show that approximately 40% of the long-term non-volcanic ozone loss occurred before 1980, and that long-term ozone loss also occurred in the tropical stratosphere. Finally, we show that halogeninduced ozone loss has declined by over 10% since stratospheric halogen loading peaked in the late 1990s, indicating that the recovery of the ozone layer is well underway.

Short-term electric load forecasting using computational intelligence methods

OpenAIRE

Jurado, Sergio; Peralta, J.; Nebot, Àngela; Mugica, Francisco; Cortez, Paulo

2013-01-01

Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. The performance of the suggested methods is experimentally justified with several experiments carried out...

Need and design of short-term auctions in the EU gas markets

International Nuclear Information System (INIS)

Vazquez, Miguel; Hallack, Michelle

2013-01-01

In the EU, gas markets are based on socializing network flexibility services. However, shippers have different preferences on network flexibility, which are not reflected in current allocation models. We propose the introduction of auction mechanisms to allocate network services in the short run. The auction aims to represent simultaneously the diversity of players′ preferences and the trade-offs implied by network constraints. Two sealed-bid auctions are proposed: (a) an auction based on bids for gas, which allocates network services through the minimization of gas price differences; (b) an auction with explicit bids for line-pack, which allows shippers′ valuation of line-pack storage. - Highlights: • EU gas regulation faces serious difficulties in network resource allocation. • We propose two short-term auctions with implicit network allocation. • The difference between the two is the implicit or explicit allocation of line-pack. • The first auction considers line-pack as an instrument to arbitrage gas prices. • The second auction design considers line-pack as a tool to hedge volatility

Short-term facilitation may stabilize parametric working memory trace

Directory of Open Access Journals (Sweden)

Vladimir eItskov

2011-10-01

Full Text Available Networks with continuous set of attractors are considered to be a paradigmatic model for parametric working memory, but require fine-tuning of connections and are thus structurally unstable. Here we analyzed the network with ring attractor, where connections are not perfectly tuned and the activity state therefore drifts in the absence of the stabilizing stimulus. We derive an analytical expression for the drift dynamics and conclude that the network cannot function as working memory for a period of several seconds, a typical delay time in monkey memory experiments. We propose that short-term synaptic facilitation in recurrent connections significantly improves the robustness of the model by slowing down the drift of activity bump. Extending the calculation of the drift velocity to network with synaptic facilitation, we conclude that facilitation can slow down the drift by a large factor, rendering the network suitable as a model of working memory.

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.

Short-Term Load Forecast in Electric Energy System in Bulgaria

Directory of Open Access Journals (Sweden)

Irina Asenova

2010-01-01

Full Text Available As the accuracy of the electricity load forecast is crucial in providing better cost effective risk management plans, this paper proposes a Short Term Electricity Load Forecast (STLF model with high forecasting accuracy. Two kind of neural networks, Multilayer Perceptron network model and Radial Basis Function network model, are presented and compared using the mean absolute percentage error. The data used in the models are electricity load historical data. Even though the very good performance of the used model for the load data, weather parameters, especially the temperature, take important part for the energy predicting which is taken into account in this paper. A comparative evaluation between a traditional statistical method and artificial neural networks is presented.

Towards a NNORSY Ozone Profile ECV from European Nadir UV/VIS Measurements

Science.gov (United States)

Felder, Martin; Kaifel, Anton; Huckle, Roger

2010-12-01

The Neural Network Ozone Retrieval System (NNORSY) has been adapted and applied to several different satellite instruments, including the backscatter UV/VIS instruments ERS2-GOME, SCIAMACHY and METOP-GOME-2. The retrieved long term ozone field hence spans the years 1995 till now. To provide target data for training the neural networks, the lower parts of the atmosphere are sampled by ozone sondes from the WOUDC and SHADOZ data archives. Higher altitudes are covered by a variety of limb-sounding instruments, including the SAGE and POAM series, HALOE, ACE-FTS and AURA-MLS. In this paper, we show ozone profile time series over the entire time range to demonstrate the "out-of-the-box" consistency and homogeneity of our data across the three different nadir sounders, i.e. without any kind of tuning applied. These features of Essential Climate Variable (ECV) datasets [1] also lie at the heart of the recently announced ESA Climate Change Initiative, to which we hope to contribute in the near future.

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.

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

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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

Algorithms and programs for processing of satellite data on ozone layer and UV radiation levels

International Nuclear Information System (INIS)

Borkovskij, N.B.; Ivanyukovich, V.A.

2012-01-01

Some algorithms and programs for automatic retrieving and processing ozone layer satellite data are discussed. These techniques are used for reliable short-term UV-radiation levels forecasting. (authors)

Short-term exposure to air pollution and digital vascular function.

Science.gov (United States)

Ljungman, Petter L; Wilker, Elissa H; Rice, Mary B; Schwartz, Joel; Gold, Diane R; Koutrakis, Petros; Vita, Joseph A; Mitchell, Gary F; Vasan, Ramachandran S; Benjamin, Emelia J; Mittleman, Murray A; Hamburg, Naomi M

2014-09-01

We investigated associations between ambient air pollution and microvessel function measured by peripheral arterial tonometry between 2003 and 2008 in the Framingham Heart Study Offspring and Third Generation Cohorts. We measured particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), black carbon, sulfates, particle number, nitrogen oxides, and ozone by using fixed monitors, and we determined moving averages for 1-7 days preceding vascular testing. We examined associations between these exposures and hyperemic response to ischemia and baseline pulse amplitude, a measure of arterial tone (n = 2,369). Higher short-term exposure to air pollutants, including PM2.5, black carbon, and particle number was associated with higher baseline pulse amplitude. For example, higher 3-day average PM2.5 exposure was associated with 6.3% higher baseline pulse amplitude (95% confidence interval: 2.0, 10.9). However, there were no consistent associations between the air pollution exposures assessed and hyperemic response. Our findings in a community-based sample exposed to relatively low pollution levels suggest that short-term exposure to ambient particulate pollution is not associated with vasodilator response, but that particulate air pollution is associated with baseline pulse amplitude, suggesting potentially adverse alterations in baseline vascular tone or compliance. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Health Effects of Ozone Pollution

Science.gov (United States)

Inhaling ozone can cause coughing, shortness of breath, worse asthma or bronchitis symptoms, and irritation and damage to airways.You can reduce your exposure to ozone pollution by checking air quality where you live.

A growing threat to the ozone layer from short-lived anthropogenic chlorocarbons

Directory of Open Access Journals (Sweden)

D. E. Oram

2017-10-01

Full Text Available Large and effective reductions in emissions of long-lived ozone-depleting substance (ODS are being achieved through the Montreal Protocol, the effectiveness of which can be seen in the declining atmospheric abundances of many ODSs. An important remaining uncertainty concerns the role of very short-lived substances (VSLSs which, owing to their relatively short atmospheric lifetimes (less than 6 months, are not regulated under the Montreal Protocol. Recent studies have found an unexplained increase in the global tropospheric abundance of one VSLS, dichloromethane (CH2Cl2, which has increased by around 60 % over the past decade. Here we report dramatic enhancements of several chlorine-containing VSLSs (Cl-VSLSs, including CH2Cl2 and CH2ClCH2Cl (1,2-dichloroethane, observed in surface and upper-tropospheric air in East and South East Asia. Surface observations were, on occasion, an order of magnitude higher than previously reported in the marine boundary layer, whilst upper-tropospheric data were up to 3 times higher than expected. In addition, we provide further evidence of an atmospheric transport mechanism whereby substantial amounts of industrial pollution from East Asia, including these chlorinated VSLSs, can rapidly, and regularly, be transported to tropical regions of the western Pacific and subsequently uplifted to the tropical upper troposphere. This latter region is a major provider of air entering the stratosphere, and so this mechanism, in conjunction with increasing emissions of Cl-VSLSs from East Asia, could potentially slow the expected recovery of stratospheric ozone.

Tropospheric Ozone Assessment Report: Present-day ozone distribution and trends relevant to human health

Directory of Open Access Journals (Sweden)

Zoë L. Fleming

2018-02-01

Full Text Available This study quantifies the present-day global and regional distributions (2010–2014 and trends (2000–2014 for five ozone metrics relevant for short-term and long-term human exposure. These metrics, calculated by the Tropospheric Ozone Assessment Report, are: 4th highest daily maximum 8-hour ozone (4MDA8; number of days with MDA8 > 70 ppb (NDGT70, SOMO35 (annual Sum of Ozone Means Over 35 ppb and two seasonally averaged metrics (3MMDA1; AVGMDA8. These metrics were explored at ozone monitoring sites worldwide, which were classified as urban or non-urban based on population and nighttime lights data. Present-day distributions of 4MDA8 and NDGT70, determined predominantly by peak values, are similar with highest levels in western North America, southern Europe and East Asia. For the other three metrics, distributions are similar with North–South gradients more prominent across Europe and Japan. Between 2000 and 2014, significant negative trends in 4MDA8 and NDGT70 occur at most US and some European sites. In contrast, significant positive trends are found at many sites in South Korea and Hong Kong, with mixed trends across Japan. The other three metrics have similar, negative trends for many non-urban North American and some European and Japanese sites, and positive trends across much of East Asia. Globally, metrics at many sites exhibit non-significant trends. At 59% of all sites there is a common direction and significance in the trend across all five metrics, whilst 4MDA8 and NDGT70 have a common trend at ~80% of all sites. Sensitivity analysis shows AVGMDA8 trends differ with averaging period (warm season or annual. Trends are unchanged at many sites when a 1995–2014 period is used; although fewer sites exhibit non-significant trends. Over the longer period 1970–2014, most Japanese sites exhibit positive 4MDA8/SOMO35 trends. Insufficient data exist to characterize ozone trends for the rest of Asia and other world regions.

Ditch network maintenance in peatland forest as a private investment: short- and long-term effects on financial performance at stand level

Directory of Open Access Journals (Sweden)

T. Penttilä

2008-03-01

Full Text Available In Finland, most of the suitable peatland has now been ditched for forestry purposes, and ditch network maintenance (DNM is carried out on 70,000–80,000 hectares of land each year. We examined the financial performance of DNM operations on 44 sample plots representing two medium-quality site types located within two different climatic regions in northern Finland. We applied a simulation approach in which actual measurements of trees growing on sample plots were fed into a stand simulator (MOTTI which predicted stand development with and without DNM. The financial assessments involved calculating short-term and long-term effects of DNM by applying, respectively, ROI (return on investment and NPV (net present value analyses. The results indicated that the financial performance of DNM, particularly in the short term, was highly dependent on the availability of government subsidies. Without the DNM subsidy, the return on investment was between 1.6% and 3.7%; whereas with government subsidy it ranged from 3.8% to 8.4%. In the long run, the net present value was ca. 4–14% higher for stands with DNM than for those without.

What are the differences between long-term, short-term, and working memory?

Science.gov (United States)

Cowan, Nelson

2008-01-01

In the recent literature there has been considerable confusion about the three types of memory: long-term, short-term, and working memory. This chapter strives to reduce that confusion and makes up-to-date assessments of these types of memory. Long- and short-term memory could differ in two fundamental ways, with only short-term memory demonstrating (1) temporal decay and (2) chunk capacity limits. Both properties of short-term memory are still controversial but the current literature is rather encouraging regarding the existence of both decay and capacity limits. Working memory has been conceived and defined in three different, slightly discrepant ways: as short-term memory applied to cognitive tasks, as a multi-component system that holds and manipulates information in short-term memory, and as the use of attention to manage short-term memory. Regardless of the definition, there are some measures of memory in the short term that seem routine and do not correlate well with cognitive aptitudes and other measures (those usually identified with the term "working memory") that seem more attention demanding and do correlate well with these aptitudes. The evidence is evaluated and placed within a theoretical framework depicted in Fig. 1.

Cooling tower water ozonation at Southern University

International Nuclear Information System (INIS)

Chen, C.C.; Knecht, A.T.; Trahan, D.B.; Yaghi, H.M.; Jackson, G.H.; Coppenger, G.D.

1990-01-01

Cooling-tower water is a critical utility for many industries. In the past, inexpensive water coupled with moderate regulation of discharge water led to the neglect of the cooling tower as an energy resource. Now, with the increased cost of chemical treatment and tough EPA rules and regulations, this situation is rapidly changing. The operator of the DOE Y-12 Plant in Oak Ridge as well as many other industries are forced to develop an alternate method of water treatment. The cooling tower is one of the major elements in large energy systems. The savings accrued from a well engineered cooling tower can be a significant part of the overall energy conservation plan. During a short-term ozonation study between 1987-1988, the Y-12 Plant has been successful in eliminating the need for cooling tower treatment chemicals. However, the long-term impact was not available. Since April 1988, the ozone cooling water treatment study at the Y-12 Plant has been moved to the site at Southern University in Baton Rouge, Louisiana. The purpose of this continued study is to determine whether the use of ozonation on cooling towers is practical from an economic, technical and environmental standpoint. This paper discusses system design, operating parameter and performance testing of the ozonation system at Southern University

On the theory of polar ozone holes

International Nuclear Information System (INIS)

Njau, E.C.

1990-12-01

The viable theories already proposed to explain polar ozone holes generally fall into two main categories, namely, chemical theories and dynamical theories. In both of these categories, polar stratospheric clouds (PSCs) are taken as part of the essential basis. Besides, all the dynamical theories are based upon temperature changes. Since formation of the PSCs is highly temperature-dependent, it has been concluded from recent research (e.g. see Kawahira and Hirooka) that temperature changes are a cause, not a result of ozone depletion in polar regions. On this basis, formulations are developed that represent short-term and long-term temperature variations in the polar regions due to natural processes. These variations, which are confined to a limited area around each pole, include specific oscillations with periods ranging from ∼ 2 years up to ∼ 218,597 years. Polar ozone variations are normally expected to be influenced by these temperature oscillations. It is, therefore, apparent that the generally decreasing trend observed in mean October ozone column at Halley Bay (76 deg. S, 27 deg. W) from 1956 up to 1987 is mostly caused by the decreasing phase of a combination of two natural temperature oscillations, one with a period of ∼ 70-80 years and the other with a period of ∼ 160-180 years. Contributions of other natural temperature oscillations are also mentioned and briefly discussed. (author). 35 refs, 4 figs

Tropical Tropospheric Ozone from SHADOZ (Southern Hemisphere ADditional Ozonesondes) Network: A Project for Satellite Research, Process Studies, Education

Science.gov (United States)

Thompson, Anne M.; Witte, Jacquelyn C.; Oltmans, Samuel J.; Schmidlin, Francis J.; Coetzee, G. J. R.; Hoegger, Bruno; Kirchhoff, V. W. J. H.; Ogawa, Toshihiro; Kawakami, Shuji; Posny, Francoise

2002-01-01

The first climatological overview of total, stratospheric and tropospheric ozone in the southern hemisphere tropical and subtropics is based on ozone sounding data from 10 sites comprising the Southern Hemisphere Additional OZonesondes (SHADOZ) network. The period covered is 1998-2000. Observations were made over: Ascension Island; Nairobi, Kenya; Irene, South Africa; Reunion Island; Watukosek, Java; Fiji; Tahiti; American Samoa; San Cristobal, Galapagos; Natal, Brazil. Campaign data were collected on a trans-Atlantic oceanographic cruise and during SAFARI-2000 in Zambia. The ozone data, with simultaneous temperature profiles to approx. 7 hPa and relative humidity to approx. 200 hPa, reside at: . SHADOZ ozone time-series and profiles give a perspective on tropical total, stratospheric and tropospheric ozone. Prominent features are highly variable tropospheric ozone and a zonal wave-one pattern in total (and tropospheric) column ozone. Total, stratospheric and tropospheric column ozone amounts peak between August and November and are lowest between March and May. Tropospheric ozone variability over the Indian and Pacific Ocean displays influences of the Indian Ocean Dipole and convective mixing. Pollution transport from Africa and South America is a seasonal feature. Tropospheric ozone seasonality over the Atlantic Basin shows effects of regional subsidence and recirculation as well as biomass burning. Dynamical and chemical influences appear to be of comparable magnitude though model studies are needed to quantify this.

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

OZONE CONCENTRATION ATTRIBUTABLE PREMATURE DEATH IN POLAND

Directory of Open Access Journals (Sweden)

Krzysztof Skotak

2010-03-01

Full Text Available Ozone in the lower part of the atmosphere (troposphere, strong photochemical oxidant, is not directly emitted to the atmosphere but formed through a series of complex reactions. Ozone concentrations depends on ozone precursors air contamination (mainly nitrogen dioxide and non-methane volatile organic compounds and meteorological conditions (temperature and solar radiation. The main sectors emitted ozone precursors are road transport, power and heat generation plants, household (heating, industry, and petrol storage and distribution. Ozone and some of its precursors are also transported long distances in the atmosphere and are therefore considered a transboundary problem. As a result, the ozone concentrations are often low in busy urban areas and higher in suburban and rural areas. Nowadays, instead of particulate matter, ozone is one of the most widespread global air pollution problems. In and around urban areas, relatively large gradients of ozone can be observed. Because of its high reactivity in elevated concentrations ozone causes serious health problems and damage to ecosystems, agricultural crops and materials. Main ill-health endpoints as a results of ozone concentrations can be characterised as an effect of pulmonary and cardiovascular system, time morbidity and mortality series, development of atherosclerosis and asthma and finally reduction in life expectancy. The associations with increased daily mortality due to ozone concentrations are confirmed by many researches and epidemiological studies. Estimation of the level selected ill-health endpoints (mortality in total and due to cardiovascular and respiratory causes as a result of the short-term ozone exposure in Poland was the main aim of the project. Final results have been done based on estimation method elaborated by WHO, ozone measurements from National Air Quality Monitoring System and statistical information such as mortality rate and populations. All analysis have been done in

Condition of The Stratospheric and Mesospheric Ozone Layer Over Bulgaria for the Period 1996-2012

Science.gov (United States)

Kaleyna, Petya; Mukhtarov, Plamen; Miloshev, Nikolay

2014-05-01

A detailed analysis of the variations of the stratospheric and mesospheric ozone over Bulgaria, in the period 1996-2012, is presented in the article on the basis of ground and satellite measurements of the Total Ozone Content (TOC). The move of the most important components: yearly running mean values, amplitudes and phases of the first four harmonics of the seasonal cycle. Their mean values for the period and the existing long term trends have been found. An evaluation of the general characteristics of the short term variability of the Total Ozone Content (TOC) over Bulgaria also has been made in the article. The impact of the planetary wave activity of the stratosphere on the total ozone has been studied and the climatology of the oscillation amplitudes with periods of 4, 7, 11 and 25 days has been defined.

The behaviour of PM10 and ozone in Malaysia through non-linear dynamical systems

Energy Technology Data Exchange (ETDEWEB)

Sapini, Muhamad Luqman [Pusat Pengajian Matematik, Fakulti Sains Komputer & Matematik Universiti Teknologi MARA Kampus Seremban, 70300 Negeri Sembilan (Malaysia); Rahim, Nurul Zahirah binti Abd [Pengajian Matematik, Fakulti Sains Komputer & Matematik Universiti Teknologi MARA Kampus Jasin, 77000 Melaka (Malaysia); Noorani, Mohd Salmi Md. [Pusat Pengajian Sains Matematik, Fakulti Sains & Teknologi Universiti Kebangsaan Malaysia, 43650 Selangor (Malaysia)

2015-10-22

Prediction of ozone (O3) and PM10 is very important as both these air pollutants affect human health, human activities and more. Short-term forecasting of air quality is needed as preventive measures and effective action can be taken. Therefore, if it is detected that the ozone data is of a chaotic dynamical systems, a model using the nonlinear dynamic from chaos theory data can be made and thus forecasts for the short term would be more accurate. This study uses two methods, namely the 0-1 Test and Lyapunov Exponent. In addition, the effect of noise reduction on the analysis of time series data will be seen by using two smoothing methods: Rectangular methods and Triangle methods. At the end of the study, recommendations were made to get better results in the future.

Short-term respiratory effects of 0. 12 ppm ozone exposure in volunteers with chronic obstructive pulmonary disease

Energy Technology Data Exchange (ETDEWEB)

Linn, W.S.; Fischer, D.A.; Medway, D.A.; Anzar, U.T.; Spier, C.E.; Valencia, L.M.; Venet, T.G.; Hackney, J.D.

1982-06-01

Twenty-five volunteers with chronic obstructive pulmonary disease of mild to moderately severe degree underwent 1-h exposures to 0.12 ppm ozone (O/sub 2/) in purified air with intermittent mild exercise. Their responses were assessed in terms of forced expiratory performance, ear oximetry, and reported symptoms. Control studied consisted of similar exposures to purified air alone. Control studies were separated from O/sub 2/ exposures by 1 month, and the order was randomized. All studies took place in a controlled-environment chamber, and were preceded by approximately 1 h of rest in a purified-air environment. No significant disturbances in forced expiratory performance or symptoms attributable to O/sub 2/ exposure were found. A slight but significant tendency to decreased arterial hemoglobin oxygen saturation (SaO/sub 2/) during exercise in O/sub 2/ was observed. The decrement in SaO/sub 2/ with O/sub 2/ relative to clean air (mean 1.3%) was near the limit of resolution of the ear oximeter test and was detected by signal averaging, thus its physiologic or clinical significance is uncertain.

Extreme events in total ozone over the Northern mid-latitudes: an analysis based on long-term data sets from five European ground-based stations

Energy Technology Data Exchange (ETDEWEB)

Rieder, Harald E. (Inst. for Atmospheric and Climate Science, ETH Zurich, Zurich (Switzerland)), e-mail: hr2302@columbia.edu; Jancso, Leonhardt M. (Inst. for Atmospheric and Climate Science, ETH Zurich, Zurich (Switzerland); Inst. for Meteorology and Geophysics, Univ. of Innsbruck, Innsbruck (Austria)); Di Rocco, Stefania (Inst. for Atmospheric and Climate Science, ETH Zurich, Zurich (Switzerland); Dept. of Geography, Univ. of Zurich, Zurich (Switzerland)) (and others)

2011-11-15

We apply methods from extreme value theory to identify extreme events in high (termed EHOs) and low (termed ELOs) total ozone and to describe the distribution tails (i.e. very high and very low values) of five long-term European ground-based total ozone time series. The influence of these extreme events on observed mean values, long-term trends and changes is analysed. The results show a decrease in EHOs and an increase in ELOs during the last decades, and establish that the observed downward trend in column ozone during the 1970-1990s is strongly dominated by changes in the frequency of extreme events. Furthermore, it is shown that clear 'fingerprints' of atmospheric dynamics (NAO, ENSO) and chemistry [ozone depleting substances (ODSs), polar vortex ozone loss] can be found in the frequency distribution of ozone extremes, even if no attribution is possible from standard metrics (e.g. annual mean values). The analysis complements earlier analysis for the world's longest total ozone record at Arosa, Switzerland, confirming and revealing the strong influence of atmospheric dynamics on observed ozone changes. The results provide clear evidence that in addition to ODS, volcanic eruptions and strong/moderate ENSO and NAO events had significant influence on column ozone in the European sector

Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer

Science.gov (United States)

Monks, P. S.; Archibald, A. T.; Colette, A.; Cooper, O.; Coyle, M.; Derwent, R.; Fowler, D.; Granier, C.; Law, K. S.; Mills, G. E.; Stevenson, D. S.; Tarasova, O.; Thouret, V.; von Schneidemesser, E.; Sommariva, R.; Wild, O.; Williams, M. L.

