The report investigates the short-term memory capacity of echo state recurrent neural networks. A quantitative measure MC of short-term memory capacity is introduced. The main result is that MC 5 N for networks with linear Output units and i.i.d. input, where N is network size. Conditions under which these maximal memory capacities are realized are described. Several theoretical and practical examples demonstrate how the short-term memory capacities of echo state networks can be exploited for...
Morris, R.J.T.; Wong, W.S.
Neural network memories with storage prescriptions based on Hebb's rule are known to collapse as more words are stored. By requiring that the most recently stored word be remembered precisely, a new simple short-term neutral network memory is obtained and its steady state capacity analyzed and simulated. Comparisons are drawn with Hopfield's method, the delta method of Widrow and Hoff, and the revised marginalist model of Mezard, Nadal, and Toulouse.
Dranias, Mark R; Ju, Han; Rajaram, Ezhilarasan; VanDongen, Antonius M J
Short-term memory refers to the ability to store small amounts of stimulus-specific information for a short period of time. It is supported by both fading and hidden memory processes. Fading memory relies on recurrent activity patterns in a neuronal network, whereas hidden memory is encoded using synaptic mechanisms, such as facilitation, which persist even when neurons fall silent. We have used a novel computational and optogenetic approach to investigate whether these same memory processes hypothesized to support pattern recognition and short-term memory in vivo, exist in vitro. Electrophysiological activity was recorded from primary cultures of dissociated rat cortical neurons plated on multielectrode arrays. Cultures were transfected with ChannelRhodopsin-2 and optically stimulated using random dot stimuli. The pattern of neuronal activity resulting from this stimulation was analyzed using classification algorithms that enabled the identification of stimulus-specific memories. Fading memories for different stimuli, encoded in ongoing neural activity, persisted and could be distinguished from each other for as long as 1 s after stimulation was terminated. Hidden memories were detected by altered responses of neurons to additional stimulation, and this effect persisted longer than 1 s. Interestingly, network bursts seem to eliminate hidden memories. These results are similar to those that have been reported from similar experiments in vivo and demonstrate that mechanisms of information processing and short-term memory can be studied using cultured neuronal networks, thereby setting the stage for therapeutic applications using this platform.
Colombo, Florian François
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...
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)
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.
Payne, Lisa; Kounios, John
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.
Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.
White, Olivia L.; Lee, Daniel D.; Sompolinsky, Haim
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.
White, Olivia L.; Lee, Daniel D.; Sompolinsky, Haim
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
Meyers, Gregory; Kapelan, Zoran; Keedwell, Edward
Water discolouration is an increasingly important and expensive issue due to rising customer expectations, tighter regulatory demands and ageing Water Distribution Systems (WDSs) in the UK and abroad. This paper presents a new turbidity forecasting methodology capable of aiding operational staff and enabling proactive management strategies. The turbidity forecasting methodology developed here is completely data-driven and does not require hydraulic or water quality network model that is expensive to build and maintain. The methodology is tested and verified on a real trunk main network with observed turbidity measurement data. Results obtained show that the methodology can detect if discolouration material is mobilised, estimate if sufficient turbidity will be generated to exceed a preselected threshold and approximate how long the material will take to reach the downstream meter. Classification based forecasts of turbidity can be reliably made up to 5 h ahead although at the expense of increased false alarm rates. The methodology presented here could be used as an early warning system that can enable a multitude of cost beneficial proactive management strategies to be implemented as an alternative to expensive trunk mains cleaning programs. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.
Stopfer, Mark; Laurent, Gilles
Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas.. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clock. Here we examine, using locusts, the changes in population dynamics of projection-neuron assemblies over repeated odour stimulations, as would occur when an animal first encounters and then repeatedly samples an odour for identification or localization. We find that the responses of these assemblies rapidly decrease in intensity, while they show a marked increase in spike time precision and inter-neuronal oscillatory coherence. Once established, this enhanced precision in the representation endures for several minutes. This change is stimulus-specific, and depends on events within the antennal lobe circuits, independent of olfactory receptor adaptation: it may thus constitute a form of sensory memory. Our results suggest that this progressive change in olfactory network dynamics serves to converge, over repeated odour samplings, on a more precise and readily classifiable odour representation, using relational information contained across neural assemblies.
Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; Mayo, Jackson R.; Armstrong, Robert C.
Here we present a characterization of short-term stability of random Boolean networks under arbitrary distributions of transfer functions. Given any distribution of transfer functions for a random Boolean network, we present a formula that decides whether short-term chaos (damage spreading) will happen. We provide a formal proof for this formula, and empirically show that its predictions are accurate. Previous work only works for special cases of balanced families. Finally, it has been observed that these characterizations fail for unbalanced families, yet such families are widespread in real biological networks.
Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes
The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes.
López, Erick; Allende, Héctor; Gil, Esteban
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...
Ji Meng; Bell, Michelle L; Cohan, Daniel S
Ozone is associated with health impacts including respiratory outcomes; however, results differ across studies. Meta-analysis is an increasingly important approach to synthesizing evidence across studies. We conducted meta-analysis of short-term ozone exposure and respiratory hospitalizations to evaluate variation across studies and explore some of the challenges in meta-analysis. We identified 136 estimates from 96 studies and investigated how estimates differed by age, ozone metric, season, lag, region, disease category, and hospitalization type. Overall results indicate associations between ozone and various kinds of respiratory hospitalizations; however, study characteristics affected risk estimates. Estimates were similar, but higher, for the elderly compared to all ages and for previous day exposure compared to same day exposure. Comparison across studies was hindered by variation in definitions of disease categories, as some (e.g., asthma) were identified through ≥ 3 different sets of ICD codes. Although not all analyses exhibited evidence of publication bias, adjustment for publication bias generally lowered overall estimates. Emergency hospitalizations for total respiratory disease increased by 4.47% (95% interval: 2.48, 6.50%) per 10 ppb 24 h ozone among the elderly without adjustment for publication bias and 2.97% (1.05, 4.94%) with adjustment. Comparison of multi-city study results and meta-analysis based on single-city studies further suggested publication bias.
Petersen, Anders; Kyllingsbæk, Søren; Hansen, Lars Kai
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...
Sturm, Bob; Santos, João Felipe; Korshunova, Iryna
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.
Ordozgoiti, Bruno; Gómez-Canaval, Sandra
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
Mozo, Alberto; Ordozgoiti, Bruno; Gómez-Canaval, Sandra
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.
Tremblay, M.; Guillaud, C.
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
Shlaer, Benjamin; Miller, Paul
Cells in recurrently connected neural networks exhibit bistability, which allows for stimulus information to persist in a circuit even after stimulus offset, i.e. short-term memory. However, such a system does not have enough hysteresis to encode temporal information about the stimuli. The biophysically described phenomenon of synaptic depression decreases synaptic transmission strengths due to increased presynaptic activity. This short-term reduction in synaptic strengths can destabilize attractor states in excitatory recurrent neural networks, causing the network to move along stimulus dependent dynamical trajectories. Such a network can successfully separate amplitudes and durations of stimuli from the number of successive stimuli. Stimulus number, duration and intensity encoding in randomly connected attractor networks with synaptic depression. Front. Comput. Neurosci. 7:59., and so provides a strong candidate network for the encoding of spatiotemporal information. Here we explicitly demonstrate the capability of a recurrent neural network with short-term synaptic depression to discriminate between the temporal sequences in which spatial stimuli are presented.
Yalcinoz, T.; Eminoglu, U.
Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey
Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng
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.
J. J. West
Full Text Available Observations and models demonstrate that ozone and its precursors can be transported between continents and across oceans. We model the influences of 10% reductions in anthropogenic nitrogen oxide (NOx emissions from each of nine world regions on surface ozone air quality in that region and all other regions. In doing so, we quantify the relative importance of long-range transport between all source-receptor pairs, for direct short-term ozone changes. We find that for population-weighted concentrations during the three-month "ozone-season", the strongest inter-regional influences are from Europe to the Former Soviet Union, East Asia to Southeast Asia, and Europe to Africa. The largest influences per unit of NOx reduced, however, are seen for source regions in the tropics and Southern Hemisphere, which we attribute mainly to greater sensitivity to changes in NOx in the lower troposphere, and secondarily to increased vertical convection to the free troposphere in tropical regions, allowing pollutants to be transported further. Results show, for example, that NOx reductions in North America are ~20% as effective per unit NOx in reducing ozone in Europe during summer, as NOx reductions from Europe itself. Reducing anthropogenic emissions of non-methane volatile organic compounds (NMVOCs and carbon monoxide (CO by 10% in selected regions, can have as large an impact on long-range ozone transport as NOx reductions, depending on the source region. We find that for many source-receptor pairs, the season of greatest long-range influence does not coincide with the season when ozone is highest in the receptor region. Reducing NOx emissions in most source regions causes a larger decrease in export of ozone from the source region than in ozone production outside of the source region.
Hiroshima, K.; Kohno, T.; Owada, H.; Hayashi, Y.
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
Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.
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.
Li, Xiangang; Wu, Xihong
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...
Whitfield, R.G; Biller, W.F.; Jusko, M.J.; Keisler, J.M.
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.
Di Persio, Luca; Honchar, Oleksandr
Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.
Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.
This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)
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)
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)
Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra
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.
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)
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)
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.
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.
Wang, Liuan; Guo, Xitong; Wu, Tianshi; Lv, Lucheng; Zhang, Zhiwei
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.
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.
Charles, Adam S; Yap, Han Lun; Rozell, Christopher J
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.
Ralf C. Staudemeyer
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.
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
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.
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.
Poirier, Marie; Saint-Aubin, Jean; Mair, Ali; Tehan, Gerry; Tolan, Anne
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.
Yu Daren; Huang Xin; Hu Qinghua; Zhou Rui; Wang Huaning; Cui Yanmei
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.
Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong
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.
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.
Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.
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)
Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe
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.
Rußwurm, M.; Körner, M.
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.
O'Brien, Michael J; Thibeault, Corey M; Srinivasa, Narayan
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.
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.
Ferreira, Arthur Amaral; Borges, Renato Alves; Paparini, Claudia; Ciraolo, Luigi; Radicella, Sandro M.
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
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.
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.
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.
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.
Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo M.; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco
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.
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.
Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
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.
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.
Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe
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
Wang, Jianyong; Zhang, Lei; Chen, Yuanyuan; Yi, Zhang
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.
Vo Ngoc Dieu; Ongsakul, Weerakorn
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.
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.
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.