2015-08-01

Ozone holds a certain fascination in atmospheric science. It is ubiquitous in the atmosphere, central to tropospheric oxidation chemistry, yet harmful to human and ecosystem health as well as being an important greenhouse gas. It is not emitted into the atmosphere but is a byproduct of the very oxidation chemistry it largely initiates. Much effort is focused on the reduction of surface levels of ozone owing to its health and vegetation impacts, but recent efforts to achieve reductions in exposure at a country scale have proved difficult to achieve owing to increases in background ozone at the zonal hemispheric scale. There is also a growing realisation that the role of ozone as a short-lived climate pollutant could be important in integrated air quality climate change mitigation. This review examines current understanding of the processes regulating tropospheric ozone at global to local scales from both measurements and models. It takes the view that knowledge across the scales is important for dealing with air quality and climate change in a synergistic manner. The review shows that there remain a number of clear challenges for ozone such as explaining surface trends, incorporating new chemical understanding, ozone-climate coupling, and a better assessment of impacts. There is a clear and present need to treat ozone across the range of scales, a transboundary issue, but with an emphasis on the hemispheric scales. New observational opportunities are offered both by satellites and small sensors that bridge the scales.

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)

Beginning of the ozone recovery over Europe? − Analysis of the total ozone data from the ground-based observations, 1964−2004

Directory of Open Access Journals (Sweden)

J. W. Krzyścin

2005-07-01

Full Text Available The total ozone variations over Europe (~50° N in the period 1964–2004 are analyzed for detection of signals of ozone recovery. The ozone deviations from the long-term monthly means (1964–1980 for selected European stations, where the ozone observations (by the Dobson spectrophotometers have been carried out continuously for at least 3–4 decades, are averaged and examined by a regression model. A new method is proposed to disclose both the ozone trend variations and date of the trend turnaround. The regression model contains a piecewise linear trend component and the terms describing the ozone response to forcing by "natural" changes in the atmosphere. Standard proxies for the dynamically driven ozone variations are used. The Multivariate Adaptive Regression Splines (MARS methodology and principal component analysis are used to find an optimal set of the explanatory variables and the trend pattern. The turnaround of the ozone trend in 1994 is suggested from the pattern of the piecewise linear trend component. Thus, the changes in the ozone mean level are calculated over the periods 1970–1994 and 1994–2003, for both the original time series and the time series having "natural" variations removed. Statistical significance of the changes are derived by bootstrapping. A first stage of recovery (according to the definition of the International Ozone Commission, i.e. lessening of a negative trend, is found over Europe. It seems possible that the increase in the ozone mean level since 1994 of about 1–2% is due to superposition of the "natural" processes. Comparison of the total ozone ground-based network (the Dobson and Brewer spectrophotometers and the satellite (TOMS, version 8 data over Europe shows the small bias in the mean values for the period 1996–2004, but the differences between the daily ozone values from these instruments are not trendless, and this may hamper an identification of the next stage of the ozone recovery over

Short-term ionic plasticity at GABAergic synapses

Directory of Open Access Journals (Sweden)

Joseph Valentino Raimondo

2012-10-01

Full Text Available Fast synaptic inhibition in the brain is mediated by the pre-synaptic release of the neurotransmitter γ-Aminobutyric acid (GABA and the post-synaptic activation of GABA-sensitive ionotropic receptors. As with excitatory synapses, it is being increasinly appreciated that a variety of plastic processes occur at inhibitory synapses, which operate over a range of timescales. Here we examine a form of activity-dependent plasticity that is somewhat unique to GABAergic transmission. This involves short-lasting changes to the ionic driving force for the postsynaptic receptors, a process referred to as short-term ionic plasticity. These changes are directly related to the history of activity at inhibitory synapses and are influenced by a variety of factors including the location of the synapse and the post-synaptic cell’s ion regulation mechanisms. We explore the processes underlying this form of plasticity, when and where it can occur, and how it is likely to impact network activity.

Order Short-Term Memory Capacity Predicts Nonword Reading and Spelling in First and Second Grade

Science.gov (United States)

Binamé, Florence; Poncelet, Martine

2016-01-01

Recent theories of short-term memory (STM) distinguish between item information, which reflects the temporary activation of long-term representations stored in the language system, and serial-order information, which is encoded in a specific representational system that is independent of the language network. Some studies examining the…

Ozone-augmented percutaneous discectomy: A novel treatment option for refractory discogenic sciatica

International Nuclear Information System (INIS)

Crockett, M.T.; Moynagh, M.; Long, N.; Kilcoyne, A.; Dicker, P.; Synnott, K.; Eustace, S.J.

2014-01-01

Aim: To assess the short and medium-term efficacy and safety of a novel, minimally invasive therapeutic option combining automated percutaneous lumbar discectomy, intradiscal ozone injection, and caudal epidural: ozone-augmented percutaneous discectomy (OPLD). Materials and methods: One hundred and forty-seven patients with a clinical and radiological diagnosis of discogenic sciatica who were refractory to initial therapy were included. Fifty patients underwent OPLD whilst 97 underwent a further caudal epidural. Outcomes were evaluated using McNab's score, improvement in visual analogue score (VAS) pain score, and requirement for further intervention. Follow-up occurred at 1 and 6 months, and comparison was made between groups. Results: OPLD achieved successful outcomes in almost three-quarters of patients in the short and medium term. OPLD achieved superior outcomes at 1 and 6 months compared to caudal epidural. There was a reduced requirement for further intervention in the OPLD group. No significant complications occurred in either group. Discussion: OPLD is a safe and effective treatment for patients with refractory discogenic sciatica in the short and medium term. OPLD has the potential to offer an alternative second-line minimally invasive treatment option that could reduce the requirement for surgery in this patient cohort. - Highlights: • Discogenic sciatica is a common condition which causes significant morbidity. • Ozone augmented percutaneous lumbar discectomy (OPLD) is a novel treatment. • Comparison was made to caudal epidural injection. • All outcomes were superior in the OPLD treatment group. • OPLD appears is an effective treatment for refractory discogenic sciatica

Prediction of Sea Surface Temperature Using Long Short-Term Memory

Science.gov (United States)

Zhang, Qin; Wang, Hui; Dong, Junyu; Zhong, Guoqiang; Sun, Xin

2017-10-01

This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one month daily prediction. We formulate the SST prediction problem as a time series regression problem. LSTM is a special kind of recurrent neural network, which introduces gate mechanism into vanilla RNN to prevent the vanished or exploding gradient problem. It has strong ability to model the temporal relationship of time series data and can handle the long-term dependency problem well. The proposed network architecture is composed of two kinds of layers: LSTM layer and full-connected dense layer. LSTM layer is utilized to model the time series relationship. Full-connected layer is utilized to map the output of LSTM layer to a final prediction. We explore the optimal setting of this architecture by experiments and report the accuracy of coastal seas of China to confirm the effectiveness of the proposed method. In addition, we also show its online updated characteristics.

What are the differences between long-term, short-term, and working memory?

OpenAIRE

Cowan, Nelson

2008-01-01

In the recent literature there has been considerable confusion about the three types of memory: long-term, short-term, and working memory. This chapter strives to reduce that confusion and makes up-to-date assessments of these types of memory. Long- and short-term memory could differ in two fundamental ways, with only short-term memory demonstrating (1) temporal decay and (2) chunk capacity limits. Both properties of short-term memory are still controversial but the current literature is rath...

The Effects of Volcano-Induced Ozone Depletion on Short-lived Climate Forcing in the Arctic

Science.gov (United States)

Ward, P. L.

2012-12-01

Photodissociation of oxygen maintains the stratopause ~50°C warmer than the tropopause. Photodissociation of ozone warms the lower stratosphere, preventing most of this high-energy DNA-damaging solar radiation from reaching the troposphere. Ozone depletion allows more UV energy to reach the lower troposphere causing photodissociation of anthropogenic ozone and nitrogen dioxide. UV energy also penetrates the ocean >10 m where it is absorbed more efficiently than infrared radiation that barely penetrates the surface. Manmade chlorofluorocarbons caused ozone depletion from 1965 to 1994 with slow recovery predicted over the next 50+ years. But the lowest levels of ozone followed the eruptions of Pinatubo (1991 VEI=6), Eyjafjallajökull (2010 VEI=4), and Grímsvötn (2011 VEI=4). Each of the relatively small, basaltic eruptions in Iceland caused more ozone depletion than the long-term effects of chlorofluorocarbons, although total ozone appears to return to pre-eruption levels within a decade. Ozone depletion by 20% increases energy flux thru the lowermost troposphere by 0.7 W m-2 for overhead sun causing temperatures in the lower stratosphere to drop >2°C since 1958 in steps after the 3 largest volcanic eruptions: Agung 1963, El Chichón 1982, and Pinatubo. Temperatures at the surface increased primarily in the regions and at the times of the greatest observed ozone depletion. The greatest warming observed was along the Western Antarctic Peninsula (65.4°S) where minimum temperatures rose 6.7°C from 1951 to 2003 while maximum temperatures remained relatively constant. Minimum total column ozone in September-October was 40-56% lower than in 1972 almost every year since 1987, strongly anti-correlated with observed minimum temperatures. Sea ice decreased 10%, 7 ice shelves separated, 87% of the glaciers retreated and the Antarctic Circumpolar Current warmed. Elsewhere under the ozone hole, warming of continental Antarctica was limited by the high albedo (0.86) of

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Directory of Open Access Journals (Sweden)

Aiqing Kang

2017-01-01

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

Automotive industry program and strategy for control of ozone depleting substances and greenhouse gases

International Nuclear Information System (INIS)

Pound, F.R.; Stirling, P.J.

1990-01-01

This paper outlines the program status and strategy for the short and long term periods for ozone depleting substances and greenhouse gases from both stationary sources in manufacturing plants and mobile sources in motor vehicles. 5 refs

Unequivocal detection of ozone recovery in the Antarctic Ozone Hole through significant increases in atmospheric layers with minimum ozone

Science.gov (United States)

de Laat, Jos; van Weele, Michiel; van der A, Ronald

2015-04-01

significant change in the distribution of ozone. The occurrence of extremely low ozone (near 100% ozone depletion) has been declining significantly in favor of the occurrence of low ozone (80-90% ozone depletion). Finally the potential for continuation of this attribution method in the light of the currently available and future planned satellite remote sensing capacity will be shortly addressed.

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.

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.

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.

The Transition of Atmospheric Infrared Sounder Total Ozone Products to Operations

Science.gov (United States)

Berndt, Emily; Zavodsky, Bradley; Jedlovec, Gary

2014-01-01

The National Aeronautics and Space Administration Short-term Prediction Research and Transition Center (NASA SPoRT) has transitioned a total column ozone product from the Atmospheric Infrared Sounder (AIRS) retrievals to the Weather Prediction Center and Ocean Prediction Center. The total column ozone product is used to diagnose regions of warm, dry, ozone-rich, stratospheric air capable of descending to the surface to create high-impact non-convective winds. Over the past year, forecasters have analyzed the Red, Green, Blue (RGB) Air Mass imagery in conjunction with the AIRS total column ozone to aid high wind forecasts. One of the limitations of the total ozone product is that it is difficult for forecasters to determine whether elevated ozone concentrations are related to stratospheric air or climatologically high values of ozone in certain regions. During the summer of 2013, SPoRT created an AIRS ozone anomaly product which calculates the percent of normal ozone based on a global stratospheric ozone mean climatology. With the knowledge that ozone values 125 percent of normal and greater typically represent stratospheric air; the anomaly product can be used with the total column ozone product to confirm regions of stratospheric air. This paper describes the generation of these products along with forecaster feedback concerning the use of the AIRS ozone products in conjunction with the RGB Air Mass product to access the utility and transition of the products.

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

Science.gov (United States)

Xu, Muyuan; Katori, Yuichi; Aihara, Kazuyuki

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

Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network

International Nuclear Information System (INIS)

Williams, David E; Henshaw, Geoff S; Bart, Mark; Laing, Greer; Wagner, John; Naisbitt, Simon; Salmond, Jennifer A

2013-01-01

This paper presents a novel low-cost instrument that uses a sensor based on conductivity changes of heated tungstic oxide, which is capable of accurately measuring ambient concentrations of ozone. A combination of temperature steps and air flow-rate steps is used to continually reset and re-zero the sensor. A two-stage calibration procedure is presented, in which a nonlinear transformation converts sensor resistance to a signal linear in ozone concentration, then a linear correlation is used to align the calibration with a reference instrument. The required calibration functions specific for the sensor, and control system for air flow rate and sensor temperature, are housed with the sensor in a compact, simple-to-exchange assembly. The instrument can be operated on solar power and uses cell phone technology to enable monitoring in remote locations. Data from field trials are presented here to demonstrate that both the accuracy and the stability of the instrument over periods of months are within a few parts-per-billion by volume. We show that common failure modes can be detected through measurement of signals available from the instrument. The combination of long-term stability, self-diagnosis, and simple, inexpensive repair means that the cost of operation and calibration of the instruments is significantly reduced in comparison with traditional reference instrumentation. These instruments enable the economical construction and operation of ozone monitoring networks of accuracy, time resolution and spatial density sufficient to resolve the local gradients that are characteristic of urban air pollution. (paper)

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)

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.

Science.gov (United States)

Min, Xu; Zeng, Wanwen; Chen, Ning; Chen, Ting; Jiang, Rui

2017-07-15

Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k -mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k -mer co-occurrence information with recent advances in deep learning. We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k -mer embedding. We first split DNA sequences into k -mers and pre-train k -mer embedding vectors based on the co-occurrence matrix of k -mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k -mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility. The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm . tingchen@tsinghua.edu.cn or ruijiang@tsinghua.edu.cn. Supplementary materials are available at

Auditory short-term memory in the primate auditory cortex.

Science.gov (United States)

Scott, Brian H; Mishkin, Mortimer

2016-06-01

Sounds are fleeting, and assembling the sequence of inputs at the ear into a coherent percept requires auditory memory across various time scales. Auditory short-term memory comprises at least two components: an active ׳working memory' bolstered by rehearsal, and a sensory trace that may be passively retained. Working memory relies on representations recalled from long-term memory, and their rehearsal may require phonological mechanisms unique to humans. The sensory component, passive short-term memory (pSTM), is tractable to study in nonhuman primates, whose brain architecture and behavioral repertoire are comparable to our own. This review discusses recent advances in the behavioral and neurophysiological study of auditory memory with a focus on single-unit recordings from macaque monkeys performing delayed-match-to-sample (DMS) tasks. Monkeys appear to employ pSTM to solve these tasks, as evidenced by the impact of interfering stimuli on memory performance. In several regards, pSTM in monkeys resembles pitch memory in humans, and may engage similar neural mechanisms. Neural correlates of DMS performance have been observed throughout the auditory and prefrontal cortex, defining a network of areas supporting auditory STM with parallels to that supporting visual STM. These correlates include persistent neural firing, or a suppression of firing, during the delay period of the memory task, as well as suppression or (less commonly) enhancement of sensory responses when a sound is repeated as a ׳match' stimulus. Auditory STM is supported by a distributed temporo-frontal network in which sensitivity to stimulus history is an intrinsic feature of auditory processing. This article is part of a Special Issue entitled SI: Auditory working memory. Published by Elsevier B.V.

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.

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…

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...

Transport aloft drives peak ozone in the Mojave Desert

Science.gov (United States)

VanCuren, Richard

2015-05-01

Transport of anthropogenic pollution eastward out of the Los Angeles megacity region in California has been periodically observed to reach the Colorado River and the Colorado Plateau region beyond. In the 1980s, anthropogenic halocarbon tracers measured in and near the Las Angeles urban area and at a mountain-top site near the Colorado River, 400 km downwind, were shown to have a correlated seven-day cycle explainable by transport from the urban area with a time lag of 1-2 days. Recent short term springtime intensive studies using aircraft observations and regional modeling of long range transport of ozone from the Southern California megacity region showed frequent and persistent ozone impacts at surface sites across the Colorado Plateau and Southern Rocky Mountain region, at distances up to 1500 km, also with time lags of 1-2 days. However, the timing of ozone peaks at low altitude monitoring sites within the Mojave Desert, at distances from 100 to 400 km from the South Coast and San Joaquin Valley ozone source regions, does not show the expected time-lag behavior seen in the larger transport studies. This discrepancy is explained by recognizing ozone transport across the Mojave Desert to occur in a persistent layer of polluted air in the lower free troposphere with a base level at approximately 1 km MSL. This layer impacts elevated downwind sites directly, but only influences low altitude surface ozone maxima through deep afternoon mixing. Pollutants in this elevated layer derive from California source regions (the Los Angeles megacity region and the intensive agricultural region of the San Joaquin Valley), from long-range transport from Asia, and stratospheric down-mixing. Recognition of the role of afternoon mixing during spring and summer over the Mojave explains and expands the significance of previously published reports of ozone and other pollutants observed in and over the Mojave Desert, and resolves an apparent paradox in the timing of ozone peaks due to

Ozone-augmented percutaneous discectomy: a novel treatment option for refractory discogenic sciatica.

Science.gov (United States)

Crockett, M T; Moynagh, M; Long, N; Kilcoyne, A; Dicker, P; Synnott, K; Eustace, S J

2014-12-01

To assess the short and medium-term efficacy and safety of a novel, minimally invasive therapeutic option combining automated percutaneous lumbar discectomy, intradiscal ozone injection, and caudal epidural: ozone-augmented percutaneous discectomy (OPLD). One hundred and forty-seven patients with a clinical and radiological diagnosis of discogenic sciatica who were refractory to initial therapy were included. Fifty patients underwent OPLD whilst 97 underwent a further caudal epidural. Outcomes were evaluated using McNab's score, improvement in visual analogue score (VAS) pain score, and requirement for further intervention. Follow-up occurred at 1 and 6 months, and comparison was made between groups. OPLD achieved successful outcomes in almost three-quarters of patients in the short and medium term. OPLD achieved superior outcomes at 1 and 6 months compared to caudal epidural. There was a reduced requirement for further intervention in the OPLD group. No significant complications occurred in either group. OPLD is a safe and effective treatment for patients with refractory discogenic sciatica in the short and medium term. OPLD has the potential to offer an alternative second-line minimally invasive treatment option that could reduce the requirement for surgery in this patient cohort. Copyright © 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Assessing the risk of foliar injury from ozone on vegetation in parks in the U.S. National Park Service's Vital Signs Network

International Nuclear Information System (INIS)

Kohut, Robert

2007-01-01

The risk of ozone injury to plants was assessed in support of the National Park Service's Vital Signs Monitoring Network program. The assessment examined bioindicator species, evaluated levels of ozone exposure, and investigated soil moisture conditions during periods of exposure for a 5-year period in each park. The assessment assigned each park a risk rating of high, moderate, or low. For the 244 parks for which assessments were conducted, the risk of foliar injury was high in 65 parks, moderate in 46 parks, and low in 131 parks. Among the well-known parks with a high risk of ozone injury are Gettysburg, Valley Forge, Delaware Water Gap, Cape Cod, Fire Island, Antietam, Harpers Ferry, Manassas, Wolf Trap Farm Park, Mammoth Cave, Shiloh, Sleeping Bear Dunes, Great Smoky Mountains, Joshua Tree, Sequoia and Kings Canyon, and Yosemite. - An assessment of the risk of foliar ozone injury on plants was conducted for 269 parks in support of the U.S. National Park Service's Vital Signs Monitoring Network Program

Ozone induced leaf loss and decreased leaf production of European Holly (Ilex aquifolium L.) over multiple seasons

International Nuclear Information System (INIS)

Ranford, Jonathan; Reiling, Kevin

2007-01-01

European Holly (Ilex aquifolium L.) was used to study the impact of one short (28 day) ozone fumigation episode on leaf production, leaf loss and stomatal conductance (g s ), in order to explore potential longer term effects over 3 growing seasons. Young I. aquifolium plants received an episode of either charcoal-filtered air or charcoal-filtered air with 70 nl l -1 O 3 added for 7 h d -1 over a 28 day period from June 15th 1996, then placed into ambient environment, Stoke-on-Trent, U.K. Data were collected per leaf cohort over the next three growing seasons. Ozone exposure significantly increased leaf loss and stomatal conductance and reduced leaf production over all subsequent seasons. Impact of the initial ozone stress was still detected in leaves that had no direct experimental ozone exposure. This study has shown the potential of ozone to introduce long-term phenological perturbations into ecosystems by influencing productivity over a number of seasons. - Ozone significantly alters Ilex aquifolium leaf production and loss over multiple seasons

Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity.

Science.gov (United States)

Koyluoglu, Onur Ozan; Pertzov, Yoni; Manohar, Sanjay; Husain, Masud; Fiete, Ila R

2017-09-07

It is widely believed that persistent neural activity underlies short-term memory. Yet, as we show, the degradation of information stored directly in such networks behaves differently from human short-term memory performance. We build a more general framework where memory is viewed as a problem of passing information through noisy channels whose degradation characteristics resemble those of persistent activity networks. If the brain first encoded the information appropriately before passing the information into such networks, the information can be stored substantially more faithfully. Within this framework, we derive a fundamental lower-bound on recall precision, which declines with storage duration and number of stored items. We show that human performance, though inconsistent with models involving direct (uncoded) storage in persistent activity networks, can be well-fit by the theoretical bound. This finding is consistent with the view that if the brain stores information in patterns of persistent activity, it might use codes that minimize the effects of noise, motivating the search for such codes in the brain.

Energy management of a university campus utilizing short-term load forecasting with an artificial neural network

Science.gov (United States)

Palchak, David

Electrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.

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

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.

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)

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...

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.

Very-long-term and short-term chromatic adaptation: are their influences cumulative?

Science.gov (United States)

Belmore, Suzanne C; Shevell, Steven K

2011-02-09

Very-long-term (VLT) chromatic adaptation results from exposure to an altered chromatic environment for days or weeks. Color shifts from VLT adaptation are observed hours or days after leaving the altered environment. Short-term chromatic adaptation, on the other hand, results from exposure for a few minutes or less, with color shifts measured within seconds or a few minutes after the adapting light is extinguished; recovery to the pre-adapted state is complete in less than an hour. Here, both types of adaptation were combined. All adaptation was to reddish-appearing long-wavelength light. Shifts in unique yellow were measured following adaptation. Previous studies demonstrate shifts in unique yellow due to VLT chromatic adaptation, but shifts from short-term chromatic adaptation to comparable adapting light can be far greater than from VLT adaptation. The question considered here is whether the color shifts from VLT adaptation are cumulative with large shifts from short-term adaptation or, alternatively, does simultaneous short-term adaptation eliminate color shifts caused by VLT adaptation. The results show the color shifts from VLT and short-term adaptation together are cumulative, which indicates that both short-term and very-long-term chromatic adaptation affect color perception during natural viewing. Copyright © 2010 Elsevier Ltd. All rights reserved.

Stuck in default mode: inefficient cross-frequency synchronization may lead to age-related short-term memory decline.

Science.gov (United States)

Pinal, Diego; Zurrón, Montserrat; Díaz, Fernando; Sauseng, Paul

2015-04-01

Aging-related decline in short-term memory capacity seems to be caused by deficient balancing of task-related and resting state brain networks activity; however, the exact neural mechanism underlying this deficit remains elusive. Here, we studied brain oscillatory activity in healthy young and old adults during visual information maintenance in a delayed match-to-sample task. Particular emphasis was on long range phase:amplitude coupling of frontal alpha (8-12 Hz) and posterior fast oscillatory activity (>30 Hz). It is argued that through posterior fast oscillatory activity nesting into the excitatory or the inhibitory phase of frontal alpha wave, long-range networks can be efficiently coupled or decoupled, respectively. On the basis of this mechanism, we show that healthy, elderly participants exhibit a lack of synchronization in task-relevant networks while maintaining synchronized regions of the resting state network. Lacking disconnection of this resting state network is predictive of aging-related short-term memory decline. These results support the idea of inefficient orchestration of competing brain networks in the aging human brain and identify the neural mechanism responsible for this control breakdown. Copyright © 2015 Elsevier Inc. All rights reserved.