Zazo, Ruben; Lozano-Diez, Alicia; Gonzalez-Dominguez, Javier; Toledano, Doroteo T; Gonzalez-Rodriguez, Joaquin
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.
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.
Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi
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 . firstname.lastname@example.org or email@example.com or firstname.lastname@example.org. Supplementary data is available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: email@example.com
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.
Min, Xu; Zeng, Wanwen; Chen, Ning; Chen, Ting; Jiang, Rui
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 . firstname.lastname@example.org or email@example.com. Supplementary materials are available at
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)
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.
Ormrod, D.P.; Landry, L.G.; Conklin, P.L.
The presence of UV-absorptive substances in the epidermal cells of leaves is thought to protect mesophyll tissues from the harmful effects of UV-B radiation. We examined the influence of short-term UV-B exposures on UV-absorptive (330 nm) sinapates and flavonols, and on shoot growth of the Arabidopsis wild type ecotype Landsberg erecta and two mutants. 114 deficient in chalcone synthase, and 115, deficient in chalcone/flavonone isomerase. Sequential ozone exposures were used to determine the effects of oxidative stress The levels of sinapates and flavonols on a leaf fresh weight basis increased substantially in the wild type and sinapates increased in the 114 mutant in vegetative vegetative/reproductive transitional and reproductive stage plants in response to short-term (48h) UV-B radiation. When UV-B was discontinued the levels generally decreased lo pre-exposure levels after 48 h in vegetative/reproductive but not in reproductive plants. Exposure to ozone before or alter UV-B treatment did not consistently affect the levels of these UV-absorptive compounds. Dry matter accumulation was less affected by UV-B at the vegetative and reproductive stages than at the vegetative/reproductive stage. At the vegetative/reproductive stage, shoot growth of all 3 genotypes was retarded by UV-B. Growth was not retarded by short-term ozone exposure alone but when exposure to ozone followed UV-B exposure, growth was reduced in all genotypes. Leaf cupping appeared on 115 plants exposed to UV-B
Smeets CJPP; Beck JP; LLO
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
Zhang, Hong-Yan; Sillar, Keith T
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 Â© 2012 Elsevier Ltd. All rights reserved.
Kopp, Franziska; Schröger, Erich; Lipka, Sigrid
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.
Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros
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.
Pride, Kerry R., E-mail: firstname.lastname@example.org [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)
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
Pride, Kerry R.; Peel, Jennifer L.; Robinson, Byron F.; Busacker, Ashley; Grandpre, Joseph; Bisgard, Kristine M.; Yip, Fuyuen Y.; Murphy, Tracy D.
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
Sun, Qingru; Gao, Xiangyun; Zhong, Weiqiong; Liu, Nairong
To examine the stability of the international oil trade network and explore the influence of countries and trade relationships on the trade stability, we construct weighted and unweighted international oil trade networks based on complex network theory using oil trading data between countries from 1996 to 2014. We analyze the stability of international oil trade network (IOTN) from short-term and long-term aspects. From the short-term perspective, we find that the trade volumes play an important role on the stability. Moreover, the weighted IOTN is stable; however, the unweighted networks can better reflect the actual evolution of IOTN. From the long-term perspective, we identify trade relationships that are maintained during the whole sample period to reveal the situation of the whole international oil trade. We provide a way to quantitatively measure the stability of complex network from short-term and long-term perspectives, which can be applied to measure and analyze trade stability of other goods or services.
Botvinick, Matthew M.; Plaut, David C.
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…
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.
Garcia-Valle, Rodrigo; P. Da Silva, Luiz C.; Xu, Zhao
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....
Dima, Danai; Friston, Karl J; Stephan, Klaas E; Frangou, Sophia
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.
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.
Popovic, D P; Stefanovic, M D [Nikola Tesla Inst., Belgrade (YU). Power System Dept.
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).
Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven
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…
Linn, W.S.; Fischer, D.A.; Medway, D.A.; Anzar, U.T.; Spier, C.E.; Valencia, L.M.; Venet, T.G.; Hackney, J.D.
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.
Hoppe, Tobias; Kiltz, Stefan; Dittmann, Jana
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.
Shevchuk, G. K.; Berg, D. B.; Zvereva, O. M.; Medvedeva, M. A.
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.
Madahar, Abhinav; Ma, Yuze; Patel, Kunal
Machine learning is increasingly prevalent in stock market trading. Though neural networks have seen success in computer vision and natural language processing, they have not been as useful in stock market trading. To demonstrate the applicability of a neural network in stock trading, we made a single-layer neural network that recommends buying or selling shares of a stock by comparing the highest high of 10 consecutive days with that of the next 10 days, a process repeated for the stock's ye...
Yu, Feng; Xu, Xiaozhong
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
Full Text Available The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.
Azadeh, A.; Asadzadeh, S.M.; Ghanbari, A.
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.
Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang
This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)
Zameer, Aneela; Arshad, Junaid; Khan, Asifullah; Raja, Muhammad Asif Zahoor
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
Full Text Available Unit commitment (UC is a non-linear, large scale, complex, mixed-integer combinatorial constrained optimization problem. This paper proposes, a new hybrid approach for generating unit commitment schedules using swarm intelligence learning rule based neural network. The training data has been generated using dynamic programming for machines without valve point effects and using genetic algorithm for machines with valve point effects. A set of load patterns as inputs and the corresponding unit generation schedules as outputs are used to train the network. The neural network fine tunes the best results to the desired targets. The proposed approach has been validated for three thermal machines with valve point effects and without valve point effects. The results are compared with the approaches available in the literature. The PSO-ANN trained model gives better results which show the promise of the proposed methodology.