The effect of ozone exposure on the airway response to inhaled allergens; Die Wirkung der Einatmung von Ozon auf die allergische Reaktion des Bronchialsystems

Energy Technology Data Exchange (ETDEWEB)

Joerres, R. [Krankenhaus Grosshansdorf (Germany). Zentrum fuer Pneumologie und Thoraxchirurgie; Nowak, D. [Krankenhaus Grosshansdorf (Germany). Zentrum fuer Pneumologie und Thoraxchirurgie; Magnussen, H. [Krankenhaus Grosshansdorf (Germany). Zentrum fuer Pneumologie und Thoraxchirurgie

1995-06-01

The aim of our study was to determine whether a short-term exposure to ozone enhances the bronchial response to allergens in subjects with allergic asthma, or facilitates a bronchial response in subjects with allergic rhinitis. In the first part of the study we investigated 57 subjects with mild stable asthma, 29 subjects with allergic rhinitis only and 32 healthy subjects. They were exposed to 250 ppb ozone for 3 hrs of intermittent exercise. The effects of ozone on symptoms, lung function parameters and methacholine responsiveness were no markedly different between groups. Twenty-four subjects with asthma and a proven bronchial response to an inhaled allergen, 12 subjects with allergic rhinitis and 10 healthy subjects participated in the second part of the study. In randomized order, subjects breathed 250 ppb ozone or filtered air (FA) for 3 hrs of intermittent exercise. Lung function and airway responsiveness to methacholine were determined before and after exposures, and allergen inhalation challenges were performed 3 hrs after exposures. The 5 subjects with asthma showed increased airway responsiveness to the inhaled allergen after ozone. The subjects with rhinitis showed a slight bronchial response when a high dose of allergen was inhalated after ozone exposure. The changes in lung function, methacholine and allergen responsiveness induced by ozone did not correlate with each other. Our data suggest that a short-term exposure to ozone can increase bronchial allergen responsiveness in subjects with asymptomatic to mild asthma and that this effect is not directly related to other functional changes induced by ozone. (orig./MG) [Deutsch] Unsere Untersuchung widmete sich der Frage, ob die Einatmung von Ozon das Auftreten oder die Auspraegung einer allergischen Reaktion der Atemwege beeinflussen kann. Zunaechst prueften wir 57 Probanden mit allergischem Asthma bronchiale, 29 mit allergischer Rhinitis ohne Asthma und 32 gesunde Kontrollpersonen auf die

The treatment of lumbar disc herniation: a comparison between percutaneous lumbar diskectomy combined with ozone and percutaneous lumbar diskectomy combined with collagenase

International Nuclear Information System (INIS)

Zhong Liming; Wei Xin; Hu Hong; You Jian; Zhao Xiaowei; Hu Kongqiong

2012-01-01

Objective: To evaluate the short-term curative effect and the incidence of postoperative adverse events of percutaneous lumbar diskectomy (PLD) combined with ozone or PLD combined with collagenase in treating lumbar disk herniation. Methods: A total of 223 patients with lumbar disk herniation were enrolled in this study. Patients in the study group (n=108) were treated with PLD combined with ozone, while patients in the control group (n=115) were treated with PLD combined with collagenase. The short-term effectiveness and the incidence of postoperative adverse events were documented. The results were analyzed and compared between the two groups. Results: In the study group, the excellent and good therapeutic results were achieved in 85.18% of the patients (n=92) and the occurrence of adverse events was 5.56%, while in the control group, the excellent and good therapeutic results were achieved in 80.00% of the patients (n=92) and the occurrence of adverse events was 13.04%. No significant difference in the short-term effectiveness existed between the two groups (Pearson Chi-Square =1.038, P=0.308). And the difference in the occurrence of postoperative adverse events was not significant between the two groups (Pearson Chi-Square =3.661, P=0.056). No disc infection occurred in the study group. Conclusion: The short-term curative effect of PLD combined with ozone is not significantly different from that of PLD combined with collagenase. In order to maintain decompression within the disc for a long period and to reduce the incidence of postoperative adverse events PLD combined with ozone ablation is an effective complementary treatment. (authors)

Ozone health effects

International Nuclear Information System (INIS)

Easterly, C.

1994-01-01

Ozone is a principal component of photochemical air pollution endogenous to numerous metropolitan areas. It is primarily formed by the oxidation of NOx in the presence of sunlight and reactive organic compounds. Ozone is a highly active oxidizing agent capable of causing injury to the lung. Lung injury may take the form of irritant effects on the respiratory tract that impair pulmonary function and result in subjective symptoms of respiratory discomfort. These symptoms include, but are not limited to, cough and shortness of breath, and they can limit exercise performance. The effects of ozone observed in humans have been primarily limited to alterations in respiratory function, and a range of respiratory physiological parameters have been measured as a function of ozone exposure in adults and children. These affects have been observed under widely varying (clinical experimental and environmental settings) conditions

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

Science.gov (United States)

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

2012-01-01

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

A Compact Mobile Ozone Lidar for Atmospheric Ozone and Aerosol Profiling

Science.gov (United States)

De Young, Russell; Carrion, William; Pliutau, Denis

2014-01-01

A compact mobile differential absorption lidar (DIAL) system has been developed at NASA Langley Research Center to provide ozone, aerosol and cloud atmospheric measurements in a mobile trailer for ground-based atmospheric ozone air quality campaigns. This lidar is integrated into the Tropospheric Ozone Lidar Network (TOLNet) currently made up of four other ozone lidars across the country. The lidar system consists of a UV and green laser transmitter, a telescope and an optical signal receiver with associated Licel photon counting and analog channels. The laser transmitter consist of a Q-switched Nd:YLF inter-cavity doubled laser pumping a Ce:LiCAF tunable UV laser with all the associated power and lidar control support units on a single system rack. The system has been configured to enable mobile operation from a trailer and was deployed to Denver, CO July 15-August 15, 2014 supporting the DISCOVER-AQ campaign. Ozone curtain plots and the resulting science are presented.

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity

Science.gov (United States)

Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R.; Baldelli, Pietro; Benfenati, Fabio

2013-01-01

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows. PMID:23970852

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity.

Science.gov (United States)

Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R; Baldelli, Pietro; Benfenati, Fabio

2013-01-01

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows.

Ozone kinetics in low-pressure discharges: vibrationally excited ozone and molecule formation on surfaces

Science.gov (United States)

Marinov, Daniil; Guerra, Vasco; Guaitella, Olivier; Booth, Jean-Paul; Rousseau, Antoine

2013-10-01

A combined experimental and modeling investigation of the ozone kinetics in the afterglow of pulsed direct current discharges in oxygen is carried out. The discharge is generated in a cylindrical silica tube of radius 1 cm, with short pulse durations between 0.5 and 2 ms, pressures in the range 1-5 Torr and discharge currents ˜40-120 mA. Time-resolved absolute concentrations of ground-state atoms and ozone molecules were measured simultaneously in situ, by two-photon absorption laser-induced fluorescence and ultraviolet absorption, respectively. The experiments were complemented by a self-consistent model developed to interpret the results and, in particular, to evaluate the roles of vibrationally excited ozone and of ozone formation on surfaces. It is found that vibrationally excited ozone, O_3^{*} , plays an important role in the ozone kinetics, leading to a decrease in the ozone concentration and an increase in its formation time. In turn, the kinetics of O_3^{*} is strongly coupled with those of atomic oxygen and O2(a 1Δg) metastables. Ozone formation at the wall does not contribute significantly to the total ozone production under the present conditions. Upper limits for the effective heterogeneous recombination probability of O atoms into ozone are established.

Ozone kinetics in low-pressure discharges: vibrationally excited ozone and molecule formation on surfaces

International Nuclear Information System (INIS)

Marinov, Daniil; Guaitella, Olivier; Booth, Jean-Paul; Rousseau, Antoine; Guerra, Vasco

2013-01-01

A combined experimental and modeling investigation of the ozone kinetics in the afterglow of pulsed direct current discharges in oxygen is carried out. The discharge is generated in a cylindrical silica tube of radius 1 cm, with short pulse durations between 0.5 and 2 ms, pressures in the range 1–5 Torr and discharge currents ∼40–120 mA. Time-resolved absolute concentrations of ground-state atoms and ozone molecules were measured simultaneously in situ, by two-photon absorption laser-induced fluorescence and ultraviolet absorption, respectively. The experiments were complemented by a self-consistent model developed to interpret the results and, in particular, to evaluate the roles of vibrationally excited ozone and of ozone formation on surfaces. It is found that vibrationally excited ozone, O 3 * , plays an important role in the ozone kinetics, leading to a decrease in the ozone concentration and an increase in its formation time. In turn, the kinetics of O 3 * is strongly coupled with those of atomic oxygen and O 2 (a 1 Δ g ) metastables. Ozone formation at the wall does not contribute significantly to the total ozone production under the present conditions. Upper limits for the effective heterogeneous recombination probability of O atoms into ozone are established. (paper)

Long-term uvb forecasting on the basis of spectral and broad-band measurements

Science.gov (United States)

Bérces, A.; Gáspár, S.; Kovács, G.; Rontó, G.

2003-04-01

The stratospheric ozone concentration has been investigated by several methods, e.g. determinations of the ozone layer using a network of ground based spectrophotometers, of the Dobson and the Brewer types. These data indicate significant decrease of the ozone layer superimposed by much larger seasonal changes at specific geographical locations. The stratospheric ozone plays an important role in the attenuation of the short-wavelength components of the solar spectrum, thus the consequence of the decreased ozone layer is an increased UVB level. Various pyranometers measuring the biological effect of environmental UV radiation have been constructed with spectral sensitivities close to the erythema action spectrum defined by the CIE. Using these erythemally weighted broad-band instruments to detect the tendency of UVB radiation controversial data have been found. To quantify the biological risk due to environmental UV radiation it is reasonable to weight the solar spectrum by the spectral sensitivity of the DNA damage taking into account the high DNA-sensitivity at the short wavelength range of the solar spectrum. Various biological dosimeters have been developed e.g. polycrystalline uracil thin layer. These are usually simple biological systems or components of them. Their UV sensitivity is a consequence of the DNA-damage. Biological dosimeters applied for long-term monitoring are promising tools for the assessment of the biological hazard. Simultaneous application of uracil dosimeters and Robertson-Berger meters can be useful to predict the increasing tendency of the biological UV exposure more precisely. The ratio of the biologically effective dose obtained by the uracil dosimeter (a predominately UVB effect) and by the Robertson-Berger meter (insensitive to changes below 300 nm) is a sensitive method for establishing changes in UVB irradiance due to changes in ozone layer.

Development of a sensitive passive sampler using indigotrisulfonate for the determination of tropospheric ozone.

Science.gov (United States)

Garcia, Gabriel; Allen, Andrew George; Cardoso, Arnaldo Alves

2010-06-01

A new sampling and analytical design for measurement of ambient ozone is presented. The procedure is based on ozone absorption and decoloration (at 600 nm) of indigotrisulfonate dye, where ozone adds itself across the carbon-carbon double bond of the indigo. A mean relative standard deviation of 8.6% was obtained using samplers exposed in triplicate, and a correlation coefficient (r) of 0.957 was achieved in parallel measurements using the samplers and a commercial UV ozone instrument. The devices were evaluated in a measurement campaign, mapping spatial and temporal trends of ozone concentrations in a region of southeast Brazil strongly influenced by seasonal agricultural biomass burning, with associated emissions of ozone precursors. Ozone concentrations were highest in rural areas and lowest at an urban site, due to formation during downwind transport and short-term depletion due to titration with nitric oxide. Ozone concentrations showed strong seasonal trends, due to the influences of precursor emissions, relative humidity and solar radiation intensity. Advantages of the technique include ease and speed of use, the ready availability of components, and excellent sensitivity. Achievable temporal resolution of ozone concentrations is 8 hours at an ambient ozone concentration of 3.8 ppb, or 2 hours at a concentration of 15.2 ppb.

Neural networks for the dimensionality reduction of GOME measurement vector in the estimation of ozone profiles

International Nuclear Information System (INIS)

Del Frate, F.; Iapaolo, M.; Casadio, S.; Godin-Beekmann, S.; Petitdidier, M.

2005-01-01

Dimensionality reduction can be of crucial importance in the application of inversion schemes to atmospheric remote sensing data. In this study the problem of dimensionality reduction in the retrieval of ozone concentration profiles from the radiance measurements provided by the instrument Global Ozone Monitoring Experiment (GOME) on board of ESA satellite ERS-2 is considered. By means of radiative transfer modelling, neural networks and pruning algorithms, a complete procedure has been designed to extract the GOME spectral ranges most crucial for the inversion. The quality of the resulting retrieval algorithm has been evaluated by comparing its performance to that yielded by other schemes and co-located profiles obtained with lidar measurements

Optical remote measurement of ozone in cirrus clouds; Optische Fernmessung von Ozon in Zirruswolken

Energy Technology Data Exchange (ETDEWEB)

Reichardt, J. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Physikalische und Chemische Analytik

1998-12-31

The subject of this thesis is theoretical and experimental investigations into the simultaneous optical remote measurement of atmospheric ozone concentration and particle properties. A lidar system was developed that combines the Raman-lidar and the polarization-lidar with the Raman-DIAL technique. An error analysis is given for ozone measurements in clouds. It turns out that the wavelength dependencies of photon multiple scattering and of the particle extinction coefficient necessitate a correction of the measured ozone concentration. To quantify the cloud influence, model calculations based on particle size distributions of spheres are carried out. The most important experimental result of this thesis is the measured evidence of pronounced minima in the ozone distribution in a humid upper troposphere shortly before and during cirrus observation. Good correlation between ozone-depleted altitude ranges and ice clouds is found. This finding is in contrast to ozone profiles measured in a dry and cloud-free troposphere. (orig.) 151 refs.

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.

A probabilistic assessment of health risks associated with short-term exposure to tropospheric ozone

Energy Technology Data Exchange (ETDEWEB)

Whitfield, R.G; Biller, W.F.; Jusko, M.J.; Keisler, J.M.

1996-06-01

The work described in this report is part of a larger risk assessment sponsored by the U.S. Environmental Protection Agency. Earlier efforts developed exposure-response relationships for acute health effects among populations engaged in heavy exertion. Those efforts also developed a probabilistic national ambient air quality standards exposure model and a general methodology for integrating probabilistic exposure-response relation- ships and exposure estimates to calculate overall risk results. Recently published data make it possible to model additional health endpoints (for exposure at moderate exertion), including hospital admissions. New air quality and exposure estimates for alternative national ambient air quality standards for ozone are combined with exposure-response models to produce the risk results for hospital admissions and acute health effects. Sample results explain the methodology and introduce risk output formats.

CHEM2D-OPP: A new linearized gas-phase ozone photochemistry parameterization for high-altitude NWP and climate models

Directory of Open Access Journals (Sweden)

J. P. McCormack

2006-01-01

Full Text Available The new CHEM2D-Ozone Photochemistry Parameterization (CHEM2D-OPP for high-altitude numerical weather prediction (NWP systems and climate models specifies the net ozone photochemical tendency and its sensitivity to changes in ozone mixing ratio, temperature and overhead ozone column based on calculations from the CHEM2D interactive middle atmospheric photochemical transport model. We evaluate CHEM2D-OPP performance using both short-term (6-day and long-term (1-year stratospheric ozone simulations with the prototype high-altitude NOGAPS-ALPHA forecast model. An inter-comparison of NOGAPS-ALPHA 6-day ozone hindcasts for 7 February 2005 with ozone photochemistry parameterizations currently used in operational NWP systems shows that CHEM2D-OPP yields the best overall agreement with both individual Aura Microwave Limb Sounder ozone profile measurements and independent hemispheric (10°–90° N ozone analysis fields. A 1-year free-running NOGAPS-ALPHA simulation using CHEM2D-OPP produces a realistic seasonal cycle in zonal mean ozone throughout the stratosphere. We find that the combination of a model cold temperature bias at high latitudes in winter and a warm bias in the CHEM2D-OPP temperature climatology can degrade the performance of the linearized ozone photochemistry parameterization over seasonal time scales despite the fact that the parameterized temperature dependence is weak in these regions.

Effects of long-term ambient ozone exposure on biomass and wood traits in poplar treated with ethylenediurea (EDU)

International Nuclear Information System (INIS)

Carriero, G.; Emiliani, G.; Giovannelli, A.; Hoshika, Y.; Manning, W.J.; Traversi, M.L.; Paoletti, E.

2015-01-01

This is the longest continuous experiment where ethylenediurea (EDU) was used to protect plants from ozone (O 3 ). Effects of long-term ambient O 3 exposure (23 ppm h AOT40) on biomass of an O 3 sensitive poplar clone (Oxford) were examined after six years from in-ground planting. Trees were irrigated with either water or 450 ppm EDU. Above (−51%) and below-ground biomass (−47%) was reduced by O 3 although the effect was significant only for stem and coarse roots. Ambient O 3 decreased diameter of the lower stem, and increased moisture content along the stem of not-protected plants (+16%). No other change in the physical wood structure was observed. A comparison with a previous assessment in the same experiment suggested that O 3 effects on biomass partitioning to above-ground organs depend on the tree ontogenetic stage. The root/shoot ratios did not change, suggesting that previous short-term observations of reduced allocation to tree roots may be overestimated. - Highlights: • 6-y ambient O 3 exposure was investigated in a sensitive poplar clone. • EDU irrigation protected poplar against ambient O 3 exposure. • O 3 reduced biomass of roots and stem, but did not change biomass allocation. • O 3 decreased stem diameter only in the lower third of the stem. • O 3 increased moisture content of the wood along the stem. - Ozone exposure reduced lateral branching, leaves and roots in younger trees, and affected stem and roots in older trees, while shoot/root ratios did not change.

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.

The Effects of Antenatal Corticosteroids on Short- and Long-Term Outcomes in Small-for-Gestational-Age Infants

Science.gov (United States)

Ishikawa, Hiroshi; Miyazaki, Ken; Ikeda, Tomoaki; Murabayashi, Nao; Hayashi, Kazutoshi; Kai, Akihiko; Ishikawa, Kaoru; Miyamoto, Yoshihiro; Nishimura, Kunihiro; Kono, Yumi; Kusuda, Satoshi; Fujimura, Masanori

2015-01-01

Aim: To evaluate the effect of antenatal corticosteroids (ANS) on short- and long-term outcomes in small-for-gestational age (SGA) infants. Methods: A retrospective database analysis was performed. A total of 1,931 single infants (birth weight <1,500 g) born at a gestational age between 22 weeks and 33 weeks 6 days who were determined to be SGA registered in the Neonatal Research Network Database in Japan between 2003 and 2007 were evaluated for short-term outcome and long-term outcome. Results: ANS was administered to a total of 719 infants (37%) in the short-term outcome evaluation group and 344 infants (36%) in the long-term outcome evaluation group. There were no significant differences between the ANS group and the no-ANS group for primary short-term outcome (adjusted odds ratio (OR) 0.73; 95% confidence interval (CI) 0.45-1.20; P-value 0.22) or primary long-term outcome (adjusted OR 0.69; 95% CI 0.40-1.17; P-value 0.17). Conclusions: Our results show that ANS does not affect short- or long-term outcome in SGA infants when the birth weight is less than 1500 g. This study strongly suggests that administration of ANS resulted in few benefits for preterm FGR fetuses. PMID:25897289

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

Science.gov (United States)

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

2009-01-01

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

Ozone depletion following future volcanic eruptions

Science.gov (United States)

Eric Klobas, J.; Wilmouth, David M.; Weisenstein, Debra K.; Anderson, James G.; Salawitch, Ross J.

2017-07-01

While explosive volcanic eruptions cause ozone loss in the current atmosphere due to an enhancement in the availability of reactive chlorine following the stratospheric injection of sulfur, future eruptions are expected to increase total column ozone as halogen loading approaches preindustrial levels. The timing of this shift in the impact of major volcanic eruptions on the thickness of the ozone layer is poorly known. Modeling four possible climate futures, we show that scenarios with the smallest increase in greenhouse gas concentrations lead to the greatest risk to ozone from heterogeneous chemical processing following future eruptions. We also show that the presence in the stratosphere of bromine from natural, very short-lived biogenic compounds is critically important for determining whether future eruptions will lead to ozone depletion. If volcanic eruptions inject hydrogen halides into the stratosphere, an effect not considered in current ozone assessments, potentially profound reductions in column ozone would result.

Partially overlapping sensorimotor networks underlie speech praxis and verbal short-term memory: evidence from apraxia of speech following acute stroke.

Science.gov (United States)

Hickok, Gregory; Rogalsky, Corianne; Chen, Rong; Herskovits, Edward H; Townsley, Sarah; Hillis, Argye E

2014-01-01

We tested the hypothesis that motor planning and programming of speech articulation and verbal short-term memory (vSTM) depend on partially overlapping networks of neural regions. We evaluated this proposal by testing 76 individuals with acute ischemic stroke for impairment in motor planning of speech articulation (apraxia of speech, AOS) and vSTM in the first day of stroke, before the opportunity for recovery or reorganization of structure-function relationships. We also evaluated areas of both infarct and low blood flow that might have contributed to AOS or impaired vSTM in each person. We found that AOS was associated with tissue dysfunction in motor-related areas (posterior primary motor cortex, pars opercularis; premotor cortex, insula) and sensory-related areas (primary somatosensory cortex, secondary somatosensory cortex, parietal operculum/auditory cortex); while impaired vSTM was associated with primarily motor-related areas (pars opercularis and pars triangularis, premotor cortex, and primary motor cortex). These results are consistent with the hypothesis, also supported by functional imaging data, that both speech praxis and vSTM rely on partially overlapping networks of brain regions.

Short-term effects of various ozone metrics on cardiopulmonary function in chronic obstructive pulmonary disease patients: Results from a panel study in Beijing, China.

Science.gov (United States)

Li, Hongyu; Wu, Shaowei; Pan, Lu; Xu, Junhui; Shan, Jiao; Yang, Xuan; Dong, Wei; Deng, Furong; Chen, Yahong; Shima, Masayuki; Guo, Xinbiao

2018-01-01

Short-term exposure to ambient air pollution has been associated with lower pulmonary function and higher blood pressure (BP). However, controversy remains regarding the relationship between ambient multiple daily ozone (O 3 ) metrics and cardiopulmonary health outcomes, especially in the developing countries. To investigate and compare the short-term effects of various O 3 metrics on pulmonary function, fractional exhaled nitric oxide (FeNO) and BP in a panel study of COPD patients. We measured pulmonary function, FeNO and BP repeatedly in a total of 43 patients with COPD for 215 home visits. Daily hourly ambient O 3 concentrations were obtained from central-monitoring stations close to subject residences. We calculated various O 3 metrics [daily 1-h maximum (O 3 -1 h max), maximum 8-h average (O 3 -8 h max) and 24-h average (O 3 -24 h avg)] based on the hourly data. Daily indoor O 3 concentrations were estimated based on estimated indoor/outdoor O 3 ratios. Linear mixed-effects models were used to estimate associations of various O 3 metrics with cardiopulmonary function variables. An interquartile range (IQR) increase in ambient O 3 -8 h max (80.5 μg/m 3 , 5-d) was associated with a 5.9% (95%CI: -11.0%, -0.7%) reduction in forced expiratory volume in 1 s (FEV 1 ) and a 6.2% (95%CI: -10.9%, -1.5%) reduction in peak expiratory flow (PEF). However, there were no significant negative associations between ambient O 3 -1 h max, O 3 -24 h avg and FEV 1 , PEF. An IQR increase in ambient O 3 -1 h max (85.3 μg/m 3 , 6-d) was associated with a 6.7 mmHg (95%CI: 0.7, 12.7) increase in systolic BP. The estimated indoor O 3 were still significantly associated with reduction of FEV 1 and PEF. No significant associations were found between various O 3 metrics and FeNO. Our results provide clues for the adverse cardiopulmonary effects associated with various O 3 metrics in COPD patients and highlight that O 3 -8 h max was more closely associated with respiratory

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

Evaluation of tropospheric and stratospheric ozone trends over Western Europe from ground-based FTIR network observations

Directory of Open Access Journals (Sweden)

C. Vigouroux

2008-12-01

Full Text Available Within the European project UFTIR (Time series of Upper Free Troposphere observations from an European ground-based FTIR network, six ground-based stations in Western Europe, from 79° N to 28° N, all equipped with Fourier Transform infrared (FTIR instruments and part of the Network for the Detection of Atmospheric Composition Change (NDACC, have joined their efforts to evaluate the trends of several direct and indirect greenhouse gases over the period 1995–2004. The retrievals of CO, CH₄, C₂H₆, N₂O, CHClF₂, and O₃ have been optimized. Using the optimal estimation method, some vertical information can be obtained in addition to total column amounts. A bootstrap resampling method has been implemented to determine annual partial and total column trends for the target gases. The present work focuses on the ozone results. The retrieved time series of partial and total ozone columns are validated with ground-based correlative data (Brewer, Dobson, UV-Vis, ozonesondes, and Lidar. The observed total column ozone trends are in agreement with previous studies: 1 no total column ozone trend is seen at the lowest latitude station Izaña (28° N; 2 slightly positive total column trends are seen at the two mid-latitude stations Zugspitze and Jungfraujoch (47° N, only one of them being significant; 3 the highest latitude stations Harestua (60° N, Kiruna (68° N and Ny-Ålesund (79° N show significant positive total column trends. Following the vertical information contained in the ozone FTIR retrievals, we provide partial columns trends for the layers: ground-10 km, 10–18 km, 18–27 km, and 27–42 km, which helps to distinguish the contributions from dynamical and chemical changes on the total column ozone trends. We obtain no statistically significant trends in the ground-10 km layer for five out of the six ground-based stations. We find significant positive trends for the lowermost

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.