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.
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.
Mahmoud, Thair S.; Habibi, Daryoush; Hassan, Mohammed Y.; Bass, Octavian
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.
Tsiouris, Κostas Μ; Pezoulas, Vasileios C; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I
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.
Hai, Zhou; Xiang, Zhu; Haijian, Shao; Ji, Wu
The operation of the power grid will be affected inevitably with the increasing scale of wind farm due to the inherent randomness and uncertainty, so the accurate wind speed forecasting is critical for the stability of the grid operation. Typically, the traditional forecasting method does not take into account the frequency characteristics of wind speed, which cannot reflect the nature of the wind speed signal changes result from the low generality ability of the model structure. AdaBoost neural network in combination with the multi-resolution and multi-scale decomposition of wind speed is proposed to design the model structure in order to improve the forecasting accuracy and generality ability. The experimental evaluation using the data from a real wind farm in Jiangsu province is given to demonstrate the proposed strategy can improve the robust and accuracy of the forecasted variable.
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
Peng, Katherine Y; Mathews, Paul M; Levy, Efrat; Wilson, Donald A
While apolipoprotein (Apo) E4 is linked to increased incidence of Alzheimer's disease (AD), there is growing evidence that it plays a role in functional brain irregularities that are independent of AD pathology. However, ApoE4-driven functional differences within olfactory processing regions have yet to be examined. Utilizing knock-in mice humanized to ApoE4 versus the more common ApoE3, we examined a simple olfactory perceptual memory that relies on the transfer of information from the olfactory bulb (OB) to the piriform cortex (PCX), the primary cortical region involved in higher order olfaction. In addition, we have recorded in vivo resting and odor-evoked local field potentials (LPF) from both brain regions and measured corresponding odor response magnitudes in anesthetized young (6-month-old) and middle-aged (12-month-old) ApoE mice. Young ApoE4 compared to ApoE3 mice exhibited a behavioral olfactory deficit coinciding with hyperactive odor-evoked response magnitudes within the OB that were not observed in older ApoE4 mice. Meanwhile, middle-aged ApoE4 compared to ApoE3 mice exhibited heightened response magnitudes in the PCX without a corresponding olfactory deficit, suggesting a shift with aging in ApoE4-driven effects from OB to PCX. Interestingly, the increased ApoE4-specific response in the PCX at middle-age was primarily due to a dampening of baseline spontaneous activity rather than an increase in evoked response power. Our findings indicate that early ApoE4-driven olfactory memory impairments and OB network abnormalities may be a precursor to later network dysfunction in the PCX, a region that not only is targeted early in AD, but may be selectively vulnerable to ApoE4 genotype. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.
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.
Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas
Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.
Siva S. Sivatha Sindhu; S. Geetha; M. Marikannan; A. Kannan
work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.
Li, Hongyu; Wu, Shaowei; Pan, Lu; Xu, Junhui; Shan, Jiao; Yang, Xuan; Dong, Wei; Deng, Furong; Chen, Yahong; Shima, Masayuki; Guo, Xinbiao
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
Michalka, Samantha W; Kong, Lingqiang; Rosen, Maya L; Shinn-Cunningham, Barbara G; Somers, David C
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.
Michalka, Samantha W.; Kong, Lingqiang; Rosen, Maya L.; Shinn-Cunningham, Barbara G.; Somers, David C.
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
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.
Jimeno Yepes, Antonio
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.
Makkeasorn, A.; Chang, N. B.; Zhou, X.
SummarySustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.
Stewart, Ian B.; Arendt, Dustin L.; Bell, Eric B.; Volkova, Svitlana
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.
Stewart, Ian; Arendt, Dustin; Bell, Eric; Volkova, Svitlana
Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. Such dynamics are especially notable during a period of crisis. This work addresses several important tasks of measuring, visualizing and predicting short term text representation shift, i.e. the change in a word's contextual semantics, and contrasting such shift with surface level word dynamics, or concept drift, observed in social media streams. ...
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.
Almonacid, F.; Pérez-Higueras, P.J.; Fernández, Eduardo F.; Hontoria, L.
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
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.
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.
Keller, T. Anderson; Sridhar, Sharath Nittur; Wang, Xin
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...
Gallego, C.; Costa, A.; Cuerva, A.
Since nowadays wind energy can't be neither scheduled nor large-scale storaged, wind power forecasting has been useful to minimize the impact of wind fluctuations. In particular, short-term forecasting (characterised by prediction horizons from minutes to a few days) is currently required by energy producers (in a daily electricity market context) and the TSO's (in order to keep the stability/balance of an electrical system). Within the short-term background, time-series based models (i.e., statistical models) have shown a better performance than NWP models for horizons up to few hours. These models try to learn and replicate the dynamic shown by the time series of a certain variable. When considering the power output of wind farms, ramp events are usually observed, being characterized by a large positive gradient in the time series (ramp-up) or negative (ramp-down) during relatively short time periods (few hours). Ramp events may be motivated by many different causes, involving generally several spatial scales, since the large scale (fronts, low pressure systems) up to the local scale (wind turbine shut-down due to high wind speed, yaw misalignment due to fast changes of wind direction). Hence, the output power may show unexpected dynamics during ramp events depending on the underlying processes; consequently, traditional statistical models considering only one dynamic for the hole power time series may be inappropriate. This work proposes a Regime Switching (RS) model based on Artificial Neural Nets (ANN). The RS-ANN model gathers as many ANN's as different dynamics considered (called regimes); a certain ANN is selected so as to predict the output power, depending on the current regime. The current regime is on-line updated based on a gradient criteria, regarding the past two values of the output power. 3 Regimes are established, concerning ramp events: ramp-up, ramp-down and no-ramp regime. In order to assess the skillness of the proposed RS-ANN model, a single
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.
Brielle M Paolini
Full Text Available Obesity is a public health crisis in North America. While lifestyle interventions for weight loss (WL remain popular, the rate of success is highly variable. Clearly, self-regulation of eating behavior is a challenge and patterns of activity across the brain may be an important determinant of success. The current study prospectively examined whether integration across the Hot-State Brain Network of Appetite (HBN-A predicts WL after 6-months of treatment in older adults. Our metric for network integration was global efficiency (GE. The present work is a sub-study (n = 56 of an ongoing randomized clinical trial involving WL. Imaging involved a baseline food-cue visualization functional MRI (fMRI scan following an overnight fast. Using graph theory to build functional brain networks, we demonstrated that regions of the HBN-A (insula, anterior cingulate cortex (ACC, superior temporal pole, amygdala and the parahippocampal gyrus were highly integrated as evidenced by the results of a principal component analysis. After accounting for known correlates of WL (baseline weight, age, sex, and self-regulatory efficacy and treatment condition, which together contributed 36.9% of the variance in WL, greater GE in the HBN-A was associated with an additional 19% of the variance. The ACC of the HBN-A was the primary driver of this effect, accounting for 14.5% of the variance in WL when entered in a stepwise regression following the covariates, p = 0.0001. The HBN-A is comprised of limbic regions important in the processing of emotions and visceral sensations and the ACC is key for translating such processing into behavioral consequences. The improved integration of these regions may enhance awareness of body and emotional states leading to more successful self-regulation and to greater WL. This is the first study among older adults to prospectively demonstrate that, following an overnight fast, GE of the HBN-A during a food visualization task is predictive of
Norris, W.; J Q Farmer, C.
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
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.
Hickok, Gregory; Rogalsky, Corianne; Chen, Rong; Herskovits, Edward H; Townsley, Sarah; Hillis, Argye E
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.
F.M. Weerman (Frank); P. Wilcox (Pamela); C.J. Sullivan (Christopher J.)
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
Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik
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.
Xu, Shiluo; Niu, Ruiqing
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
E. D. Sofen
Full Text Available Surface ozone observations with modern instrumentation have been made around the world for more than 40 years. Some of these observations have been made as one-off activities with short-term, specific science objectives and some have been made as part of wider networks which have provided a foundational infrastructure of data collection, calibration, quality control, and dissemination. These observations provide a fundamental underpinning to our understanding of tropospheric chemistry, air quality policy, atmosphere–biosphere interactions, etc. brought together eight of these networks to provide a single data set of surface ozone observations. We investigate how representative this combined data set is of global surface ozone using the output from a global atmospheric chemistry model. We estimate that on an area basis, 25 % of the globe is observed (34 % land, 21 % ocean. Whereas Europe and North America have almost complete coverage, other continents, Africa, South America, Australia, and Asia (12–17 % show significant gaps. Antarctica is surprisingly well observed (78 %. Little monitoring occurs over the oceans, with the tropical and southern oceans particularly poorly represented. The surface ozone over key biomes such as tropical forests and savanna is almost completely unmonitored. A chemical cluster analysis suggests that a significant number of observations are made of polluted air masses, but cleaner air masses whether over the land or ocean (especially again in the tropics are significantly under-observed. The current network is unlikely to see the impact of the El Niño–Southern Oscillation (ENSO but may be capable of detecting other planetary-scale signals. Model assessment and validation activities are hampered by a lack of observations in regions where the models differ substantially, as is the ability to monitor likely changes in surface ozone over the next century. Using our methodology we are able to suggest new
Sofen, E. D.; Bowdalo, D.; Evans, M. J.
Surface ozone observations with modern instrumentation have been made around the world for more than 40 years. Some of these observations have been made as one-off activities with short-term, specific science objectives and some have been made as part of wider networks which have provided a foundational infrastructure of data collection, calibration, quality control, and dissemination. These observations provide a fundamental underpinning to our understanding of tropospheric chemistry, air quality policy, atmosphere-biosphere interactions, etc. brought together eight of these networks to provide a single data set of surface ozone observations. We investigate how representative this combined data set is of global surface ozone using the output from a global atmospheric chemistry model. We estimate that on an area basis, 25 % of the globe is observed (34 % land, 21 % ocean). Whereas Europe and North America have almost complete coverage, other continents, Africa, South America, Australia, and Asia (12-17 %) show significant gaps. Antarctica is surprisingly well observed (78 %). Little monitoring occurs over the oceans, with the tropical and southern oceans particularly poorly represented. The surface ozone over key biomes such as tropical forests and savanna is almost completely unmonitored. A chemical cluster analysis suggests that a significant number of observations are made of polluted air masses, but cleaner air masses whether over the land or ocean (especially again in the tropics) are significantly under-observed. The current network is unlikely to see the impact of the El Niño-Southern Oscillation (ENSO) but may be capable of detecting other planetary-scale signals. Model assessment and validation activities are hampered by a lack of observations in regions where the models differ substantially, as is the ability to monitor likely changes in surface ozone over the next century. Using our methodology we are able to suggest new sites which would help to close
Full Text Available Daily land surface temperature (LST forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD coupled with Machine Learning (ML algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs and a single residue item. Then, the Partial Autocorrelation Function (PACF is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE, Mean Absolute Error (MAE, Mean Absolute Percentage Error (MAPE, Root Mean Square Error (RMSE, Pearson Correlation Coefficient (CC and Nash-Sutcliffe Coefficient of Efficiency (NSCE. To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN, LSTM and Empirical Mode Decomposition (EMD coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other
Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei
Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five
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 ...