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

Science.gov (United States)

Sanderson, David J; Bannerman, David M

2011-04-01

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

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.

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.

Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)

OpenAIRE

Singh, Akash

2017-01-01

We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal time series patterns and predict future values. The resulting prediction errors are modeled to give anomaly scores. We investigate different ways of maintaining LSTM state, and the effect of using a fixed number of time steps on...

Evolution of Heterogeneous Wireless Networks

DEFF Research Database (Denmark)

Zhang, Q.; Fitzek, Frank; Katz, Marcos

2006-01-01

Mobile and wireless content, services and networks - Short-term and long-term development trends......Mobile and wireless content, services and networks - Short-term and long-term development trends...

Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China - Part 1: Overall trends and characteristics

Science.gov (United States)

Xu, Wanyun; Lin, Weili; Xu, Xiaobin; Tang, Jie; Huang, Jianqing; Wu, Hao; Zhang, Xiaochun

2016-05-01

Tropospheric ozone is an important atmospheric oxidant, greenhouse gas and atmospheric pollutant at the same time. The oxidation capacity of the atmosphere, climate, human and vegetation health can be impacted by the increase of the ozone level. Therefore, long-term determination of trends of baseline ozone is highly needed information for environmental and climate change assessment. So far, studies on the long-term trends of ozone at representative sites are mainly available for European and North American sites. Similar studies are lacking for China and many other developing countries. Measurements of surface ozone were carried out at a baseline Global Atmospheric Watch (GAW) station in the north-eastern Tibetan Plateau region (Mt Waliguan, 36°17' N, 100°54' E, 3816 m a.s.l.) for the period of 1994 to 2013. To uncover the variation characteristics, long-term trends and influencing factors of surface ozone at this remote site in western China, a two-part study has been carried out, with this part focusing on the overall characteristics of diurnal, seasonal and long-term variations and the trends of surface ozone. To obtain reliable ozone trends, we performed the Mann-Kendall trend test and the Hilbert-Huang transform (HHT) analysis on the ozone data. Our results confirm that the mountain-valley breeze plays an important role in the diurnal cycle of surface ozone at Waliguan, resulting in higher ozone values during the night and lower ones during the day, as was previously reported. Systematic diurnal and seasonal variations were found in mountain-valley breezes at the site, which were used in defining season-dependent daytime and nighttime periods for trend calculations. Significant positive trends in surface ozone were detected for both daytime (0.24 ± 0.16 ppbv year-1) and nighttime (0.28 ± 0.17 ppbv year-1). The largest nighttime increasing rate occurred in autumn (0.29 ± 0.11 ppbv year-1), followed by spring (0.24 ± 0.12 ppbv year-1), summer (0.22 ± 0

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 ...

A hybrid process of biofiltration of secondary effluent followed by ozonation and short soil aquifer treatment for water reuse.

Science.gov (United States)

Zucker, I; Mamane, H; Cikurel, H; Jekel, M; Hübner, U; Avisar, D

2015-11-01

The Shafdan reclamation project facility (Tel Aviv, Israel) practices soil aquifer treatment (SAT) of secondary effluent with hydraulic retention times (HRTs) of a few months to a year for unrestricted agricultural irrigation. During the SAT, the high oxygen demand (>40 mg L(-1)) of the infiltrated effluent causes anoxic conditions and mobilization of dissolved manganese from the soil. An additional emerging problem is the occurrence of persistent trace organic compounds (TrOCs) in reclaimed water that should be removed prior to reuse. An innovative hybrid process based on biofiltration, ozonation and short SAT with ∼22 d HRT is proposed for treatment of the Shafdan secondary effluent to overcome limitations of the existing system and to reduce the SAT's physical footprint. Besides efficient removal of particulate matter to minimize clogging, coagulation/flocculation and filtration (5-6 m h(-1)) operated with the addition of hydrogen peroxide as an oxygen source efficiently removed dissolved organic carbon (DOC, to 17-22%), ammonium and nitrite. This resulted in reduced effluent oxygen demand during infiltration and oxidant (ozone) demand during ozonation by 23 mg L(-1) and 1.5 mg L(-1), respectively. Ozonation (1.0-1.2 mg O3 mg DOC(-1)) efficiently reduced concentrations of persistent TrOCs and supplied sufficient dissolved oxygen (>30 mg L(-1)) for fully oxic operation of the short SAT with negligible Mn(2+) mobilization (<50 μg L(-1)). Overall, the examined hybrid process provided DOC reduction of 88% to a value of 1.2 mg L(-1), similar to conventional SAT, while improving the removal of TrOCs and efficiently preventing manganese dissolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Solar Backscatter UV (SBUV total ozone and profile algorithm

Directory of Open Access Journals (Sweden)

P. K. Bhartia

2013-10-01

Full Text Available We describe the algorithm that has been applied to develop a 42 yr record of total ozone and ozone profiles from eight Solar Backscatter UV (SBUV instruments launched on NASA and NOAA satellites since April 1970. The Version 8 (V8 algorithm was released more than a decade ago and has been in use since then at NOAA to produce their operational ozone products. The current algorithm (V8.6 is basically the same as V8, except for updates to instrument calibration, incorporation of new ozone absorption cross-sections, and new ozone and cloud height climatologies. Since the V8 algorithm has been optimized for deriving monthly zonal mean (MZM anomalies for ozone assessment and model comparisons, our emphasis in this paper is primarily on characterizing the sources of errors that are relevant for such studies. When data are analyzed this way the effect of some errors, such as vertical smoothing of short-term variability, and noise due to clouds and aerosols diminish in importance, while the importance of others, such as errors due to vertical smoothing of the quasi-biennial oscillation (QBO and other periodic and aperiodic variations, become more important. With V8.6 zonal mean data we now provide smoothing kernels that can be used to compare anomalies in SBUV profile and partial ozone columns with models. In this paper we show how to use these kernels to compare SBUV data with Microwave Limb Sounder (MLS ozone profiles. These kernels are particularly useful for comparisons in the lower stratosphere where SBUV profiles have poor vertical resolution but partial column ozone values have high accuracy. We also provide our best estimate of the smoothing errors associated with SBUV MZM profiles. Since smoothing errors are the largest source of uncertainty in these profiles, they can be treated as error bars in deriving interannual variability and trends using SBUV data and for comparing with other measurements. In the V8 and V8.6 algorithms we derive total

Short-term Memory as a Processing Shift

Science.gov (United States)

Lewis-Smith, Marion Quinn

1975-01-01

The series of experiments described here examined the predictions for free recall from sequential models and the shift formulation, focusing on the roles of short- and long-term memory in the primacy/recency shift and on the effects of expectancies on short- and long-term memory. (Author/RK)

Study of Ozone Layer Variability near St. Petersburg on the Basis of SBUV Satellite Measurements and Numerical Simulation (2000-2014)

Science.gov (United States)

Virolainen, Y. A.; Timofeyev, Y. M.; Smyshlyaev, S. P.; Motsakov, M. A.; Kirner, O.

2017-12-01

A comparison between the numerical simulation results of ozone fields with different experimental data makes it possible to estimate the quality of models for their further use in reliable forecasts of ozone layer evolution. We analyze time series of satellite (SBUV) measurements of the total ozone column (TOC) and the ozone partial columns in two atmospheric layers (0-25 and 25-60 km) and compare them with the results of numerical simulation in the chemistry transport model (CTM) for the low and middle atmosphere and the chemistry climate model EMAC. The daily and monthly average ozone values, short-term periods of ozone depletion, and long-term trends of ozone columns are considered; all data sets relate to St. Petersburg and the period between 2000 and 2014. The statistical parameters (means, standard deviations, variations, medians, asymmetry parameter, etc.) of the ozone time series are quite similar for all datasets. However, the EMAC model systematically underestimates the ozone columns in all layers considered. The corresponding differences between satellite measurements and EMAC numerical simulations are (5 ± 5)% and (7 ± 7)% and (1 ± 4)% for the ozone column in the 0-25 and 25-60 km layers, respectively. The correspondent differences between SBUV measurements and CTM results amount to (0 ± 7)%, (1 ± 9)%, and (-2 ± 8)%. Both models describe the sudden episodes of the ozone minimum well, but the EMAC accuracy is much higher than that of the CTM, which often underestimates the ozone minima. Assessments of the long-term linear trends show that they are close to zero for all datasets for the period under study.

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.

On the relationship between short- and long-term memory

DEFF Research Database (Denmark)

Sørensen, Thomas Alrik

James (1890) divided memory into separate stores; primary and secondary – or short-term and long-term memory. The interaction between the two stores often assumes that information initially is represented in volatile short-term store before entering and consolidating in the more durable long-term......, accepted). Counter to popular beliefs this suggest that long-term memory precedes short-term memory and not vice versa....... memory system (e.g. Atkinson & Shiffrin, 1968). Short-term memory seems to provide a surprising processing bottleneck where only a very limited amount of information can be represented at any given moment (Miller, 1956; Cowan, 2001). A number of studies have investigated the nature of this processing...

High resolution tempo-spatial ozone prediction with SVM and LSTM

Science.gov (United States)

Gao, D.; Zhang, Y.; Qu, Z.; Sadighi, K.; Coffey, E.; LIU, Q.; Hannigan, M.; Henze, D. K.; Dick, R.; Shang, L.; Lv, Q.

2017-12-01

To investigate and predict the exposure of ozone and other pollutants in urban areas, we utilize data from various infrastructures including EPA, NOAA and RIITS from government of Los Angeles and construct statistical models to conduct ozone concentration prediction in Los Angeles areas at finer spatial and temporal granularity. Our work involves cyber data such as traffic, roads and population data as features for prediction. Two statistical models, Support Vector Machine (SVM) and Long Short-term Memory (LSTM, deep learning method) are used for prediction. . Our experiments show that kernelized SVM gains better prediction performance when taking traffic counts, road density and population density as features, with a prediction RMSE of 7.99 ppb for all-time ozone and 6.92 ppb for peak-value ozone. With simulated NOx from Chemical Transport Model(CTM) as features, SVM generates even better prediction performance, with a prediction RMSE of 6.69ppb. We also build LSTM, which has shown great advantages at dealing with temporal sequences, to predict ozone concentration by treating ozone concentration as spatial-temporal sequences. Trained by ozone concentration measurements from the 13 EPA stations in LA area, the model achieves 4.45 ppb RMSE. Besides, we build a variant of this model which adds spatial dynamics into the model in the form of transition matrix that reveals new knowledge on pollutant transition. The forgetting gate of the trained LSTM is consistent with the delay effect of ozone concentration and the trained transition matrix shows spatial consistency with the common direction of winds in LA area.

Short-term memory and long-term memory are still different.

Science.gov (United States)

Norris, Dennis

2017-09-01

A commonly expressed view is that short-term memory (STM) is nothing more than activated long-term memory. If true, this would overturn a central tenet of cognitive psychology-the idea that there are functionally and neurobiologically distinct short- and long-term stores. Here I present an updated case for a separation between short- and long-term stores, focusing on the computational demands placed on any STM system. STM must support memory for previously unencountered information, the storage of multiple tokens of the same type, and variable binding. None of these can be achieved simply by activating long-term memory. For example, even a simple sequence of digits such as "1, 3, 1" where there are 2 tokens of the digit "1" cannot be stored in the correct order simply by activating the representations of the digits "1" and "3" in LTM. I also review recent neuroimaging data that has been presented as evidence that STM is activated LTM and show that these data are exactly what one would expect to see based on a conventional 2-store view. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Quality assessment of the Ozone_cci Climate Research Data Package (release 2017) - Part 1: Ground-based validation of total ozone column data products

Science.gov (United States)

Garane, Katerina; Lerot, Christophe; Coldewey-Egbers, Melanie; Verhoelst, Tijl; Elissavet Koukouli, Maria; Zyrichidou, Irene; Balis, Dimitris S.; Danckaert, Thomas; Goutail, Florence; Granville, Jose; Hubert, Daan; Keppens, Arno; Lambert, Jean-Christopher; Loyola, Diego; Pommereau, Jean-Pierre; Van Roozendael, Michel; Zehner, Claus

2018-03-01

The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a level-3 data record, which combines individual sensor products into one single cohesive record covering the 22-year period from 1995 to 2016, generated in the frame of the European Space Agency's Climate Change Initiative Phase II. It is based on level-2 total ozone data produced by the GODFIT (GOME-type Direct FITting) v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/Metop-A and Metop-B observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate-chemistry modelling community for decadal stability long-term and short-term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC) at both level 2 and level 3. The inter-sensor consistency of the individual level-2 data sets has mean differences generally within 0.5 % at moderate latitudes (±50°), whereas the level-3 data sets show mean differences with respect to the OMI reference data record that span between -0.2 ± 0.9 % (for GOME-2B) and 1.0 ± 1.4 % (for SCIAMACHY). Very similar findings are reported for the level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments: the mean bias between GODFIT v4 satellite TOC and the ground instrument is well within 1.0 ± 1.0 % for all sensors, the drift per decade spans between -0.5 % and 1.0 ± 1.0 % depending on the sensor, and the peak-to-peak seasonality of the differences ranges from ˜ 1 % for GOME and OMI to ˜ 2 % for SCIAMACHY. For the level-3 validation, our first goal was to show that the level-3 CRDP produces findings consistent with the level-2 individual sensor comparisons. We show a very good agreement with 0.5 to 2 % peak-to-peak amplitude for the monthly mean difference time series and a negligible drift per decade of the differences in the Northern Hemisphere

Effect of ozone and histamine on airway permeability to horseradish peroxidase in guinea pigs

International Nuclear Information System (INIS)

Miller, P.D.; Gordon, T.; Warnick, M.; Amdur, M.O.

1986-01-01

Airway permeability was studied in groups of male guinea pigs at 2, 8, and 24 h after a 1-h exposure to 1 ppm ozone or at 2 h after a 1-h exposure to filtered air (control). Intratracheal administration of 2 mg horseradish peroxidase (HRP) was followed by blood sampling at 5-min intervals up to 30 min. The rate of appearance of HRP in plasma was significantly higher at 2 and 8 h after ozone exposure than that found in animals examined 2 h after air exposure or 24 h after ozone exposure. A dose of 0.12 mg/kg of subcutaneous histamine given after the 15 min blood sample significantly increased the already elevated permeability seen at 2 h post ozone, but had no effect on animals exposed to filtered air 2 h earlier or to ozone 24 h earlier. No difference was seen in the amount of subcutaneous radiolabeled histamine in the lungs of animals exposed 2 h earlier either to air or to ozone. These data indicate that a short-term exposure to ozone produced a reversible increase in respiratory epithelial permeability to HRP in guinea pigs. The potentiation of this increased permeability by histamine may be another manifestation of ozone-induced hyperreactivity

Spektroskopische (DOAS)-Langzeitmessungen von Ozon und Vorlaeufersubstanzen an der Ostseekuestenstation Arkona. Abschlussbericht; Long term spectroscopic (DOAS) measurement of ozone and related species at the Baltic coast station Arkona. Final report

Energy Technology Data Exchange (ETDEWEB)

Dubois, R [Institut fuer Troposphaerenforschung e.V. (IfT), Leipzig (Germany); Flentje, H [Institut fuer Troposphaerenforschung e.V. (IfT), Leipzig (Germany); Heintz, F [Heidelberg Univ. (Germany). Inst. fuer Umweltphysik; Karbach, H J [Institut fuer Troposphaerenforschung e.V. (IfT), Leipzig (Germany); Stutz, J [Heidelberg Univ. (Germany). Inst. fuer Umweltphysik; Platt, U [Heidelberg Univ. (Germany). Inst. fuer Umweltphysik

1996-08-01

Boundary layer ozone concentrations have been recorded since 1956 by the German Weather Service, (MD / DWD) at Cape Arkona, Island of Ruegen, GDR / FRG. In April 1993, a Long Path Differential Optical Absorption Spectrometer (LP-DOAS) was set up near the DWD-site. Measurements of the concentrations of O{sub 3}, NO{sub 2}, NO{sub 3} and SO{sub 2} were carried out with a newly developed DOAS system. The system incorporates two coaxially arranged Newton-type telescopes, a flat field holographic grating spectrometer and a retro reflector array. Combining the meteorological data with ozone and other gas concentrations, sector-classified results are used to identify the constraints for future evaluations of regional long-term trends of ozone concentrations. A statistical analysis of different trace gases for the periods summer, autumn, winter and spring is prepared. The long term nitrate radicals data record is used to retrieve information on the production rate of nitrate radicals, its lifetime, and possible depletion mechanism. (orig.)

Short-term power plant operation scheduling in thermal systems with long-term boundary conditions

International Nuclear Information System (INIS)

Wolter, H.

1990-01-01

For the first time, the modeling of long-term quantitative conditions within the short-term planning of the application of power stations is made via their shadow prices. It corresponds to a decomposition of the quantitative conditions by means of the method of the Langrange relaxation. The shadow prices determined by the planning for energy application regarding long- term quantitative conditions pass into the short-term planning for power station application and subsidize or rather punish the application of limited amounts as for as they are not claimed for sufficiently or excessively. The clear advantage of this modeling is that the short-term planning of power station application can deviate from the envisioned energy application regarding the total optimum, because the shadow prices contain all information about the cost effect of the energy shifts in the residual total period, which become necessary due to the deviations in the short-term period to be planned in the current short-term period. (orig./DG) [de

Long-term ozone decline and its effect on night airglow intensity of Li ...

Indian Academy of Sciences (India)

A critical analysis has been made on the long-term yearly and seasonal variations of ozone concentration at Varanasi (25°N, 83°E), India and Halley Bay (76°S, 27°W), a British Antarctic Service Station. The effect of O3 depletion on night airglow emission of Li 6708 Å line at Varanasi and Halley Bay has been studied.

OTDM Networking for Short Range High-Capacity Highly Dynamic Networks

DEFF Research Database (Denmark)

Medhin, Ashenafi Kiros

This PhD thesis aims at investigating the possibility of designing energy-efficient high-capacity (up to Tbit/s) optical network scenarios, leveraging on the effect of collective switching of many bits simultaneously, as is inherent in high bit rate serial optical data signals. The focus...... is on short range highly dynamic networks, catering to data center needs. The investigation concerns optical network scenarios, and experimental implementations of high bit rate serial data packet generation and reception, scalable optical packet labeling, simple optical label extraction and stable ultra...

Analysis of surface ozone using a recurrent neural network.

Science.gov (United States)

Biancofiore, Fabio; Verdecchia, Marco; Di Carlo, Piero; Tomassetti, Barbara; Aruffo, Eleonora; Busilacchio, Marcella; Bianco, Sebastiano; Di Tommaso, Sinibaldo; Colangeli, Carlo

2015-05-01

Hourly concentrations of ozone (O₃) and nitrogen dioxide (NO₂) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O₃ and NO₂ recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O₃ concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O₃ have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O₃ hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O₃ levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O₃ also in sites where it has not been measured yet. Copyright © 2015 Elsevier B.V. All rights reserved.

Network operating system

Science.gov (United States)

1985-01-01

Long-term and short-term objectives for the development of a network operating system for the Space Station are stated. The short-term objective is to develop a prototype network operating system for a 100 megabit/second fiber optic data bus. The long-term objective is to establish guidelines for writing a detailed specification for a Space Station network operating system. Major milestones are noted. Information is given in outline form.

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

Lessons from the Ozone Hole

International Nuclear Information System (INIS)

Benedick, R.E.

1991-01-01

On September 16, 1987, a treaty was signed that was unique in that annals of international diplomacy. The Montreal Protocol on substrates that Deplete the Ozone Layer mandated significant reductions in the use of chlorofluorocarbons (CFCs) and halons. Perhaps the most extraordinary aspect of the Montreal Protocol was that it imposed substantial short-term economic costs in order to protect human health and the environment against speculative future dangers - dangers which rested on scientific theories rather than on proven facts. Unlike environmental agreements of the past, it was not a response to harmful events, but rather preventive action on a global scale. In the realm of international relations, there will always be resistance to change and there will always be uncertainties - political, economic, scientific, psychological. The ozone negotiations demonstrated that the international community, even in the real world of ambiguity and imperfect knowledge, can be capable of undertaking difficult cooperative actions for the benefit of future generation. The Montreal Protocol may well be a paradigm for international cooperation on the challenge of global warming

Mid-latitude tropospheric ozone columns from the MOZAIC program: climatology and interannual variability

Directory of Open Access Journals (Sweden)

R. M. Zbinden

2006-01-01

Full Text Available Several thousands of ozone vertical profiles collected in the course of the MOZAIC programme (Measurements of Ozone, Water Vapour, Carbon Monoxide and Nitrogen Oxides by In-Service Airbus Aircraft from August 1994 to February 2002 are investigated to bring out climatological and interannual variability aspects. The study is centred on the most frequently visited MOZAIC airports, i.e. Frankfurt (Germany, Paris (France, New York (USA and the cluster of Tokyo, Nagoya and Osaka (Japan. The analysis focuses on the vertical integration of ozone from the ground to the dynamical tropopause and the vertical integration of stratospheric-origin ozone throughout the troposphere. The characteristics of the MOZAIC profiles: frequency of flights, accuracy, precision, and depth of the troposphere observed, are presented. The climatological analysis shows that the Tropospheric Ozone Column (TOC seasonal cycle ranges from a wintertime minimum at all four stations to a spring-summer maximum in Frankfurt, Paris, and New York. Over Japan, the maximum occurs in spring presumably because of the earlier springtime sun. The incursion of monsoon air masses into the boundary layer and into the mid troposphere then steeply diminishes the summertime value. Boundary layer contributions to the TOC are 10% higher in New York than in Frankfurt and Paris during spring and summer, and are 10% higher in Japan than in New York, Frankfurt and Paris during autumn and early spring. Local and remote anthropogenic emissions, and biomass burning over upstream regions of Asia may be responsible for the larger low- and mid-tropospheric contributions to the tropospheric ozone column over Japan throughout the year except during the summer-monsoon season. A simple Lagrangian analysis has shown that a minimum of 10% of the TOC is of stratospheric-origin throughout the year. Investigation of the short-term trends of the TOC over the period 1995–2001 shows a linear increase 0.7%/year in

The Structure and Content of Long-Term and Short-Term Mate Preferences

Directory of Open Access Journals (Sweden)

Peter K. Jonason

2013-12-01

Full Text Available This study addresses two limitations in the mate preferences literature. First, research all-too-often relies on single-item assessments of mate preferences precluding more advanced statistical techniques like factor analysis. Second, when factor analysis could be done, it exclusively has done for long-term mate preferences, at the exclusion of short-term mate preferences. In this study (N = 401, we subjected 20 items designed to measure short- and long-term mate preferences to both principle components (n = 200 and confirmatory factor analysis (n = 201. In the long-term context, we replicated previous findings that there are three different categories of preferences: physical attractiveness, interpersonal warmth, and social status. In the short-term context, physical attractiveness occupied two parts of the structure, social status dropped out, and interpersonal warmth remained. Across short- and long-term contexts, there were slight changes in what defined the shared dimensions (i.e., physical attractiveness and interpersonal warmth, suggesting prior work that applies the same inventory to each context might be flawed. We also replicated sex differences and similarities in mate preferences and correlates with sociosexuality and mate value. We adopt an evolutionary paradigm to understand our results.

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

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.

Short-Term Dosage Regimen for Stimulation-Induced Long-Lasting Desynchronization

Directory of Open Access Journals (Sweden)

Thanos Manos

2018-04-01

Full Text Available In this paper, we computationally generate hypotheses for dose-finding studies in the context of desynchronizing neuromodulation techniques. Abnormally strong neuronal synchronization is a hallmark of several brain disorders. Coordinated Reset (CR stimulation is a spatio-temporally patterned stimulation technique that specifically aims at disrupting abnormal neuronal synchrony. In networks with spike-timing-dependent plasticity CR stimulation may ultimately cause an anti-kindling, i.e., an unlearning of abnormal synaptic connectivity and neuronal synchrony. This long-lasting desynchronization was theoretically predicted and verified in several pre-clinical and clinical studies. We have shown that CR stimulation with rapidly varying sequences (RVS robustly induces an anti-kindling at low intensities e.g., if the CR stimulation frequency (i.e., stimulus pattern repetition rate is in the range of the frequency of the neuronal oscillation. In contrast, CR stimulation with slowly varying sequences (SVS turned out to induce an anti-kindling more strongly, but less robustly with respect to variations of the CR stimulation frequency. Motivated by clinical constraints and inspired by the spacing principle of learning theory, in this computational study we propose a short-term dosage regimen that enables a robust anti-kindling effect of both RVS and SVS CR stimulation, also for those parameter values where RVS and SVS CR stimulation previously turned out to be ineffective. Intriguingly, for the vast majority of parameter values tested, spaced multishot CR stimulation with demand-controlled variation of stimulation frequency and intensity caused a robust and pronounced anti-kindling. In contrast, spaced CR stimulation with fixed stimulation parameters as well as singleshot CR stimulation of equal integral duration failed to improve the stimulation outcome. In the model network under consideration, our short-term dosage regimen enables to robustly induce

Short-Term Dosage Regimen for Stimulation-Induced Long-Lasting Desynchronization.

Science.gov (United States)

Manos, Thanos; Zeitler, Magteld; Tass, Peter A

2018-01-01

In this paper, we computationally generate hypotheses for dose-finding studies in the context of desynchronizing neuromodulation techniques. Abnormally strong neuronal synchronization is a hallmark of several brain disorders. Coordinated Reset (CR) stimulation is a spatio-temporally patterned stimulation technique that specifically aims at disrupting abnormal neuronal synchrony. In networks with spike-timing-dependent plasticity CR stimulation may ultimately cause an anti-kindling, i.e., an unlearning of abnormal synaptic connectivity and neuronal synchrony. This long-lasting desynchronization was theoretically predicted and verified in several pre-clinical and clinical studies. We have shown that CR stimulation with rapidly varying sequences (RVS) robustly induces an anti-kindling at low intensities e.g., if the CR stimulation frequency (i.e., stimulus pattern repetition rate) is in the range of the frequency of the neuronal oscillation. In contrast, CR stimulation with slowly varying sequences (SVS) turned out to induce an anti-kindling more strongly, but less robustly with respect to variations of the CR stimulation frequency. Motivated by clinical constraints and inspired by the spacing principle of learning theory, in this computational study we propose a short-term dosage regimen that enables a robust anti-kindling effect of both RVS and SVS CR stimulation, also for those parameter values where RVS and SVS CR stimulation previously turned out to be ineffective. Intriguingly, for the vast majority of parameter values tested, spaced multishot CR stimulation with demand-controlled variation of stimulation frequency and intensity caused a robust and pronounced anti-kindling. In contrast, spaced CR stimulation with fixed stimulation parameters as well as singleshot CR stimulation of equal integral duration failed to improve the stimulation outcome. In the model network under consideration, our short-term dosage regimen enables to robustly induce long-term

Assessing the risk of foliar injury from ozone on vegetation in parks in the U.S. National Park Service's Vital Signs Network

Energy Technology Data Exchange (ETDEWEB)

Kohut, Robert [Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, NY 14853 (United States)], E-mail: rjk9@cornell.edu

2007-10-15

The risk of ozone injury to plants was assessed in support of the National Park Service's Vital Signs Monitoring Network program. The assessment examined bioindicator species, evaluated levels of ozone exposure, and investigated soil moisture conditions during periods of exposure for a 5-year period in each park. The assessment assigned each park a risk rating of high, moderate, or low. For the 244 parks for which assessments were conducted, the risk of foliar injury was high in 65 parks, moderate in 46 parks, and low in 131 parks. Among the well-known parks with a high risk of ozone injury are Gettysburg, Valley Forge, Delaware Water Gap, Cape Cod, Fire Island, Antietam, Harpers Ferry, Manassas, Wolf Trap Farm Park, Mammoth Cave, Shiloh, Sleeping Bear Dunes, Great Smoky Mountains, Joshua Tree, Sequoia and Kings Canyon, and Yosemite. - An assessment of the risk of foliar ozone injury on plants was conducted for 269 parks in support of the U.S. National Park Service's Vital Signs Monitoring Network Program.

Ozone initiated reactions and human comfort in indoor environments

DEFF Research Database (Denmark)

Tamas, Gyöngyi

2006-01-01

Chemical reactions between ozone and pollutants commonly found indoors have been suggested to cause adverse health and comfort effects among building occupants. Of special interest are reactions with terpenes and other pollutants containing unsaturated carbon-carbon bonds that are fast enough...... to occur under normal conditions in various indoor settings. These reactions are known to occur both in the gas phase (homogeneous reactions) and on the surfaces of building materials (heterogeneous reactions), producing a number of compounds that can be orders of magnitude more odorous and irritating than...... their precursors. The present thesis investigates the effects of ozone-initiated reactions with limonene and with various interior surfaces, including those associated with people, on short-term sensory responses. The evaluations were conducted using a perceived air quality (PAQ) method introduced by Fanger (1988...

Visibility graph analysis of very short-term heart rate variability during sleep

Science.gov (United States)

Hou, F. Z.; Li, F. W.; Wang, J.; Yan, F. R.

2016-09-01

Based on a visibility-graph algorithm, complex networks were constructed from very short-term heart rate variability (HRV) during different sleep stages. Network measurements progressively changed from rapid eye movement (REM) sleep to light sleep and then deep sleep, exhibiting promising ability for sleep assessment. Abnormal activation of the cardiovascular controls with enhanced 'small-world' couplings and altered fractal organization during REM sleep indicates that REM could be a potential risk factor for adverse cardiovascular event, especially in males, older individuals, and people who are overweight. Additionally, an apparent influence of gender, aging, and obesity on sleep was demonstrated in healthy adults, which may be helpful for establishing expected sleep-HRV patterns in different populations.

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…

Attention Problems, Phonological Short-Term Memory, and Visuospatial Short-Term Memory: Differential Effects on Near- and Long-Term Scholastic Achievement

Science.gov (United States)

Sarver, Dustin E.; Rapport, Mark D.; Kofler, Michael J.; Scanlan, Sean W.; Raiker, Joseph S.; Altro, Thomas A.; Bolden, Jennifer

2012-01-01

The current study examined individual differences in children's phonological and visuospatial short-term memory as potential mediators of the relationship among attention problems and near- and long-term scholastic achievement. Nested structural equation models revealed that teacher-reported attention problems were associated negatively with…

Effect of Pulse Width on Ozone Generation in Pulsed Streamer Discharges

OpenAIRE

Tamaribuchi, Hiroyuki; Wang, Douyan; Namihira, Takao; Katsuki, Sunao; Akiyama, Hidenori; タマリブチ, ヒロユキ; オウ, トエン; ナミヒラ, タカオ; カツキ, スナオ; アキヤマ, ヒデノリ; 溜渕, 浩之; 王, 斗艶; 浪平, 隆男; 勝木, 淳; 秋山, 秀典

2007-01-01

Ozone has been used in treatment of drinking water andwaste water (e.g., deodorization, decolorization, anddisinfection). Though general ozonizers based on silentdischarge or barrier discharge have been used to supplyozone at many industrial situations, there is still someproblem, such as improvements of ozone concentrationand ozone yield.In this work, ozone was generated by pulsed powerdischarge in order to improve the characteristics of ozonegeneration. High electric field with short pulse ...

Partially Overlapping Sensorimotor Networks Underlie Speech Praxis and Verbal Short-Term Memory: Evidence from Apraxia of Speech Following Acute Stroke

Directory of Open Access Journals (Sweden)

Gregory eHickok

2014-08-01

Full Text Available We tested the hypothesis that motor planning and programming of speech articulation and verbal short-term memory (vSTM depend on partially overlapping networks of neural regions. We evaluated this proposal by testing 76 individuals with acute ischemic stroke for impairment in motor planning of speech articulation (apraxia of speech; AOS and vSTM in the first day of stroke, before the opportunity for recovery or reorganization of structure-function relationships. We also evaluate areas of both infarct and low blood flow that might have contributed to AOS or impaired vSTM in each person. We found that AOS was associated with tissue dysfunction in motor-related areas (posterior primary motor cortex, pars opercularis; premotor cortex, insula and sensory-related areas (primary somatosensory cortex, secondary somatosensory cortex, parietal operculum/auditory cortex; while impaired vSTM was associated with primarily motor-related areas (pars opercularis and pars triangularis, premotor cortex, and primary motor cortex. These results are consistent with the hypothesis, also supported by functional imaging data, that both speech praxis and vSTM rely on partially overlapping networks of brain regions.

Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Science.gov (United States)

Johnson, Matthew S.; Sullivan, John T.; Liu, Xiong; Newchurch, Mike; Kuang, Shi; McGee, Thomas J.; Langford, Andrew O'Neil; Senff, Christoph J.; Leblanc, Thierry; Berkoff, Timothy;

Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Science.gov (United States)

Johnson, M. S.; Sullivan, J. T.; Liu, X.; Newchurch, M.; Kuang, S.; McGee, T. J.; Langford, A. O.; Senff, C. J.; Leblanc, T.; Berkoff, T.; Gronoff, G.; Chen, G.; Strawbridge, K. B.

2016-12-01

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.

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…

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...

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.

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

Global pictures of the ozone field from high altitudes from DE-I

Science.gov (United States)

Keating, G. M.; Frank, L.; Craven, J.; Shapiro, M.; Young, D.; Bhartia, P.

1982-01-01

Detailed synoptic views of the column ozone field can be obtained by the Spin-Scan Ozone Imager (SOI) (Keating et al., 1981) aboard the Dynamics Explorer I satellite. The eccentric polar orbit with an apogee altitude of 23,000 km allows high resolution global-scale images to be obtained within 12 minutes, and allows regions to be viewed for long periods of time. At perigee, a pixel size of nadir measurements of 3 km is possible, and measurements are determined using the backscattered ultraviolet technique. A wavelength measurement of 317.5 nm is used as there are limitations in filter locations and it allows comparison with Nimbus 7 SBUV/TOMS data. Consideration of the reflectivities of this data aids in checking the SOI data reduction algorithm. SOI data show short-term (less than one day) variations in the observed ozone field, and a negative correlation (greater than 0.9) between ozone and tropopause heights. It is expected, due to this correlation, that SOI data will aid in understanding the time evolution of dynamics near the tropopause.

Trend prognosis of regional ozone maxima in 1994 using various meteorologic data

International Nuclear Information System (INIS)

Loibl, W.

1995-06-01

The purpose of this study was to develop and test a statistical method for the short-term forecast of ozone concentrations. Austrian ozone monitoring data from April to September 1994 are used to develop the forecast model. It builds upon a multiple linear regression model developed earlier which uses the temperature of the forecast day, and the ozone maxima of the previous day as variables. In this study temperature difference between previous and forecast day, and wind velocity of the forecast day were additionally taken into account. Furthermore wind direction dependent regression models were developed using subsamples of the data set devided into 8 wind direction classes. Different regression function parameters have to be applied for each of the 40 selected ozone monitoring sites to allow forecasting of regional ozone maxima throughout Austria. It was found that regression models with temperature difference and wind velocity as additional variables did not improve the results. Wind direction dependent regression models only slightly improved the results for some wind directions at several monitoring sites. Best forecast results in general were achieved by using the base regression model with the temperature of the forecast day and the ozone maxima of the previous day as variables. Ozone forecast maps were calculated by spatial interpolation of the forecasted ozone maxima of the monitoring sites. Forecast accuracy is within ± 10 ppb on 70-80 % of the observed days. Errors higher than ± 10 ppb occur mainly on days with ozone maxima of 80 ppb and more. (author)

Dobson spectrophotometer ozone measurements during international ozone rocketsonde intercomparison

Science.gov (United States)

Parsons, C. L.

1980-01-01

Measurements of the total ozone content of the atmosphere, made with seven ground based instruments at a site near Wallops Island, Virginia, are discussed in terms for serving as control values with which the rocketborne sensor data products can be compared. These products are profiles of O3 concentration with altitude. By integrating over the range of altitudes from the surface to the rocket apogee and by appropriately estimating the residual ozone amount from apogee to the top of the atmosphere, a total ozone amount can be computed from the profiles that can be directly compared with the ground based instrumentation results. Dobson spectrophotometers were used for two of the ground-based instruments. Preliminary data collected during the IORI from Dobson spectrophotometers 72 and 38 are presented. The agreement between the two and the variability of total ozone overburden through the experiment period are discussed.

Long-term trends in the northern extratropical ozone laminae with focus on European stations

Czech Academy of Sciences Publication Activity Database

Laštovička, Jan; Križan, Peter; Kozubek, Michal

2014-01-01

Roč. 120, December (2014), s. 88-95 ISSN 1364-6826 R&D Projects: GA MŠk LD12070; GA ČR GAP209/10/1792 Institutional support: RVO:68378289 Keywords : ozone laminae * long-term trends * atmospheric dynamics Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.474, year: 2014 http://www.sciencedirect.com/science/article/pii/S1364682614002119

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.

Advanced treatment of biotreated textile industry wastewater with ozone, virgin/ozonated granular activated carbon and their combination.

Science.gov (United States)

Arslan-Alaton, Idil; Seremet, Ozden

2004-01-01

Biotreated textile wastewater (CODo = 248 mg L(-1); TOCo = 58 mg L(-1); A620 = 0.007 cm(-1); A525 = 0.181 cm(-1); A436 = 0.198 cm(-1)) was subjected to advanced treatment with ozonation, granular activated carbon (GAC) adsorption in serial and simultaneous applications. Experiments were conducted to investigate the effects of applied ozone dose, ozone absorption rate, specific ozone absorption efficiency, GAC dose, and reaction pH on the treatment performance of the selected tertiary treatment scheme. In separate experiments, the impact of virgin GAC ozonation on its adsorptive capacity for biotreated and biotreated + ozonated textile effluent was also investigated. Ozonation appeared to be more effective for decolorization (kd = 0.15 min(-1) at pH = 3), whereas GAC adsorption yielded higher COD removal rates (54% at pH = 3). It was also found that GAC addition (4 g/L) at pH = 7 and 9 enhanced the COD abatement rate of the ozonation process significantly and that the sequential application of ozonation (at pH = 3-11, 675 mg L(-1) O3) followed by GAC adsorption (at pH = 3-7, 10 g L(-1) GAC) resulted in the highest treatment performances both in terms of color and COD reduction. Simultaneous application of GAC and ozone at acidic and alkaline pH seriously inhibited COD abatement rates as a consequence of competitive adsorption and partial oxidation of textile components and GAC. It could also be established that ozone absorption efficiency decreased after color removal was complete. Ozonation of biotreated textile wastewater with 113 mg L(-1) ozone resulted in an appreciable enhancement of GAC adsorptive capacity in terms of residual color removal. Ozonation of GAC at relatively low doses (= 10.8 mg/g GAC) did not improve its overall adsorption capacity.

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.

Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring

Science.gov (United States)

Zhang, Duo; Lindholm, Geir; Ratnaweera, Harsha

2018-01-01

Combined sewer overflow causes severe water pollution, urban flooding and reduced treatment plant efficiency. Understanding the behavior of CSO structures is vital for urban flooding prevention and overflow control. Neural networks have been extensively applied in water resource related fields. In this study, we collect data from an Internet of Things monitoring CSO structure and build different neural network models for simulating and predicting the water level of the CSO structure. Through a comparison of four different neural networks, namely multilayer perceptron (MLP), wavelet neural network (WNN), long short-term memory (LSTM) and gated recurrent unit (GRU), the LSTM and GRU present superior capabilities for multi-step-ahead time series prediction. Furthermore, GRU achieves prediction performances similar to LSTM with a quicker learning curve.

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...

The interaction of short-term and long-term memory in phonetic category formation

Science.gov (United States)

Harnsberger, James D.

2002-05-01

This study examined the role that short-term memory capacity plays in the relationship between novel stimuli (e.g., non-native speech sounds, native nonsense words) and phonetic categories in long-term memory. Thirty native speakers of American English were administered five tests: categorial AXB discrimination using nasal consonants from Malayalam; categorial identification, also using Malayalam nasals, which measured the influence of phonetic categories in long-term memory; digit span; nonword span, a short-term memory measure mediated by phonetic categories in long-term memory; and paired-associate word learning (word-word and word-nonword pairs). The results showed that almost all measures were significantly correlated with one another. The strongest predictor for the discrimination and word-nonword learning results was nonword (r=+0.62) and digit span (r=+0.51), respectively. When the identification test results were partialed out, only nonword span significantly correlated with discrimination. The results show a strong influence of short-term memory capacity on the encoding of phonetic detail within phonetic categories and suggest that long-term memory representations regulate the capacity of short-term memory to preserve information for subsequent encoding. The results of this study will also be discussed with regards to resolving the tension between episodic and abstract models of phonetic category structure.

Semantic and phonological contributions to short-term repetition and long-term cued sentence recall.

Science.gov (United States)

Meltzer, Jed A; Rose, Nathan S; Deschamps, Tiffany; Leigh, Rosie C; Panamsky, Lilia; Silberberg, Alexandra; Madani, Noushin; Links, Kira A

2016-02-01

The function of verbal short-term memory is supported not only by the phonological loop, but also by semantic resources that may operate on both short and long time scales. Elucidation of the neural underpinnings of these mechanisms requires effective behavioral manipulations that can selectively engage them. We developed a novel cued sentence recall paradigm to assess the effects of two factors on sentence recall accuracy at short-term and long-term stages. Participants initially repeated auditory sentences immediately following a 14-s retention period. After this task was complete, long-term memory for each sentence was probed by a two-word recall cue. The sentences were either concrete (high imageability) or abstract (low imageability), and the initial 14-s retention period was filled with either an undemanding finger-tapping task or a more engaging articulatory suppression task (Exp. 1, counting backward by threes; Exp. 2, repeating a four-syllable nonword). Recall was always better for the concrete sentences. Articulatory suppression reduced accuracy in short-term recall, especially for abstract sentences, but the sentences initially recalled following articulatory suppression were retained better at the subsequent cued-recall test, suggesting that the engagement of semantic mechanisms for short-term retention promoted encoding of the sentence meaning into long-term memory. These results provide a basis for using sentence imageability and subsequent memory performance as probes of semantic engagement in short-term memory for sentences.

Long-term plasticity determines the postsynaptic response to correlated afferents with multivesicular short-term synaptic depression

Directory of Open Access Journals (Sweden)

Alexander David Bird

2014-01-01

Full Text Available Synchrony in a presynaptic population leads to correlations in vesicle occupancy at the active sites for neurotransmitter release. The number of independent release sites per presynaptic neuron, a synaptic parameter recently shown to be modifed during long-term plasticity, will modulate these correlations and therefore have a significant effect on the firing rate of the postsynaptic neuron. To understand how correlations from synaptic dynamics and from presynaptic synchrony shape the postsynaptic response, we study a model of multiple release site short-term plasticity and derive exact results for the crosscorrelation function of vesicle occupancy and neurotransmitter release, as well as the postsynaptic voltage variance. Using approximate forms for the postsynaptic firing rate in the limits of low and high correlations, we demonstrate that short-term depression leads to a maximum response for an intermediate number of presynaptic release sites, and that this leads to a tuning-curve response peaked at an optimal presynaptic synchrony setby the number of neurotransmitter release sites per presynaptic neuron. These effects arise because, above a certain level of correlation, activity in the presynaptic population is overly strong resulting in wastage of the pool of releasable neurotransmitter. As the nervous system operates under constraints of efficient metabolism it is likely that this phenomenon provides an activity-dependent constraint on network architecture.

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

NARCIS (Netherlands)

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

2017-01-01

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

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

Short-term and long-term sick-leave in Sweden

DEFF Research Database (Denmark)

Blank, N; Diderichsen, Finn

1995-01-01

The primary aim of the study was to analyse similarities and differences between repeated spells of short-term sick-leave (more than 3 spells of less than 7 days' duration in a 12-month period) and long-term absence through sickness (at least 1 spell of more than 59 days' duration in a 12-month p...

Ozone therapy in periodontics.

Science.gov (United States)

Gupta, G; Mansi, B

2012-02-22

Gingival and Periodontal diseases represent a major concern both in dentistry and medicine. The majority of the contributing factors and causes in the etiology of these diseases are reduced or treated with ozone in all its application forms (gas, water, oil). The beneficial biological effects of ozone, its anti-microbial activity, oxidation of bio-molecules precursors and microbial toxins implicated in periodontal diseases and its healing and tissue regeneration properties, make the use of ozone well indicated in all stages of gingival and periodontal diseases. The primary objective of this article is to provide a general review about the clinical applications of ozone in periodontics. The secondary objective is to summarize the available in vitro and in vivo studies in Periodontics in which ozone has been used. This objective would be of importance to future researchers in terms of what has been tried and what the potentials are for the clinical application of ozone in Periodontics.

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...

Minor effects of long-term ozone exposure on boreal peatland species Eriophorum vaginatum and Sphagnum papillosum

DEFF Research Database (Denmark)

Mörsky, SK; Haapala, JK; Rinnan, Riikka

2011-01-01

The effects of long-term ozone fumigation on two common peatland plant species, a sedge Eriophorum vaginatum L. and a moss Sphagnum papillosum Lindb., were studied applying peatland microcosms. The peat cores with intact vegetation were cored from an oligotrophic pine fen and partially embedded...

Tropospheric ozone using an emission tagging technique in the CAM-Chem and WRF-Chem models

Science.gov (United States)

Lupascu, A.; Coates, J.; Zhu, S.; Butler, T. M.