Shah, Peter Jivan
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...
Thompson, Anne M.; Oltmans, Samuel J.; Tarasick, David W.; von der Gathen, Peter; Smit, Herman G. J.; Witte, Jacquelyn C.
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,
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.
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.
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.
Hall, Tim; LeBlanc, Troy; Ulman, Brian; McDonald, Aaron; Gramm, Paul; Chang, Li-Min; Keerthi, Suman; Kivlovitz, Dov; Hadlock, Jason
Onboard Short Term Plan Viewer (OSTPV) is a computer program for electronic display of mission plans and timelines, both aboard the International Space Station (ISS) and in ISS ground control stations located in several countries. OSTPV was specifically designed both (1) for use within the limited ISS computing environment and (2) to be compatible with computers used in ground control stations. OSTPV supplants a prior system in which, aboard the ISS, timelines were printed on paper and incorporated into files that also contained other paper documents. Hence, the introduction of OSTPV has both reduced the consumption of resources and saved time in updating plans and timelines. OSTPV accepts, as input, the mission timeline output of a legacy, print-oriented, UNIX-based program called "Consolidated Planning System" and converts the timeline information for display in an interactive, dynamic, Windows Web-based graphical user interface that is used by both the ISS crew and ground control teams in real time. OSTPV enables the ISS crew to electronically indicate execution of timeline steps, launch electronic procedures, and efficiently report to ground control teams on the statuses of ISS activities, all by use of laptop computers aboard the ISS.
Rolls, Edmund T.; Dempere-Marco, Laura; Deco, Gustavo
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
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.
Rolls, Edmund T; Dempere-Marco, Laura; Deco, Gustavo
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.
Brönnimann, S.; Brönnimann, S.; Farmer, S.
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
Jolicoeur, Pierre; Dell'Acqua, Roberto
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)
Seeley, Karen M.
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…
Friso, K.; Wismans, L. J.J.; Tijink, M. B.
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
Tillmann, Barbara; Lévêque, Yohana; Fornoni, Lesly; Albouy, Philippe; Caclin, Anne
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.
Silva, Marco; Sousa, Tiago; Morais, Hugo; Vale, Zita [Polytechnic of Porto (Portugal). GECAD - Knowledge Engineering and Decision Support Research Center
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.)
Gillary, Grant; Heydt, Rüdiger von der; Niebur, Ernst
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.
Irwin, David E
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.
Jurado, Sergio; Peralta, J.; Nebot, Àngela; Mugica, Francisco; Cortez, Paulo
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...
Johnson, Samuel; Marro, J.; Torres, Joaquin J.
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...
Biancofiore, Fabio; Verdecchia, Marco; Di Carlo, Piero; Tomassetti, Barbara; Aruffo, Eleonora; Busilacchio, Marcella; Bianco, Sebastiano; Di Tommaso, Sinibaldo; Colangeli, Carlo
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.
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...
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.
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
Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.
Heffernan, Rhys; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi
The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some 'short to intermediate' non-local interactions. Here, we employed Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks (BRNNs) which are capable of capturing long range interactions without using a window. We showed that the application of LSTM-BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long-range contacts (|i-j| >19) over a previous window-based, deep-learning method SPIDER2. Capturing long-range interactions allows the accuracy of three-state secondary structure prediction to reach 84% and the correlation coefficient between predicted and actual solvent accessible surface areas to reach 0.80, plus a reduction of 5%, 10%, 5% and 10% in the mean absolute error for backbone ϕ , ψ , θ and τ angles, respectively, from SPIDER2. More significantly, 27% of 182724 40-residue models directly constructed from predicted C α atom-based θ and τ have similar structures to their corresponding native structures (6Å RMSD or less), which is 3% better than models built by ϕ and ψ angles. We expect the method to be useful for assisting protein structure and function prediction. The method is available as a SPIDER3 server and standalone package at http://sparks-lab.org . email@example.com or firstname.lastname@example.org. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email
Wang, Wei; Zhang, Kai; Tang, Xiao-wei
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.
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.
Eldegard, Tom; Lund, Arne-Christian; Miltersen, Kristian; Rud, Linda
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)
Gheusi, F.; Chevalier, A.; Delmas, R.; Athier, G.; Bouchou, P.; Cousin, J.M.; Meyerfeld, Y.; Laj, P.; Sellegri, K.; Ancellet, G.