2017-12-01

Tropospheric ozone is a short-lived climate forcing pollutant. High concentration of ozone can affect human health (cardiorespiratory and increased mortality due to long-term exposure), and also it damages crops. Attributing ozone concentrations to the contributions from different sources would indicate the effects of locally emitted or transported precursors on ozone levels in specific regions. This information could be used as an important component of the design of emissions reduction strategies by indicating which emission sources could be targeted for effective reductions, thus reducing the burden of ozone pollution. Using a "tagging" approach within the CAM-Chem (global) and WRF-Chem (regional) models, we can quantify the contribution of individual emission of NOx and VOC precursors on air quality. Hence, when precursor emissions of NOx are tagged, we have seen that the largest contributors on ozone levels are the anthropogenic sources, while in the case of precursor emissions of VOCs, the biogenic sources and methane account for more than 50% of ozone levels. Further, we have extended the NOx tagging method in order to investigate continental source region contributions to concentrations of ozone over various receptor regions over the globe, with a zoom over Europe. In general, summertime maximum ozone in most receptor regions is largely attributable to local emissions of anthropogenic NOx and biogenic VOC. During the rest of the year, especially during springtime, ozone in most receptor regions shows stronger influences from anthropogenic emissions of NOx and VOC in remote source regions.

Musical and Verbal Memory in Alzheimer's Disease: A Study of Long-Term and Short-Term Memory

Science.gov (United States)

Menard, Marie-Claude; Belleville, Sylvie

2009-01-01

Musical memory was tested in Alzheimer patients and in healthy older adults using long-term and short-term memory tasks. Long-term memory (LTM) was tested with a recognition procedure using unfamiliar melodies. Short-term memory (STM) was evaluated with same/different judgment tasks on short series of notes. Musical memory was compared to verbal…

Trend prognosis of regional ozone maxima in 1994 using various meteorologic data: appendix

International Nuclear Information System (INIS)

Loibl, W.

1995-06-01

The purpose of this study was to develop and test a statistical method for the short-term forecast of ozone concentrations. Austrian ozone monitoring data from April to September 1994 are used to develop the forecast model. It builds upon a multiple linear regression model developed earlier which uses the temperature of the forecast day, and the ozone maxima of the previous day as variables. In this study temperature difference between previous and forecast day, and wind velocity of the forecast day were additionally taken into account. Furthermore wind direction dependent regression models were developed using subsamples of the data set devided into 8 wind direction classes. Different regression function parameters have to be applied for each of the 40 selected ozone monitoring sites to allow forecasting of regional ozone maxima throughout Austria. It was found that regression models with temperature difference and wind velocity as additional variables did not improve the results. Wind direction dependent regression models only slightly improved the results for some wind directions at several monitoring sites. Best forecast results in general were achieved by using the base regression model with the temperature of the forecast day and the ozone maxima of the previous day as variables. Ozone forecast maps were calculated by spatial interpolation of the forecasted ozone maxima of the monitoring sites. Forecast accuracy is within ± 10 ppb on 70-80 % of the observed days. Errors higher than ± 10 ppb occur mainly on days with ozone maxima of 80 ppb and more. (author)

Statistical evaluation of the impact of shale gas activities on ozone pollution in North Texas.

Science.gov (United States)

Ahmadi, Mahdi; John, Kuruvilla

2015-12-01

Over the past decade, substantial growth in shale gas exploration and production across the US has changed the country's energy outlook. Beyond its economic benefits, the negative impacts of shale gas development on air and water are less well known. In this study the relationship between shale gas activities and ground-level ozone pollution was statistically evaluated. The Dallas-Fort Worth (DFW) area in north-central Texas was selected as the study region. The Barnett Shale, which is one the most productive and fastest growing shale gas fields in the US, is located in the western half of DFW. Hourly meteorological and ozone data were acquired for fourteen years from monitoring stations established and operated by the Texas Commission on Environmental Quality (TCEQ). The area was divided into two regions, the shale gas region (SGR) and the non-shale gas (NSGR) region, according to the number of gas wells in close proximity to each monitoring site. The study period was also divided into 2000-2006 and 2007-2013 because the western half of DFW has experienced significant growth in shale gas activities since 2007. An evaluation of the raw ozone data showed that, while the overall trend in the ozone concentration was down over the entire region, the monitoring sites in the NSGR showed an additional reduction of 4% in the annual number of ozone exceedance days than those in the SGR. Directional analysis of ozone showed that the winds blowing from areas with high shale gas activities contributed to higher ozone downwind. KZ-filtering method and linear regression techniques were used to remove the effects of meteorological variations on ozone and to construct long-term and short-term meteorologically adjusted (M.A.) ozone time series. The mean value of all M.A. ozone components was 8% higher in the sites located within the SGR than in the NSGR. These findings may be useful for understanding the overall impact of shale gas activities on the local and regional ozone

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

Directory of Open Access Journals (Sweden)

Hazem eToutounji

2014-05-01

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

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)

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)

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...

Do Short-Term Managerial Objectives Lead to Under- or Over-Investment in Long-Term Projects

OpenAIRE

Lucian Arye Bebchuk; Lars A. Stole

1994-01-01

This paper studies managerial decisions about investment in long-run projects in the presence of imperfect information (the market knows less about such investments than the firm's managers) and short-term managerial objectives (the managers are concerned about the short-term stock price as well as the long-term stock price). Prior work has suggested that imperfect information and short-term managerial objectives induce managers to underinvest in long-run projects. We show that either underin...

Sleep Quality, Short-Term and Long-Term CPAP Adherence

Science.gov (United States)

Somiah, Manya; Taxin, Zachary; Keating, Joseph; Mooney, Anne M.; Norman, Robert G.; Rapoport, David M.; Ayappa, Indu

2012-01-01

Study Objectives: Adherence to CPAP therapy is low in patients with obstructive sleep apnea/hypopnea syndrome (OSAHS). The purpose of the present study was to evaluate the utility of measures of sleep architecture and sleep continuity on the CPAP titration study as predictors of both short- and long-term CPAP adherence. Methods: 93 patients with OSAHS (RDI 42.8 ± 34.3/h) underwent in-laboratory diagnostic polysomnography, CPAP titration, and follow-up polysomnography (NPSG) on CPAP. Adherence to CPAP was objectively monitored. Short-term (ST) CPAP adherence was averaged over 14 days immediately following the titration study. Long-term (LT) CPAP adherence was obtained in 56/93 patients after approximately 2 months of CPAP use. Patients were grouped into CPAP adherence groups for ST ( 4 h) and LT adherence ( 4 h). Sleep architecture, sleep disordered breathing (SDB) indices, and daytime outcome variables from the diagnostic and titration NPSGs were compared between CPAP adherence groups. Results: There was a significant relationship between ST and LT CPAP adherence (r = 0.81, p CPAP adherence groups had significantly lower %N2 and greater %REM on the titration NPSG. A model combining change in sleep efficiency and change in sleep continuity between the diagnostic and titration NPSGs predicted 17% of the variance in LT adherence (p = 0.006). Conclusions: These findings demonstrate that characteristics of sleep architecture, even on the titration NPSG, may predict some of the variance in CPAP adherence. Better sleep quality on the titration night was related to better CPAP adherence, suggesting that interventions to improve sleep on/prior to the CPAP titration study might be used as a therapeutic intervention to improve CPAP adherence. Citation: Somiah M; Taxin Z; Keating J; Mooney AM; Norman RG; Rapoport DM; Ayappa I. Sleep quality, short-term and long-term CPAP adherence. J Clin Sleep Med 2012;8(5):489-500. PMID:23066359

Assessing the associative deficit of older adults in long-term and short-term/working memory.

Science.gov (United States)

Chen, Tina; Naveh-Benjamin, Moshe

2012-09-01

Older adults exhibit a deficit in associative long-term memory relative to younger adults. However, the literature is inconclusive regarding whether this deficit is attenuated in short-term/working memory. To elucidate the issue, three experiments assessed younger and older adults' item and interitem associative memory and the effects of several variables that might potentially contribute to the inconsistent pattern of results in previous studies. In Experiment 1, participants were tested on item and associative recognition memory with both long-term and short-term retention intervals in a single, continuous recognition paradigm. There was an associative deficit for older adults in the short-term and long-term intervals. Using only short-term intervals, Experiment 2 utilized mixed and blocked test designs to examine the effect of test event salience. Blocking the test did not attenuate the age-related associative deficit seen in the mixed test blocks. Finally, an age-related associative deficit was found in Experiment 3, under both sequential and simultaneous presentation conditions. Even while accounting for some methodological issues, the associative deficit of older adults is evident in short-term/working memory.

A Lagrangian analysis of mid-latitude stratospheric ozone variability and long-term trends.

Science.gov (United States)

Koch, G.; Wernli, H.; Staehelin, J.; Peter, T.

2002-05-01

A systematic Lagrangian investigation is performed of wintertime high-resolution stratospheric ozone soundings at Payerne, Switzerland, from January 1970 to March 2001. For every ozone sounding, 10-day backward trajectories have been calculated on 16 isentropic levels using NCEP reanalysis data. Both the minimum/maximum latitude and potential vorticity (PV) averaged along the trajectories are used as indicators of the air parcels' ``origin''. The importance of transport for the understandin g of single ozone profiles is confirmed by a statistical analysis which shows that negative/positive ozone deviations gener ally coincide with transport from regions with climatologically low/high ozone values. The stable relationship between PV and ozone for the 32 year period indicates either no direct chemical impact or no temporal change of this impact. In the upper layer the PV-ozone relationship changes significantly after 1987 and a separate trend analysis for air masses transported from the polar, midlatitude and subtropical regions shows negative ozone trends in all three categories (with a maximum for the polar region). This is not direct evidence for, but would be in agreement with, an increased chemical ozone depletion in the Arctic since the late 1980s. The reasons for the negative trend in the mid-stratospheric air masses with subtropical origin that are in qualitative agreement with recent satellite observations are presently unknown.

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.

A short-term study of the state of surface water acidification at Semenyih dam

International Nuclear Information System (INIS)

Kantasamy, Nesamalar; Sumari, S.M.; Salam, S.M.; Riniswani Aziz

2007-01-01

A short-term study was done to analyze the state of acidification of surface water at Semenyih Dam. This study is part of a continuous monitoring programme for Malaysia as a participatory country of EANET (Acid Monitoring Network in East Asia). Surface water samples were taken at selected points of the dam from February to December 2005. Temperature, electrical conductivity, pH, alkalinity, acid neutralizing capacity (ANC) as well as concentration of specific ionic species were measured, determined and analysed in this study. Present available sort-term study data indicates Semenyih Dam surface water is currently not undergoing acidification. (author)

Ozone, Fine Particulate Matter, and Chronic Lower Respiratory Disease Mortality in the United States.

Science.gov (United States)

Hao, Yongping; Balluz, Lina; Strosnider, Heather; Wen, Xiao Jun; Li, Chaoyang; Qualters, Judith R

2015-08-01

Short-term effects of air pollution exposure on respiratory disease mortality are well established. However, few studies have examined the effects of long-term exposure, and among those that have, results are inconsistent. To evaluate long-term association between ambient ozone, fine particulate matter (PM2.5, particles with an aerodynamic diameter of 2.5 μm or less), and chronic lower respiratory disease (CLRD) mortality in the contiguous United States. We fit Bayesian hierarchical spatial Poisson models, adjusting for five county-level covariates (percentage of adults aged ≥65 years, poverty, lifetime smoking, obesity, and temperature), with random effects at state and county levels to account for spatial heterogeneity and spatial dependence. We derived county-level average daily concentration levels for ambient ozone and PM2.5 for 2001-2008 from the U.S. Environmental Protection Agency's down-scaled estimates and obtained 2007-2008 CLRD deaths from the National Center for Health Statistics. Exposure to ambient ozone was associated with an increased rate of CLRD deaths, with a rate ratio of 1.05 (95% credible interval, 1.01-1.09) per 5-ppb increase in ozone; the association between ambient PM2.5 and CLRD mortality was positive but statistically insignificant (rate ratio, 1.07; 95% credible interval, 0.99-1.14). This study links air pollution exposure data with CLRD mortality for all 3,109 contiguous U.S. counties. Ambient ozone may be associated with an increased rate of death from CLRD in the contiguous United States. Although we adjusted for selected county-level covariates and unobserved influences through Bayesian hierarchical spatial modeling, the possibility of ecologic bias remains.

Long-term trends of surface ozone and its influencing factors at the Mt. Waliguan GAW station, China - Part 1: Overall trends and characteristics

Science.gov (United States)

Xu, W. Y.; Lin, W. L.; Xu, X. B.; Tang, J.; Huang, J. Q.; Wu, H.; Zhang, X. C.

2015-11-01

Tropospheric ozone is an important atmospheric oxidant, greenhouse gas and atmospheric pollutant at the same time. The level of tropospheric ozone, particularly in the surface layer, is impacted by emissions of precursors and is subjected to meteorological conditions. Due its importance, the long-term variation trend of baseline ozone is highly needed for environmental and climate change assessment. So far, studies about the long-term trends of ozone at representative sites are mainly available for European and North American sites. Similar studies are lacking for China, a country with rapid economic growth for recent decades, and many other developing countries. To uncover the long-term characteristics and trends of baseline surface ozone, concentration in western China, measurements at a global baseline Global Atmospheric Watch (GAW) station in the north-eastern Tibetan Plateau region (Mt. Waliguan) for the period of 1994 to 2013 were analysed in this study, using a modified Mann-Kendall test and the Hilbert-Huang Transform analysis for the trend and periodicity analysis, respectively. Results reveal higher surface ozone during the night and lower during the day at Waliguan, due to mountain-valley breezes. A seasonal maximum in summer was found, which was probably caused by enhanced stratosphere-to-troposphere exchange events and/or by tropospheric photochemistry. Analysis suggests that there is a season-diurnal cycle in the three-dimensional winds on top of Mt. Waliguan. Season-dependent daytime and nighttime ranges of 6 h were determined based on the season-diurnal cycle in the three-dimensional winds and were used to sort subsets of ozone data for trend analysis. Significant increasing trends in surface ozone were detected for both daytime (1.5-2.7 ppbv 10 a-1) and nighttime (1.3-2.9 ppbv 10 a-1). Autumn and spring revealed the largest increase rates, while summer and winter showed relatively weaker increases. The HHT spectral analysis confirmed the increasing

Genetic deletion of melanin-concentrating hormone neurons impairs hippocampal short-term synaptic plasticity and hippocampal-dependent forms of short-term memory.

Science.gov (United States)

Le Barillier, Léa; Léger, Lucienne; Luppi, Pierre-Hervé; Fort, Patrice; Malleret, Gaël; Salin, Paul-Antoine

2015-11-01

The cognitive role of melanin-concentrating hormone (MCH) neurons, a neuronal population located in the mammalian postero-lateral hypothalamus sending projections to all cortical areas, remains poorly understood. Mainly activated during paradoxical sleep (PS), MCH neurons have been implicated in sleep regulation. The genetic deletion of the only known MCH receptor in rodent leads to an impairment of hippocampal dependent forms of memory and to an alteration of hippocampal long-term synaptic plasticity. By using MCH/ataxin3 mice, a genetic model characterized by a selective deletion of MCH neurons in the adult, we investigated the role of MCH neurons in hippocampal synaptic plasticity and hippocampal-dependent forms of memory. MCH/ataxin3 mice exhibited a deficit in the early part of both long-term potentiation and depression in the CA1 area of the hippocampus. Post-tetanic potentiation (PTP) was diminished while synaptic depression induced by repetitive stimulation was enhanced suggesting an alteration of pre-synaptic forms of short-term plasticity in these mice. Behaviorally, MCH/ataxin3 mice spent more time and showed a higher level of hesitation as compared to their controls in performing a short-term memory T-maze task, displayed retardation in acquiring a reference memory task in a Morris water maze, and showed a habituation deficit in an open field task. Deletion of MCH neurons could thus alter spatial short-term memory by impairing short-term plasticity in the hippocampus. Altogether, these findings could provide a cellular mechanism by which PS may facilitate memory encoding. Via MCH neuron activation, PS could prepare the day's learning by increasing and modulating short-term synaptic plasticity in the hippocampus. © 2015 Wiley Periodicals, Inc.

Relationship between short and long term radon measurements

International Nuclear Information System (INIS)

Martinez, T.; Ramirez, D.; Navarrete, M.; Cabrera, L.; Ramirez, A.; Gonzalez, P.

2000-01-01

In this work the radon group of the Faculty of Chemistry at the National University of Mexico presents the results obtained in the establishment of a relation between the short and long term radon measures made with passive electret detectors E-PERM type LLT and HST. The measures were carried out inside single family dwellings (open house condition) located in the southeast of Mexico City (in Xochimilco) during the four seasons of the year 1997. A correlation was established between the short term measures (five days) and those of a long term for every season as well as an annual average, with an equation that relates them. The objective and advantage of this correlation are that with a short term measure it is possible to predict the annual mean radon concentration, that represents a saving of human and economic resources. (author)

Analyzing Snowpack Metrics Over Large Spatial Extents Using Calibrated, Enhanced-Resolution Brightness Temperature Data and Long Short Term Memory Artificial Neural Networks

Science.gov (United States)

Norris, W.; J Q Farmer, C.

2017-12-01

Snow water equivalence (SWE) is a difficult metric to measure accurately over large spatial extents; snow-tell sites are too localized, and traditional remotely sensed brightness temperature data is at too coarse of a resolution to capture variation. The new Calibrated Enhanced-Resolution Brightness Temperature (CETB) data from the National Snow and Ice Data Center (NSIDC) offers remotely sensed brightness temperature data at an enhanced resolution of 3.125 km versus the original 25 km, which allows for large spatial extents to be analyzed with reduced uncertainty compared to the 25km product. While the 25km brightness temperature data has proved useful in past research — one group found decreasing trends in SWE outweighed increasing trends three to one in North America; other researchers used the data to incorporate winter conditions, like snow cover, into ecological zoning criterion — with the new 3.125 km data, it is possible to derive more accurate metrics for SWE, since we have far more spatial variability in measurements. Even with higher resolution data, using the 37 - 19 GHz frequencies to estimate SWE distorts the data during times of melt onset and accumulation onset. Past researchers employed statistical splines, while other successful attempts utilized non-parametric curve fitting to smooth out spikes distorting metrics. In this work, rather than using legacy curve fitting techniques, a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) was trained to perform curve fitting on the data. LSTM ANN have shown great promise in modeling time series data, and with almost 40 years of data available — 14,235 days — there is plenty of training data for the ANN. LSTM's are ideal for this type of time series analysis because they allow important trends to persist for long periods of time, but ignore short term fluctuations; since LSTM's have poor mid- to short-term memory, they are ideal for smoothing out the large spikes generated in the melt

Impact of short-term severe accident management actions in a long-term perspective. Final Report

International Nuclear Information System (INIS)

2000-03-01

The present systems for severe accident management are focused on mitigating the consequences of special severe accident phenomena and to reach a safe plant state. However, in the development of strategies and procedures for severe accident management, it is also important to consider the long-term perspective of accident management and especially to secure the safe state of the plant. The main reason for this is that certain short-term actions have an impact on the long-term scenario. Both positive and negative effects from short-term actions on the accident management in the long-term perspective have been included in this paper. Short-term actions are accident management measures taken within about 24 hours after the initiating event. The purpose of short-term actions is to reach a stable status of the plant. The main goal in the long-term perspective is to maintain the reactor in a stable state and prevent uncontrolled releases of activity. The purpose of this short Technical Note, deliberately limited in scope, is to draw attention to potential long-term problems, important to utilities and regulatory authorities, arising from the way a severe accident would be managed during the first hours. Its objective is to encourage discussions on the safest - and maybe also most economical - way to manage a severe accident in the long term by not making the situation worse through inappropriate short-term actions, and on the identification of short-term actions likely to make long-term management easier and safer. The Note is intended as a contribution to the knowledge base put at the disposal of Member countries through international collaboration. The scope of the work has been limited to a literature search. Useful further activities have been identified. However, there is no proposal, at this stage, for more detailed work to be undertaken under the auspices of the CSNI. Plant-specific applications would need to be developed by utilities

The long-term variability of atmospheric ozone from the 50-yr observations carried out at Belsk (51.84°N, 20.78°E, Poland

Directory of Open Access Journals (Sweden)

JANUSZ W. Krzyścin

2013-11-01

Full Text Available Total ozone (TO3 and ozone vertical profile (by the Umkehr method have been measured at Belsk (51.84°N, 20.78°E, Poland, since March 1963. The monthly mean data are analysed for the long-term changes in the period 1975–1996 and 1997–2012, that is, in the increasing and decreasing phases of the ozone-depleting substances (ODS concentration in the mid-altitude stratosphere over the NH mid-latitudes. Standard explanatory variables are selected for the ozone variability attribution to chemical and dynamical processes. A triad of regression models with various formulae for the trend term is examined to get a synergetic effect. The trend term could be: (1 proportional to ODS, (2 piecewise linear (with the turning points in 1975 – the trend onset and in 1997 – the trend overturning, (3 represented by any smooth curve fitted to the ozone time series having ‘natural variations’ removed. Confirming the results from previous studies on the midlatitudinal ozone, the analyses show a weakening of the TO3 trend and the statistically significant positive trend in the upper stratospheric region (33–43 km since 1997. The TO3 depletion in summer and autumn for the period 1997–2012 is found in the Umkehr data due to the ozone decrease in the lower and mid-stratosphere. A novel statistical-simulation-based test is proposed. It uses the bootstrap sample of the smooth trend pattern to calculate statistical significance of hypotheses for the trend variability. The test corroborates the results of the regression models and shows strengthening of the ozone negative trend in summer and autumn, disclosed in the Umkehr data, since about 2005.

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

OpenAIRE

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

2008-01-01

Capacity limits in linguistic short-term memory (STM) are typically measured with forward span tasks in which participants are asked to recall lists of words in the order presented. Using such tasks, native signers of American Sign Language (ASL) exhibit smaller spans than native speakers (Boutla, Supalla, Newport, & Bavelier, 2004). Here, we test the hypothesis that this population difference reflects differences in the way speakers and signers maintain temporal order information in short-te...

Short-term and long-term deflection of reinforced hollow core ...

African Journals Online (AJOL)

This paper presents a study on different methods of analysis that are currently used by design codes to predict the short-term and long-term deflection of reinforced concrete slab systems and compares the predicted deflections with measured deflections. The experimental work to measure deflections involved the testing of ...

Mice deficient for striatal Vesicular Acetylcholine Transporter (VAChT) display impaired short-term but normal long-term object recognition memory.

Science.gov (United States)

Palmer, Daniel; Creighton, Samantha; Prado, Vania F; Prado, Marco A M; Choleris, Elena; Winters, Boyer D

2016-09-15

Substantial evidence implicates Acetylcholine (ACh) in the acquisition of object memories. While most research has focused on the role of the cholinergic basal forebrain and its cortical targets, there are additional cholinergic networks that may contribute to object recognition. The striatum contains an independent cholinergic network comprised of interneurons. In the current study, we investigated the role of this cholinergic signalling in object recognition using mice deficient for Vesicular Acetylcholine Transporter (VAChT) within interneurons of the striatum. We tested whether these striatal VAChT(D2-Cre-flox/flox) mice would display normal short-term (5 or 15min retention delay) and long-term (3h retention delay) object recognition memory. In a home cage object recognition task, male and female VAChT(D2-Cre-flox/flox) mice were impaired selectively with a 15min retention delay. When tested on an object location task, VAChT(D2-Cre-flox/flox) mice displayed intact spatial memory. Finally, when object recognition was tested in a Y-shaped apparatus, designed to minimize the influence of spatial and contextual cues, only females displayed impaired recognition with a 5min retention delay, but when males were challenged with a 15min retention delay, they were also impaired; neither males nor females were impaired with the 3h delay. The pattern of results suggests that striatal cholinergic transmission plays a role in the short-term memory for object features, but not spatial location. Copyright © 2016 Elsevier B.V. All rights reserved.

Variation in Parasympathetic Dysregulation Moderates Short-term Memory Problems in Childhood Attention-Deficit/Hyperactivity Disorder.

Science.gov (United States)

Ward, Anthony R; Alarcón, Gabriela; Nigg, Joel T; Musser, Erica D

2015-11-01

Although attention deficit/hyperactivity disorder (ADHD) is associated with impairment in working memory and short-term memory, up to half of individual children with ADHD perform within a normative range. Heterogeneity in other ADHD-related mechanisms, which may compensate for or combine with cognitive weaknesses, is a likely explanation. One candidate is the robustness of parasympathetic regulation (as indexed by respiratory sinus arrhythmia; RSA). Theory and data suggest that a common neural network is likely tied to both heart-rate regulation and certain cognitive functions (including aspects of working and short-term memory). Cardiac-derived indices of parasympathetic reactivity were collected during short-term memory (STM) storage and rehearsal tasks from 243 children (116 ADHD, 127 controls). ADHD was associated with lower STM performance, replicating previous work. In addition, RSA reactivity moderated the association between STM and ADHD - both as a category and a dimension - independent of comorbidity. Specifically, conditional effects revealed that high levels of withdrawal interacted with weakened STM but high levels of augmentation moderated a positive association predicting ADHD. Thus, variations in parasympathetic reactivity may help explain neuropsychological heterogeneity in ADHD.

Merged SAGE II, Ozone_cci and OMPS ozone profile dataset and evaluation of ozone trends in the stratosphere

Directory of Open Access Journals (Sweden)

V. F. Sofieva

2017-10-01

Full Text Available In this paper, we present a merged dataset of ozone profiles from several satellite instruments: SAGE II on ERBS, GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, ACE-FTS on SCISAT, and OMPS on Suomi-NPP. The merged dataset is created in the framework of the European Space Agency Climate Change Initiative (Ozone_cci with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The datasets from the individual instruments have been extensively validated and intercompared; only those datasets which are in good agreement, and do not exhibit significant drifts with respect to collocated ground-based observations and with respect to each other, are used for merging. The long-term SAGE–CCI–OMPS dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The merged SAGE–CCI–OMPS dataset consists of deseasonalized anomalies of ozone in 10° latitude bands from 90° S to 90° N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to July 2016. This newly created dataset is used for evaluating ozone trends in the stratosphere through multiple linear regression. Negative ozone trends in the upper stratosphere are observed before 1997 and positive trends are found after 1997. The upper stratospheric trends are statistically significant at midlatitudes and indicate ozone recovery, as expected from the decrease of stratospheric halogens that started in the middle of the 1990s and stratospheric cooling.

A Two-Timescale Response to Ozone Depletion: Importance of the Background State

Science.gov (United States)

Seviour, W.; Waugh, D.; Gnanadesikan, A.

2015-12-01

It has been recently suggested that the response of Southern Ocean sea-ice extent to stratospheric ozone depletion is time-dependent; that the ocean surface initially cools due to enhanced northward Ekman drift caused by a poleward shift in the eddy-driven jet, and then warms after some time due to upwelling of warm waters from below the mixed layer. It is therefore possible that ozone depletion could act to favor a short-term increase in sea-ice extent. However, many uncertainties remain in understanding this mechanism, with different models showing widely differing time-scales and magnitudes of the response. Here, we analyze an ensemble of coupled model simulations with a step-function ozone perturbation. The two-timescale response is present with an approximately 30 year initial cooling period. The response is further shown to be highly dependent upon the background ocean temperature and salinity stratification, which is influenced by both natural internal variability and the isopycnal eddy mixing parameterization. It is suggested that the majority of inter-model differences in the Southern Ocean response to ozone depletion is caused by differences in stratification.

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...

Global tropospheric ozone modeling: Quantifying errors due to grid resolution

Science.gov (United States)

Wild, Oliver; Prather, Michael J.

2006-06-01

Ozone production in global chemical models is dependent on model resolution because ozone chemistry is inherently nonlinear, the timescales for chemical production are short, and precursors are artificially distributed over the spatial scale of the model grid. In this study we examine the sensitivity of ozone, its precursors, and its production to resolution by running a global chemical transport model at four different resolutions between T21 (5.6° × 5.6°) and T106 (1.1° × 1.1°) and by quantifying the errors in regional and global budgets. The sensitivity to vertical mixing through the parameterization of boundary layer turbulence is also examined. We find less ozone production in the boundary layer at higher resolution, consistent with slower chemical production in polluted emission regions and greater export of precursors. Agreement with ozonesonde and aircraft measurements made during the NASA TRACE-P campaign over the western Pacific in spring 2001 is consistently better at higher resolution. We demonstrate that the numerical errors in transport processes on a given resolution converge geometrically for a tracer at successively higher resolutions. The convergence in ozone production on progressing from T21 to T42, T63, and T106 resolution is likewise monotonic but indicates that there are still large errors at 120 km scales, suggesting that T106 resolution is too coarse to resolve regional ozone production. Diagnosing the ozone production and precursor transport that follow a short pulse of emissions over east Asia in springtime allows us to quantify the impacts of resolution on both regional and global ozone. Production close to continental emission regions is overestimated by 27% at T21 resolution, by 13% at T42 resolution, and by 5% at T106 resolution. However, subsequent ozone production in the free troposphere is not greatly affected. We find that the export of short-lived precursors such as NOx by convection is overestimated at coarse resolution.

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.

Impact of long-term and short-term therapies on seminal parameters

Directory of Open Access Journals (Sweden)

Jlenia Elia

2013-04-01

Full Text Available Aim: The aim of this work was: i to evaluate the prevalence of male partners of subfertile couples being treated with long/short term therapies for non andrological diseases; ii to study their seminal profile for the possible effects of their treatments on spermatogenesis and/or epididymal maturation. Methods: The study group was made up of 723 subjects, aged between 25 and 47 years. Semen analysis was performed according to World Health Organization (WHO guidelines (1999. The Superimposed Image Analysis System (SIAS, which is based on the computerized superimposition of spermatozoa images, was used to assess sperm motility parameters. Results: The prevalence of subjects taking pharmacological treatments was 22.7% (164/723. The prevalence was 3.7% (27/723 for the Short-Term Group and 18.9% (137/723 for the Long-Term Group. The subjects of each group were also subdivided into subgroups according to the treatments being received. Regarding the seminal profile, we did not observe a significant difference between the Long-Term, Short-Term or the Control Group. However, regarding the subgroups, we found a significant decrease in sperm number and progressive motility percentage in the subjects receiving treatment with antihypertensive drugs compared with the other subgroups and the Control Group. Conclusions: In the management of infertile couples, the potential negative impact on seminal parameters of any drugs being taken as Long-Term Therapy should be considered. The pathogenic mechanism needs to be clarified.

Understanding Long-Term Variations in Surface Ozone in United States (U.S. National Parks

Directory of Open Access Journals (Sweden)

Deborah McGlynn

2018-03-01

Full Text Available Long-term surface ozone observations at 25 National Park Service sites across the United States were analyzed for processes on varying time scales using a time scale decomposition technique, the Ensemble Empirical Mode Decomposition (EEMD. Time scales of interest include the seasonal cycle, large-scale climate oscillations, and long-term (>10 years trends. Emission reductions were found to have a greater impact on sites that are nearest major urban areas. Multidecadal trends in surface ozone were increasing at a rate of 0.07 to 0.37 ppbv year−1 before 2004 and decreasing at a rate of −0.08 to −0.60 ppbv year−1 after 2004 for sites in the East, Southern California, and Northwestern Washington. Sites in the Intermountain West did not experience a reversal of trends from positive to negative until the mid- to late 2000s. The magnitude of the annual amplitude (=annual maximum–minimum decreased at eight sites, two in the West, two in the Intermountain West, and four in the East, by 5–20 ppbv and significantly increased at three sites; one in Alaska, one in the West, and one in the Intermountain West, by 3–4 ppbv. Stronger decreases in the annual amplitude occurred at a greater proportion of sites in the East (4/6 sites than in the West/Intermountain West (4/19 sites. The date of annual maximums and/or minimums has changed at 12 sites, occurring 10–60 days earlier in the year. There appeared to be a link between the timing of the annual maximum and the decrease in the annual amplitude, which was hypothesized to be related to a decrease in ozone titration resulting from NOx emission reductions. Furthermore, it was found that a phase shift of the Pacific Decadal Oscillation (PDO, from positive to negative, in 1998–1999 resulted in increased occurrences of La Niña-like conditions. This shift had the effect of directing more polluted air masses from East Asia to higher latitudes over the North American continent. The change in the

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

TOLNet ozone lidar intercomparison during the discover-aq and frappé campaigns

Science.gov (United States)

Newchurch, Michael J.; Alvarez, Raul J.; Berkoff, Timothy A.; Carrion, William; DeYoung, Russell J.; Ganoe, Rene; Gronoff, Guillaume; Kirgis, Guillaume; Kuang, Shi; Langford, Andy O.; Leblanc, Thierry; McGee, Thomas J.; Pliutau, Denis; Senff, Christoph; Sullivan, John T.; Sumnicht, Grant; Twigg, Laurence W.; Wang, Lihua

2018-04-01

The Tropospheric Ozone Lidar Network (TOLNet) is a unique network of lidar systems that measure atmospheric profiles of ozone and aerosols, to contribute to air-quality studies, atmospheric modeling, and satellite validation efforts. The accurate characterization of these lidars is of critical interest, and is necessary to determine cross-instrument calibration uniformity. From July to August 2014, three lidars, the TROPospheric OZone (TROPOZ) lidar, the Tunable Optical Profiler for Aerosol and oZone (TOPAZ) lidar, and the Langley Mobile Ozone Lidar (LMOL), of TOLNet participated in the "Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality" (DISCOVER-AQ) mission and the "Front Range Air Pollution and Photochemistry Éxperiment" (FRAPPÉ) to measure sub-hourly ozone variations from near the surface to the top of the troposphere. Although large differences occur at few individual altitudes in the near field and far field range, the TOLNet lidars agree with each other within ±4%. These results indicate excellent measurement accuracy for the TOLNet lidars that is suitable for use in air-quality and ozone modeling efforts.

Prefrontocortical dopamine loss in rats delays long-term extinction of contextual conditioned fear, and reduces social interaction without affecting short-term social interaction memory.

Science.gov (United States)

Fernandez Espejo, Emilio

2003-03-01

Prefrontal dopamine loss delays extinction of cued fear conditioning responses, but its role in contextual fear conditioning has not been explored. Medial prefrontal lesions also enhance social interaction in rats, but the role of prefrontal dopamine loss on social interaction memory is not known. Besides, a role for subcortical accumbal dopamine on mnesic changes after prefrontal dopamine manipulation has been proposed but not explored. The objective was to study the involvement of dopaminergic neurotransmission in the medial prefrontal cortex (mPFC) and nucleus accumbens in two mnesic tasks: contextual fear conditioning and social interaction memory. For contextual fear conditioning, short- and long-term freezing responses after an electric shock were studied, as well as extinction retention. Regarding social interaction memory, the recognition of a juvenile, a very sensitive short-term memory test, was used. Dopamine loss was carried out by injection of 6-hydroxydopamine, and postmortem catecholamine levels were analyzed by high-performance liquid chromatography. Prefrontocortical dopamine loss (>76%) led to a reactive enhancement of accumbal dopamine content (ploss. In lesioned rats, long-term extinction of contextual fear conditioning was significantly delayed and extinction retention was impaired without changes in acquisition and short-term contextual fear conditioning and, on the other hand, acquisition and short-term social interaction memory were not affected, although time spent on social interaction was significantly reduced. Added dopamine loss in the nucleus accumbens (>76%) did not alter these behavioral changes. In summary, the results of the present study indicate that the dopaminergic network in the mPFC (but not in the nucleus accumbens) coordinates the normal long-term extinction of contextual fear conditioning responses without affecting their acquisition, and it is involved in time spent on social interaction, but not acquisition and short-term

Reconstruction of daily erythemal UV radiation values for the last century - The benefit of modelled ozone

Science.gov (United States)

Junk, J.; Feister, U.; Rozanov, E.; Krzyścin, J. W.

2013-05-01

Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long-term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVER time series. Therefore, we combined groundbased measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVER for the period from 1901 to 1999. Additionally, analyses of the long-term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVER provide a long-term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN-approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.

Short-term flow induced crystallization in isotactic polypropylene : how short is short?

NARCIS (Netherlands)

Ma, Z.; Balzano, L.; Portale, G.; Peters, G.W.M.

2013-01-01

The so-called "short-term flow" protocol is widely applied in experimental flow-induced crystallization studies on polymers in order to separate the nucleation and subsequent growth processes [Liedauer et al. Int. Polym. Proc. 1993, 8, 236–244]. The basis of this protocol is the assumption that

The impact of short-term UV irradiation on grains of sensitive and tolerant cereal genotypes studied by EPR.

Science.gov (United States)

Kurdziel, Magdalena; Filek, Maria; Łabanowska, Maria

2018-05-01

UV irradiation has ionisation character and leads to the generation of reactive oxygen species (ROS). The destructive character of ROS was observed among others during interaction of cereal grains with ozone and was caused by changes in structures of biomolecules leading to the formation of stable organic radicals. That effect was more evident for stress sensitive genotypes. In this study we investigated the influence of UV irradiation on cereal grains originating from genotypes with different tolerance to oxidative stress. Grains and their parts (endosperm, embryo and seed coat) of barley, wheat and oat were subjected to short-term UV irradiation. It was found that UV caused the appearance of various kinds of reactive species (O 2 -• , H 2 O 2 ) and stable radicals (semiquinone, phenoxyl and carbon-centred). Simultaneously, lipid peroxidation occurred and the organic structure of Mn(II) and Fe(III) complexes become disturbed. UV irradiation causes damage of main biochemical structures of plant tissues, the effect is more significant in sensitive genotypes. In comparison with ozone treatment, UV irradiation leads to stronger destruction of biomolecules in grains and their parts. It is caused by the high energy of UV light, facilitating easier breakage of molecular bonds in biochemical compounds. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Comparison of Short Term with Long Term Catheterization after Anterior Colporrhaphy Surgery

Directory of Open Access Journals (Sweden)

F. Movahed

2010-07-01

Full Text Available Introduction & Objective: This belief that overfilling the bladder after anterior colporrhaphy might have a negative influence on surgical outcome, causes routine catheterization after operation. This study was done to compare short term (24h with long term (72h catheterization after anterior colporrhaphy.Materials & Methods: This randomized clinical trial was carried out at Kosar Hospital , Qazvin (Iran in 2005-2006. One hundred cases candidating for anterior colporrhaphy , were divided in two equal groups . In the first group foley catheter was removed 24 hours and in the second group 72 hours after the operation. Before removing catheter, urine sample was obtained for culture . After removal and urination, residual volume was determinded. If the volume exceeded 200 ml or retention occured, the catheter would be fixed for more 72 hours. Need for recatheterization, urinary retention, positive urine culture,and hospital stay were surveyed. The data was analyzed using T and Fisher tests.Results: Residual volume exceeding 200 ml and the need for recatheterization occurred in one case (2% in the short term group but in the long term group none of the subjects needed recatheterization (P=1. Retention was not seen. In the both groups, one case (2% had positive urine culture with no statistically significant difference (P=1. Mean hospital stay was short in the first group (P=0.00.Conclusion: Short term catheterization after anterior colporrhaphy does not cause urinary retention and decreases hospital stay.

Nuclear war: short-term chemical and radiative effects of stratospheric injections

International Nuclear Information System (INIS)

Luther, F.M.

1983-10-01

Earlier investigations of the atmospheric effects of a nuclear war focused primarily on the potential reduction in stratospheric ozone. The numerical models used in those assessments were one-dimensional and calculated the average ozone reduction over the Northern Hemisphere. The results presented here are the first assessment of the potential reduction in total ozone on a subcontinental scale. The purpose is to determine whether regions of large ozone reduction (sometimes called ozone holes) are possible, and to identify the important parameters affecting the magnitude of the ozone reduction and rate of recovery

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)

Results of ozone measurements in Northern Germany: A case study

Science.gov (United States)

Schmidt, Manfred

1994-01-01

At most of the German ozone recording stations which have records over a sufficiently long period, the results of the summer months of 1989 showed the highest values since the beginning of the measurements. One of the reasons for this phenomenon was the high duration of sunshine in that summer; for example, in Potsdam near Berlin in May 1989 the sunshine duration was the highest in May since the beginning of the records in 1893. For that reason we selected this summer for a case study. The basis for the study was mainly the ozone measuring stations of the network of Lower Saxony and the Federal Office of Environment (Umweltbundesamt). The results of these summer measurements point to intense sources of ozone, probably in form of gaseous precursors, in the Middle German industrial areas near Leipzig and Halle and in Northwestern Czechoslovakia, with coal-mining, chemical and petrochemical industries, coking plants and others. The maps of average ozone concentrations, number or days with high ozone maxima, ozone-windroses of the stations, etc., suggest that these areas could be a main source of precursors and of photochemical ozone production in summer smog episodes in Central Europe. Stations on the North Sea coast, at which early ozone measurements were made by our institute in 1973/74 are compared with similarly located stations of the Lower Saxon network in 1989 and the results show a reversal of the ozone-windroses. In 1973/74, the highest ozone concentrations were correlated with wind directions from the sea while in 1989 these concentrations were correlated with directions from the continent. In the recent years, photochemical ozone production on the continent is probably predominant, while in former years the higher ozone content of the maritime subpolar air masses has been explained by stratospheric-tropospheric exchange.

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.

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

Long-term trends in stratospheric ozone, temperature, and water vapor over the Indian region

Science.gov (United States)

Thankamani Akhil Raj, Sivan; Venkat Ratnam, Madineni; Narayana Rao, Daggumati; Venkata Krishna Murthy, Boddam

2018-01-01

We have investigated the long-term trends in and variabilities of stratospheric ozone, water vapor and temperature over the Indian monsoon region using the long-term data constructed from multi-satellite (Upper Atmosphere Research Satellite (UARS MLS and HALOE, 1993-2005), Aura Microwave Limb Sounder (MLS, 2004-2015), Sounding of the Atmosphere using Broadband Emission Radiometry (SABER, 2002-2015) on board TIMED (Thermosphere Ionosphere Mesosphere Energetics Dynamics)) observations covering the period 1993-2015. We have selected two locations, namely, Trivandrum (8.4° N, 76.9° E) and New Delhi (28° N, 77° E), covering northern and southern parts of the Indian region. We also used observations from another station, Gadanki (13.5° N, 79.2° E), for comparison. A decreasing trend in ozone associated with NOx chemistry in the tropical middle stratosphere is found, and the trend turned to positive in the upper stratosphere. Temperature shows a cooling trend in the stratosphere, with a maximum around 37 km over Trivandrum (-1.71 ± 0.49 K decade-1) and New Delhi (-1.15 ± 0.55 K decade-1). The observed cooling trend in the stratosphere over Trivandrum and New Delhi is consistent with Gadanki lidar observations during 1998-2011. The water vapor shows a decreasing trend in the lower stratosphere and an increasing trend in the middle and upper stratosphere. A good correlation between N2O and O3 is found in the middle stratosphere (˜ 10 hPa) and poor correlation in the lower stratosphere. There is not much regional difference in the water vapor and temperature trends. However, upper stratospheric ozone trends over Trivandrum and New Delhi are different. The trend analysis carried out by varying the initial year has shown significant changes in the estimated trend.

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.

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

Differences in health status between long-term and short-term benzodiazepine users.

NARCIS (Netherlands)

Zandstra, S.M.; Furer, J.W.; Lisdonk, E.H. van de; Bor, J.H.J.; Zitman, F.G.; Weel, C. van

2002-01-01

BACKGROUND: Despite generally accepted advice to keep treatment short, benzodiazepines are often prescibed for more than six months. Prevention of long-term benzodiazepine use could be facilitated by the utilisation of risk indicators for long-term use. However, the characteristics of long-term

Implications of Wide-Area Geographic Diversity for Short- Term Variability of Solar Power

Energy Technology Data Exchange (ETDEWEB)

Mills, Andrew; Wiser, Ryan

2010-08-23

Worldwide interest in the deployment of photovoltaic generation (PV) is rapidly increasing. Operating experience with large PV plants, however, demonstrates that large, rapid changes in the output of PV plants are possible. Early studies of PV grid impacts suggested that short-term variability could be a potential limiting factor in deploying PV. Many of these early studies, however, lacked high-quality data from multiple sites to assess the costs and impacts of increasing PV penetration. As is well known for wind, accounting for the potential for geographic diversity can significantly reduce the magnitude of extreme changes in aggregated PV output, the resources required to accommodate that variability, and the potential costs of managing variability. We use measured 1-min solar insolation for 23 time-synchronized sites in the Southern Great Plains network of the Atmospheric Radiation Measurement program and wind speed data from 10 sites in the same network to characterize the variability of PV with different degrees of geographic diversity and to compare the variability of PV to the variability of similarly sited wind. The relative aggregate variability of PV plants sited in a dense 10 x 10 array with 20 km spacing is six times less than the variability of a single site for variability on time scales less than 15-min. We find in our analysis of wind and PV plants similarly sited in a 5 x 5 grid with 50 km spacing that the variability of PV is only slightly more than the variability of wind on time scales of 5-15 min. Over shorter and longer time scales the level of variability is nearly identical. Finally, we use a simple approximation method to estimate the cost of carrying additional reserves to manage sub-hourly variability. We conclude that the costs of managing the short-term variability of PV are dramatically reduced by geographic diversity and are not substantially different from the costs for managing the short-term variability of similarly sited wind in

The roles of long-term phonotactic and lexical prosodic knowledge in phonological short-term memory.

Science.gov (United States)

Tanida, Yuki; Ueno, Taiji; Lambon Ralph, Matthew A; Saito, Satoru

2015-04-01

Many previous studies have explored and confirmed the influence of long-term phonological representations on phonological short-term memory. In most investigations, phonological effects have been explored with respect to phonotactic constraints or frequency. If interaction between long-term memory and phonological short-term memory is a generalized principle, then other phonological characteristics-that is, suprasegmental aspects of phonology-should also exert similar effects on phonological short-term memory. We explored this hypothesis through three immediate serial-recall experiments that manipulated Japanese nonwords with respect to lexical prosody (pitch-accent type, reflecting suprasegmental characteristics) as well as phonotactic frequency (reflecting segmental characteristics). The results showed that phonotactic frequency affected the retention not only of the phonemic sequences, but also of pitch-accent patterns, when participants were instructed to recall both the phoneme sequence and accent pattern of nonwords. In addition, accent pattern typicality influenced the retention of the accent pattern: Typical accent patterns were recalled more accurately than atypical ones. These results indicate that both long-term phonotactic and lexical prosodic knowledge contribute to phonological short-term memory performance.

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...

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.

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.

The uranium industry: long-term planning for short-term competition

International Nuclear Information System (INIS)

Vottero, X.; Georges Capus, G.

2001-01-01

Long term planning for short term competition Today, uranium producers face new challenges in terms of both production (new regulatory, environmental and social constraints) and market conditions (new sources of uranium supply, very low prices and tough competition). In such a context, long-term planning is not just a prerequisite to survive in the nuclear fuel cycle industry. In fact, it also contributes to sustaining nuclear electricity generation facing fierce competition from other energy sources in increasingly deregulated markets. Firstly, the risk of investing in new mining projects in western countries is growing because, on the one hand, of very erratic market conditions and, on the other hand, of increasingly lengthy, complex and unpredictable regulatory conditions. Secondly, the supply of other sources of uranium (uranium derived from nuclear weapons, uranium produced in CIS countries, ...) involve other risks, mainly related to politics and commercial restrictions. Consequently, competitive uranium supply requires not only technical competence but also financial strength and good marketing capabilities in order to anticipate long-term market trends, in terms of both demand and supply. It also requires taking into account new parameters such as politics, environment, regulations, etc. Today, a supplier dedicated to the sustainable production of nuclear electricity must manage a broad range of long-term risks inherent to the procurement of uranium. Taking into account all these parameters in a context of short-term, fast-changing market is a great challenge for the future generation. World Uranium Civilian Supply and Demand. (authors)

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.