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)
Landberg, L; Joensen, A; Giebel, G [and others
This paper will giver a general overview of the results from a EU JOULE funded project (`Implementing short-term prediction at utilities`, JOR3-CT95-0008). Reference will be given to specialised papers where applicable. The goal of the project was to implement wind farm power output prediction systems in operational environments at a number of utilities in Europe. Two models were developed, one by Risoe and one by the Technical University of Denmark (DTU). Both prediction models used HIRLAM predictions from the Danish Meteorological Institute (DMI). (au) EFP-94; EU-JOULE. 11 refs.
Reeves, Adam; Lei, Quan
Does visual short-term memory (VSTM) depend on depth, as it might be if information was stored in more than one depth layer? Depth is critical in natural viewing and might be expected to affect retention, but whether this is so is currently unknown. Cued partial reports of letter arrays (Sperling, 1960) were measured up to 700 ms after display termination. Adding stereoscopic depth hardly affected VSTM capacity or decay inferred from total errors. The pattern of transposition errors (letters reported from an uncued row) was almost independent of depth and cue delay. We conclude that VSTM is effectively two-dimensional. Copyright © 2014 Elsevier Ltd. All rights reserved.
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
The Energy Information Administration (EIA) prepares The Short-Term Energy Outlook (energy supply, demand, and price projections) monthly for distribution on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. In addition, printed versions of the report are available to subscribers in January, April, July and October. The forecast period for this issue of the Outlook extends from July 1998 through December 1999. Values for second quarter of 1998 data, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the July 1998 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. 28 figs., 19 tabs.
The Energy Information Administration (EIA) prepares the Short-Term Energy Outlook (energy supply, demand, and price projections) monthly. The forecast period for this issue of the Outlook extends from January 1999 through December 2000. Data values for the fourth quarter 1998, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the January 1999 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 28 figs., 19 tabs.
Ensuring energy security has been at the centre of the IEA mission since its inception, following the oil crises of the early 1970s. While the security of oil supplies remains important, contemporary energy security policies must address all energy sources and cover a comprehensive range of natural, economic and political risks that affect energy sources, infrastructures and services. In response to this challenge, the IEA is currently developing a Model Of Short-term Energy Security (MOSES) to evaluate the energy security risks and resilience capacities of its member countries. The current version of MOSES covers short-term security of supply for primary energy sources and secondary fuels among IEA countries. It also lays the foundation for analysis of vulnerabilities of electricity and end-use energy sectors. MOSES contains a novel approach to analysing energy security, which can be used to identify energy security priorities, as a starting point for national energy security assessments and to track the evolution of a country's energy security profile. By grouping together countries with similar 'energy security profiles', MOSES depicts the energy security landscape of IEA countries. By extending the MOSES methodology to electricity security and energy services in the future, the IEA aims to develop a comprehensive policy-relevant perspective on global energy security. This Brochure provides and overview of the analysis and results. Readers interested in an in-depth discussion of methodology are referred to the MOSES Working Paper.
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.
Johnson, Samuel; Marro, J.; Torres, Joaquín J.
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
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.
Johnson, Samuel; Marro, J; Torres, Joaquín J
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.
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
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 ...
Heiselberg, Per; Perino, M.
The need to improve the energy efficiency of buildings requires new and more efficient ventilation systems. It has been demonstrated that innovative operating concepts that make use of natural ventilation seem to be more appreciated by occupants. Among the available ventilation strategies...... that are currently available, buoyancy driven, single-sided natural ventilation has proved to be very effective and can provide high air change rates for temperature and Indoor Air Quality (IAQ) control. However, to promote a wider distribution of these systems an improvement in the knowledge of their working...... airflow rate, ventilation efficiency, thermal comfort and dynamic temperature conditions. A suitable laboratory test rig was developed to perform extensive experimental analyses of the phenomenon under controlled and repeatable conditions. The results showed that short-term window airing is very effective...
Frees, E W
A new methodological approach to the forecasting of short-term trends in internal migration in the United States is introduced. "Panel-data (or longitudinal-data) models are used to represent the relationship between destination-specific out-migration and several explanatory variables. The introduction of this methodology into the migration literature is possible because of some new and improved databases developed by the U.S. Bureau of the Census.... Data from the Bureau of Economic Analysis are used to investigate the incorporation of exogenous factors as variables in the model." The exogenous factors considered include employment and unemployment, income, population size of state, and distance between states. The author concludes that "when one...includes additional parameters that are estimable in longitudinal-data models, it turns out that there is little additional information in the exogenous factors that is useful for forecasting." excerpt
The solar economics based on life cycle costs are refuted as both imaginary and irrelevant. It is argued that predicting rates of inflation and fuel escalation, expected life, maintenance costs, and legislation over the next ten to twenty years is pure guesswork. Furthermore, given the high mobility level of the U.S. population, the average consumer is skeptical of long run arguments which will pay returns only to the next owners. In the short term cost analysis, the house is sold prior to the end of the expected life of the system. The cash flow of the seller and buyer are considered. All the relevant factors, including the federal tax credit and the added value of the house because of the solar system are included.