Insensitivity of visual short-term memory to irrelevant visual information.

Science.gov (United States)

Andrade, Jackie; Kemps, Eva; Werniers, Yves; May, Jon; Szmalec, Arnaud

2002-07-01

Several authors have hypothesized that visuo-spatial working memory is functionally analogous to verbal working memory. Irrelevant background speech impairs verbal short-term memory. We investigated whether irrelevant visual information has an analogous effect on visual short-term memory, using a dynamic visual noise (DVN) technique known to disrupt visual imagery (Quinn & McConnell, 1996b). Experiment I replicated the effect of DVN on pegword imagery. Experiments 2 and 3 showed no effect of DVN on recall of static matrix patterns, despite a significant effect of a concurrent spatial tapping task. Experiment 4 showed no effect of DVN on encoding or maintenance of arrays of matrix patterns, despite testing memory by a recognition procedure to encourage visual rather than spatial processing. Serial position curves showed a one-item recency effect typical of visual short-term memory. Experiment 5 showed no effect of DVN on short-term recognition of Chinese characters, despite effects of visual similarity and a concurrent colour memory task that confirmed visual processing of the characters. We conclude that irrelevant visual noise does not impair visual short-term memory. Visual working memory may not be functionally analogous to verbal working memory, and different cognitive processes may underlie visual short-term memory and visual imagery.

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

Science.gov (United States)

Nairne, James S

2002-01-01

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

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

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.

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...

A processing architecture for associative short-term memory in electronic noses

Science.gov (United States)

Pioggia, G.; Ferro, M.; Di Francesco, F.; DeRossi, D.

2006-11-01

Electronic nose (e-nose) architectures usually consist of several modules that process various tasks such as control, data acquisition, data filtering, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, and fuzzy rules used to implement such tasks may lead to issues concerning module interconnection and cooperation. Moreover, a new learning phase is mandatory once new measurements have been added to the dataset, thus causing changes in the previously derived model. Consequently, if a loss in the previous learning occurs (catastrophic interference), real-time applications of e-noses are limited. To overcome these problems this paper presents an architecture for dynamic and efficient management of multi-transducer data processing techniques and for saving an associative short-term memory of the previously learned model. The architecture implements an artificial model of a hippocampus-based working memory, enabling the system to be ready for real-time applications. Starting from the base models available in the architecture core, dedicated models for neurons, maps and connections were tailored to an artificial olfactory system devoted to analysing olive oil. In order to verify the ability of the processing architecture in associative and short-term memory, a paired-associate learning test was applied. The avoidance of catastrophic interference was observed.

The benefit of modeled ozone data for the reconstruction of a 99-year UV radiation time series

Science.gov (United States)

Junk, J.; Feister, U.; Helbig, A.; GöRgen, K.; Rozanov, E.; KrzyśCin, J. W.; Hoffmann, L.

2012-08-01

Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long-term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground-based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long-term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long-term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN-approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.

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.

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 (

Ozone exposure of a weed community produces adaptive changes in seed populations of Spergula arvensis.

Directory of Open Access Journals (Sweden)

Jennifer B Landesmann

Full Text Available Tropospheric ozone is one of the major drivers of global change. This stress factor alters plant growth and development. Ozone could act as a selection pressure on species communities composition, but also on population genetic background, thus affecting life history traits. Our objective was to evaluate the consequences of prolonged ozone exposure of a weed community on phenotypic traits of Spergulaarvensis linked to persistence. Specifically, we predicted that the selection pressure exerted by high ozone concentrations as well as the concomitant changes in the weed community would drive population adaptive changes which will be reflected on seed germination, dormancy and longevity. In order to test seed viability and dormancy level, we conducted germination experiments for which we used seeds produced by S. arvensis plants grown within a weed community exposed to three ozone treatments during four years (0, 90 and 120 ppb. We also performed a soil seed bank experiment to test seed longevity with seeds coming from both the four-year ozone exposure experiment and from a short-term treatment conducted at ambient and added ozone concentrations. We found that prolonged ozone exposure produced changes in seed germination, dormancy and longevity, resulting in three S. arvensis populations. Seeds from the 90 ppb ozone selection treatment had the highest level of germination when stored at 75% RH and 25 °C and then scarified. These seeds showed the lowest dormancy level when being subjected to 5 ºC/5% RH and 25 ºC/75% followed by 5% RH storage conditions. Furthermore, ozone exposure increased seed persistence in the soil through a maternal effect. Given that tropospheric ozone is an important pollutant in rural areas, changes in seed traits due to ozone exposure could increase weed persistence in fields, thus affecting weed-crop interactions, which could ultimately reduce crop production.

Ozone exposure of a weed community produces adaptive changes in seed populations of Spergula arvensis.

Science.gov (United States)

Landesmann, Jennifer B; Gundel, Pedro E; Martínez-Ghersa, M Alejandra; Ghersa, Claudio M

2013-01-01

Tropospheric ozone is one of the major drivers of global change. This stress factor alters plant growth and development. Ozone could act as a selection pressure on species communities composition, but also on population genetic background, thus affecting life history traits. Our objective was to evaluate the consequences of prolonged ozone exposure of a weed community on phenotypic traits of Spergulaarvensis linked to persistence. Specifically, we predicted that the selection pressure exerted by high ozone concentrations as well as the concomitant changes in the weed community would drive population adaptive changes which will be reflected on seed germination, dormancy and longevity. In order to test seed viability and dormancy level, we conducted germination experiments for which we used seeds produced by S. arvensis plants grown within a weed community exposed to three ozone treatments during four years (0, 90 and 120 ppb). We also performed a soil seed bank experiment to test seed longevity with seeds coming from both the four-year ozone exposure experiment and from a short-term treatment conducted at ambient and added ozone concentrations. We found that prolonged ozone exposure produced changes in seed germination, dormancy and longevity, resulting in three S. arvensis populations. Seeds from the 90 ppb ozone selection treatment had the highest level of germination when stored at 75% RH and 25 °C and then scarified. These seeds showed the lowest dormancy level when being subjected to 5 ºC/5% RH and 25 ºC/75% followed by 5% RH storage conditions. Furthermore, ozone exposure increased seed persistence in the soil through a maternal effect. Given that tropospheric ozone is an important pollutant in rural areas, changes in seed traits due to ozone exposure could increase weed persistence in fields, thus affecting weed-crop interactions, which could ultimately reduce crop production.

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...

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)

Quality assessment of the Ozone_cci Climate Research Data Package (release 2017 – Part 1: Ground-based validation of total ozone column data products

Directory of Open Access Journals (Sweden)

K. Garane

2018-03-01

Full Text Available The GOME-type Total Ozone Essential Climate Variable (GTO-ECV is a level-3 data record, which combines individual sensor products into one single cohesive record covering the 22-year period from 1995 to 2016, generated in the frame of the European Space Agency's Climate Change Initiative Phase II. It is based on level-2 total ozone data produced by the GODFIT (GOME-type Direct FITting v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/Metop-A and Metop-B observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate–chemistry modelling community for decadal stability long-term and short-term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC at both level 2 and level 3. The inter-sensor consistency of the individual level-2 data sets has mean differences generally within 0.5 % at moderate latitudes (±50°, whereas the level-3 data sets show mean differences with respect to the OMI reference data record that span between −0.2 ± 0.9 % (for GOME-2B and 1.0 ± 1.4 % (for SCIAMACHY. Very similar findings are reported for the level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments: the mean bias between GODFIT v4 satellite TOC and the ground instrument is well within 1.0 ± 1.0 % for all sensors, the drift per decade spans between −0.5 % and 1.0 ± 1.0 % depending on the sensor, and the peak-to-peak seasonality of the differences ranges from ∼ 1 % for GOME and OMI to  ∼ 2 % for SCIAMACHY. For the level-3 validation, our first goal was to show that the level-3 CRDP produces findings consistent with the level-2 individual sensor comparisons. We show a very good agreement with 0.5 to 2 % peak-to-peak amplitude for the monthly mean difference time series and a

Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia

Science.gov (United States)

Crabtree, Gregg W.; Gogos, Joseph A.

2014-01-01

Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations. PMID:25505409

Short-horizon regulation for long-term investors

NARCIS (Netherlands)

Shi, Z.; Werker, B.J.M.

2012-01-01

We study the effects of imposing repeated short-horizon regulatory constraints on long-term investors. We show that Value-at-Risk and Expected Shortfall constraints, when imposed dynamically, lead to similar optimal portfolios and wealth distributions. We also show that, in utility terms, the costs

Intensive video gaming improves encoding speed to visual short-term memory in young male adults.

Science.gov (United States)

Wilms, Inge L; Petersen, Anders; Vangkilde, Signe

2013-01-01

The purpose of this study was to measure the effect of action video gaming on central elements of visual attention using Bundesen's (1990) Theory of Visual Attention. To examine the cognitive impact of action video gaming, we tested basic functions of visual attention in 42 young male adults. Participants were divided into three groups depending on the amount of time spent playing action video games: non-players (15h/month, N=20). All participants were tested in three tasks which tap central functions of visual attention and short-term memory: a test based on the Theory of Visual Attention (TVA), an enumeration test and finally the Attentional Network Test (ANT). The results show that action video gaming does not seem to impact the capacity of visual short-term memory. However, playing action video games does seem to improve the encoding speed of visual information into visual short-term memory and the improvement does seem to depend on the time devoted to gaming. This suggests that intense action video gaming improves basic attentional functioning and that this improvement generalizes into other activities. The implications of these findings for cognitive rehabilitation training are discussed. Copyright © 2012 Elsevier B.V. All rights reserved.

Ambient Ozone Pollution and Daily Mortality: A Nationwide Study in 272 Chinese Cities.

Science.gov (United States)

Yin, Peng; Chen, Renjie; Wang, Lijun; Meng, Xia; Liu, Cong; Niu, Yue; Lin, Zhijing; Liu, Yunning; Liu, Jiangmei; Qi, Jinlei; You, Jinling; Zhou, Maigeng; Kan, Haidong

2017-11-21

Few large multicity studies have been conducted in developing countries to address the acute health effects of atmospheric ozone pollution. We explored the associations between ozone and daily cause-specific mortality in China. We performed a nationwide time-series analysis in 272 representative Chinese cities between 2013 and 2015. We used distributed lag models and over-dispersed generalized linear models to estimate the cumulative effects of ozone (lagged over 0-3 d) on mortality in each city, and we used hierarchical Bayesian models to combine the city-specific estimates. Regional, seasonal, and demographic heterogeneity were evaluated by meta-regression. At the national-average level, a 10-μg/m 3 increase in 8-h maximum ozone concentration was associated with 0.24% [95% posterior interval (PI): 0.13%, 0.35%], 0.27% (95% PI: 0.10%, 0.44%), 0.60% (95% PI: 0.08%, 1.11%), 0.24% (95% PI: 0.02%, 0.46%), and 0.29% (95% PI: 0.07%, 0.50%) higher daily mortality from all nonaccidental causes, cardiovascular diseases, hypertension, coronary diseases, and stroke, respectively. Associations between ozone and daily mortality due to respiratory and chronic obstructive pulmonary disease specifically were positive but imprecise and nonsignificant. There were no statistically significant differences in associations between ozone and nonaccidental mortality according to region, season, age, sex, or educational attainment. Our findings provide robust evidence of higher nonaccidental and cardiovascular mortality in association with short-term exposure to ambient ozone in China. https://doi.org/10.1289/EHP1849.

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.

The stimulation of hematosis on short-term and prolong irradiation

International Nuclear Information System (INIS)

Tukhtaev, T.M.

1978-01-01

This book studies the stimulation of hematosis on short-term and prolong irradiation, pathogenetic mechanisms of lesion and reconstruction of hematosis at critical radiation sickness, action hematosis stimulators in short-term irradiation conditions

Rapid effects of estrogens on short-term memory: Possible mechanisms.

Science.gov (United States)

Paletta, Pietro; Sheppard, Paul A S; Matta, Richard; Ervin, Kelsy S J; Choleris, Elena

2018-06-01

Estrogens affect learning and memory through rapid and delayed mechanisms. Here we review studies on rapid effects on short-term memory. Estradiol rapidly improves social and object recognition memory, spatial memory, and social learning when administered systemically. The dorsal hippocampus mediates estrogen rapid facilitation of object, social and spatial short-term memory. The medial amygdala mediates rapid facilitation of social recognition. The three estrogen receptors, α (ERα), β (ERβ) and the G-protein coupled estrogen receptor (GPER) appear to play different roles depending on the task and brain region. Both ERα and GPER agonists rapidly facilitate short-term social and object recognition and spatial memory when administered systemically or into the dorsal hippocampus and facilitate social recognition in the medial amygdala. Conversely, only GPER can facilitate social learning after systemic treatment and an ERβ agonist only rapidly improved short-term spatial memory when given systemically or into the hippocampus, but also facilitates social recognition in the medial amygdala. Investigations into the mechanisms behind estrogens' rapid effects on short term memory showed an involvement of the extracellular signal-regulated kinase (ERK) and the phosphoinositide 3-kinase (PI3K) kinase pathways. Recent evidence also showed that estrogens interact with the neuropeptide oxytocin in rapidly facilitating social recognition. Estrogens can increase the production and/or release of oxytocin and other neurotransmitters, such as dopamine and acetylcholine. Therefore, it is possible that estrogens' rapid effects on short-term memory may occur through the regulation of various neurotransmitters, although more research is need on these interactions as well as the mechanisms of estrogens' actions on short-term memory. Copyright © 2018 Elsevier Inc. All rights reserved.

PARAMETER EVALUATION AND MODEL VALIDATION OF OZONE EXPOSURE ASSESSMENT USING HARVARD SOUTHERN CALIFORNIA CHRONIC OZONE EXPOSURE STUDY DATA

Science.gov (United States)

To examine factors influencing long-term ozone exposures by children living in urban communities, we analyzed longitudinal data on personal, indoor, and outdoor ozone concentrations as well as related housing and other questionnaire information collected in the one-year-long Harv...

Combined Effects of Ozone and Drought on the Physiology and Membrane Lipids of Two Cowpea (Vigna unguiculata (L.) Walp) Cultivars.

Science.gov (United States)

Rebouças, Deborah Moura; De Sousa, Yuri Maia; Bagard, Matthieu; Costa, Jose Helio; Jolivet, Yves; De Melo, Dirce Fernandes; Repellin, Anne

2017-03-03

The interactive effects of drought and ozone on the physiology and leaf membrane lipid content, composition and metabolism of cowpea (Vigna unguiculata (L.) Walp.) were investigated in two cultivars (EPACE-1 and IT83-D) grown under controlled conditions. The drought treatment (three-week water deprivation) did not cause leaf injury but restricted growth through stomatal closure. In contrast, the short-term ozone treatment (130 ppb 12 h daily during 14 day) had a limited impact at the whole-plant level but caused leaf injury, hydrogen peroxide accumulation and galactolipid degradation. These effects were stronger in the IT83-D cultivar, which also showed specific ozone responses such as a higher digalactosyl-diacylglycerol (DGDG):monogalactosyldiacylglycerol (MGDG) ratio and the coordinated up-regulation of DGDG synthase (VuDGD2) and ω-3 fatty acid desaturase 8 (VuFAD8) genes, suggesting that membrane remodeling occurred under ozone stress in the sensitive cultivar. When stresses were combined, ozone did not modify the stomatal response to drought and the observed effects on whole-plant physiology were essentially the same as when drought was applied alone. Conversely, the drought-induced stomatal closure appeared to alleviate ozone effects through the reduction of ozone uptake.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Directory of Open Access Journals (Sweden)

Haiyang Yu

2017-06-01

Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

Science.gov (United States)

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-06-26

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Ozone production process in pulsed positive dielectric barrier discharge

Science.gov (United States)

Ono, Ryo; Oda, Tetsuji

2007-01-01

The ozone production process in a pulsed positive dielectric barrier discharge (DBD) is studied by measuring the spatial distribution of ozone density using a two-dimensional laser absorption method. DBD occurs in a 6 mm point-to-plane gap with a 1 mm-thick glass plate placed on the plane electrode. First, the propagation of DBD is observed using a short-gated ICCD camera. It is shown that DBD develops in three phases: primary streamer, secondary streamer and surface discharge phases. Next, the spatial distribution of ozone density is measured. It is shown that ozone is mostly produced in the secondary streamer and surface discharge, while only a small amount of ozone is produced in the primary streamer. The rate coefficient of the ozone production reaction, O + O2 + M → O3 + M, is estimated to be 2.5 × 10-34 cm6 s-1.

Ozone production process in pulsed positive dielectric barrier discharge

International Nuclear Information System (INIS)

Ono, Ryo; Oda, Tetsuji

2007-01-01

The ozone production process in a pulsed positive dielectric barrier discharge (DBD) is studied by measuring the spatial distribution of ozone density using a two-dimensional laser absorption method. DBD occurs in a 6 mm point-to-plane gap with a 1 mm-thick glass plate placed on the plane electrode. First, the propagation of DBD is observed using a short-gated ICCD camera. It is shown that DBD develops in three phases: primary streamer, secondary streamer and surface discharge phases. Next, the spatial distribution of ozone density is measured. It is shown that ozone is mostly produced in the secondary streamer and surface discharge, while only a small amount of ozone is produced in the primary streamer. The rate coefficient of the ozone production reaction, O + O 2 + M → O 3 + M, is estimated to be 2.5 x 10 -34 cm 6 s -1

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.

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

Directory of Open Access Journals (Sweden)

Maura Murru

2010-11-01

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

Category Specific Knowledge Modulate Capacity Limitations of Visual Short-Term Memory

DEFF Research Database (Denmark)

Dall, Jonas Olsen; Watanabe, Katsumi; Sørensen, Thomas Alrik

2016-01-01

We explore whether expertise can modulate the capacity of visual short-term memory, as some seem to argue that training affects capacity of short-term memory [13] while others are not able to find this modulation [12]. We extend on a previous study [3] demonstrating expertise effects by investiga...... are in line with the theoretical interpretation that visual short-term memory reflects the sum of the reverberating feedback loops to representations in long-term memory.......We explore whether expertise can modulate the capacity of visual short-term memory, as some seem to argue that training affects capacity of short-term memory [13] while others are not able to find this modulation [12]. We extend on a previous study [3] demonstrating expertise effects......), and expert observers (Japanese university students). For both the picture and the letter condition we find no performance difference in memory capacity, however, in the critical hiragana condition we demonstrate a systematic difference relating expertise differences between the groups. These results...

Solar dynamics influence on the atmospheric ozone

International Nuclear Information System (INIS)

Gogosheva, T.; Grigorieva, V.; Mendeva, B.; Krastev, D.; Petkov, B.

2007-01-01

A response of the atmospheric ozone to the solar dynamics has been studied using the total ozone content data, taken from the satellite experiments GOME on ERS-2 and TOMS-EP together with data obtained from the ground-based spectrophotometer Photon operating in Stara Zagora, Bulgaria during the period 1999-2005. We also use data from surface ozone observations performed in Sofia, Bulgaria. The solar activity was characterized by the sunspot daily numbers W, the solar radio flux at 10.7 cm (F10.7) and the MgII wing-to-core ratio solar index. The impact of the solar activity on the total ozone has been investigated analysing the ozone response to sharp changes of these parameters. Some of the examined cases showed a positive correlation between the ozone and the solar parameters, however, a negative correlation in other cases was found. There were some cases when the sharp increases of the solar activity did not provoke any ozone changes. The solar radiation changes during an eclipse can be considered a particular case of the solar dynamics as this event causes a sharp change of irradiance within a comparatively short time interval. The results of both - the total and surface ozone measurements carried out during the eclipses on 11 August 1999, 31 May 2003 and 29 March 2006 are presented. It was found that the atmospheric ozone behavior shows strong response to the fast solar radiation changes which take place during solar eclipse. (authors)

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.

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

Potentials of short term and long term cryopreserved sperm of the ...

African Journals Online (AJOL)

To service the growing demand for male African giant catfish (Clarias gariepinus) broodstock for aquaculture in Nigeria, and to conserve valuable genetic resources, we improved both short-term (in deep freezer at -35°C) and long-term cryopreservation (in liquid nitrogen at -296°C) of catfish sperm. Catfish sperm ...

Assessment of ozone impacts on vegetation in southern Africa and directions for future research

CSIR Research Space (South Africa)

Van Tienhoven, AM

2005-03-01

Full Text Available in the high ozone levels measured at the beginning of the southern African summer.17,23,24 The concentrations of ozone precursors, the complex production and removal pro- cesses, and the short lifespan of ozone, mean that ozone concentration in the atmosphere... jointoformextensiveareasofchlorosisas the leaf ages. Damage to foliage can be extensive enough to cause complete loss ofleafycropssuchaslettuceandchicory.39 Visible symptoms of ozone effects must be interpreted with caution, particularly in field studies where interactions...

Forecasting stock return volatility: A comparison between the roles of short-term and long-term leverage effects

Science.gov (United States)

Pan, Zhiyuan; Liu, Li

2018-02-01

In this paper, we extend the GARCH-MIDAS model proposed by Engle et al. (2013) to account for the leverage effect in short-term and long-term volatility components. Our in-sample evidence suggests that both short-term and long-term negative returns can cause higher future volatility than positive returns. Out-of-sample results show that the predictive ability of GARCH-MIDAS is significantly improved after taking the leverage effect into account. The leverage effect for short-term volatility component plays more important role than the leverage effect for long-term volatility component in affecting out-of-sample forecasting performance.

Ozone Production Using Pulsed Dielectric Barrier Discharge in Oxygen

OpenAIRE

Samaranayake, W. J. M.; Miyahara, Y.; Namihira, T.; Katsuki, S.; Hackam, R.; Akiyama, H.; ナミヒラ, タカオ; カツキ, スナオ; アキヤマ, ヒデノリ; 浪平, 隆男; 勝木, 淳; 秋山, 秀典

2000-01-01

The production of ozone was investigated using a dielectric barrier discharge in oxygen, and employing short-duration pulsed power. The dependence of the ozone concentration (parts per million, ppm) and ozone production yield (g(O3)/kWh) on the peak pulsed voltage (17.5 to 57.9 kV) and the pulse repetition rate (25 to 400 pulses/s, pps) were investigated. In the present study, the following parameters were kept constant: a pressure of 1.01×105 Pa, a temperature of 26±4°C a gas flow rate of 3....

Auditory short-term memory activation during score reading.

Science.gov (United States)

Simoens, Veerle L; Tervaniemi, Mari

2013-01-01

Performing music on the basis of reading a score requires reading ahead of what is being played in order to anticipate the necessary actions to produce the notes. Score reading thus not only involves the decoding of a visual score and the comparison to the auditory feedback, but also short-term storage of the musical information due to the delay of the auditory feedback during reading ahead. This study investigates the mechanisms of encoding of musical information in short-term memory during such a complicated procedure. There were three parts in this study. First, professional musicians participated in an electroencephalographic (EEG) experiment to study the slow wave potentials during a time interval of short-term memory storage in a situation that requires cross-modal translation and short-term storage of visual material to be compared with delayed auditory material, as it is the case in music score reading. This delayed visual-to-auditory matching task was compared with delayed visual-visual and auditory-auditory matching tasks in terms of EEG topography and voltage amplitudes. Second, an additional behavioural experiment was performed to determine which type of distractor would be the most interfering with the score reading-like task. Third, the self-reported strategies of the participants were also analyzed. All three parts of this study point towards the same conclusion according to which during music score reading, the musician most likely first translates the visual score into an auditory cue, probably starting around 700 or 1300 ms, ready for storage and delayed comparison with the auditory feedback.