Jääskeläinen, Iiro P; Ahveninen, Jyrki; Belliveau, John W; Raij, Tommi; Sams, Mikko
Converging lines of evidence suggest that auditory system short-term plasticity can enable several perceptual and cognitive functions that have been previously considered as relatively distinct phenomena. Here we review recent findings suggesting that auditory stimulation, auditory selective attention and cross-modal effects of visual stimulation each cause transient excitatory and (surround) inhibitory modulations in the auditory cortex. These modulations might adaptively tune hierarchically organized sound feature maps of the auditory cortex (e.g. tonotopy), thus filtering relevant sounds during rapidly changing environmental and task demands. This could support auditory sensory memory, pre-attentive detection of sound novelty, enhanced perception during selective attention, influence of visual processing on auditory perception and longer-term plastic changes associated with perceptual learning.
Barış ERGÜL; Arzu ALTIN YAVUZ; Ebru GÜNDOĞAN AŞIK
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 ...
Berman, Marc G.; Jonides, John; Lewis, Richard L.
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…
As announced at the meeting of the Standing Concertation Committee (SCC) on 26 June 2007 and in http://Bulletin No. 28/2007, the existing Saved Leave Scheme will be discontinued as of 31 December 2007. Staff participating in the Scheme will shortly receive a contract amendment stipulating the end of financial contributions compensated by save leave. Leave already accumulated on saved leave accounts can continue to be taken in accordance with the rules applicable to the current scheme. A new system of saved leave will enter into force on 1 January 2008 and will be the subject of a new implementation procedure entitled "Short-term saved leave scheme" dated 1 January 2008. At its meeting on 4 December 2007, the SCC agreed to recommend the Director-General to approve this procedure, which can be consulted on the HR Department’s website at the following address: https://cern.ch/hr-services/services-Ben/sls_shortterm.asp All staff wishing to participate in the new scheme a...
As announced at the meeting of the Standing Concertation Committee (SCC) on 26 June 2007 and in http://Bulletin No. 28/2007, the existing Saved Leave Scheme will be discontinued as of 31 December 2007. Staff participating in the Scheme will shortly receive a contract amendment stipulating the end of financial contributions compensated by save leave. Leave already accumulated on saved leave accounts can continue to be taken in accordance with the rules applicable to the current scheme. A new system of saved leave will enter into force on 1 January 2008 and will be the subject of a new im-plementation procedure entitled "Short-term saved leave scheme" dated 1 January 2008. At its meeting on 4 December 2007, the SCC agreed to recommend the Director-General to approve this procedure, which can be consulted on the HR Department’s website at the following address: https://cern.ch/hr-services/services-Ben/sls_shortterm.asp All staff wishing to participate in the new scheme ...
The forecast period for this issue of the Outlook extends from April 1999 through December 2000. Data values for the first quarter 1999, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the April 1999 version of the Short-Term Integrated forecasting system (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 25 figs., 19 tabs.
Wulder, Michael A.; White, Joanne C.; Masek, Jeffery G.; Dwyer, John L.; Roy, David P.
As of writing in mid-2010, both Landsat-5 and -7 continue to function, with sufficient fuel to enable data collection until the launch of the Landsat Data Continuity Mission (LDCM) scheduled for December of 2012. Failure of one or both of Landsat-5 or -7 may result in a lack of Landsat data for a period of time until the 2012 launch. Although the potential risk of a component failure increases the longer the sensor's design life is exceeded, the possible gap in Landsat data acquisition is reduced with each passing day and the risk of Landsat imagery being unavailable diminishes for all except a handful of applications that are particularly data demanding. Advances in Landsat data compositing and fusion are providing opportunities to address issues associated with Landsat-7 SLC-off imagery and to mitigate a potential acquisition gap through the integration of imagery from different sensors. The latter will likely also provide short-term, regional solutions to application-specific needs for the continuity of Landsat-like observations. Our goal in this communication is not to minimize the community's concerns regarding a gap in Landsat observations, but rather to clarify how the current situation has evolved and provide an up-to-date understanding of the circumstances, implications, and mitigation options related to a potential gap in the Landsat data record.
Chenthur Pandian, S.; Duraiswamy, K.; Kanagaraj, N. [Electrical and Electronics Engg., K.S. Rangasamy College of Technology, Tiruchengode 637209, Tamil Nadu (India); Christober Asir Rajan, C. [Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Pondicherry (India)
The main objective of short term load forecasting (STLF) is to provide load predictions for generation scheduling, economic load dispatch and security assessment at any time. The STLF is needed to supply necessary information for the system management of day-to-day operations and unit commitment. In this paper, the 'time' and 'temperature' of the day are taken as inputs for the fuzzy logic controller and the 'forecasted load' is the output. The input variable 'time' has been divided into eight triangular membership functions. The membership functions are Mid Night, Dawn, Morning, Fore Noon, After Noon, Evening, Dusk and Night. Another input variable 'temperature' has been divided into four triangular membership functions. They are Below Normal, Normal, Above Normal and High. The 'forecasted load' as output has been divided into eight triangular membership functions. They are Very Low, Low, Sub Normal, Moderate Normal, Normal, Above Normal, High and Very High. Case studies have been carried out for the Neyveli Thermal Power Station Unit-II (NTPS-II) in India. The fuzzy forecasted load values are compared with the conventional forecasted values. The forecasted load closely matches the actual one within +/-3%. (author)
Potocnik, P.; Govekar, E.; Grabec, I.
Energy forecasting requirements for Slovenia's natural gas market were investigated along with the cycles of natural gas consumption. This paper presented a short-term natural gas forecasting approach where the daily, weekly and yearly gas consumption were analyzed and the information obtained was incorporated into the forecasting model for hourly forecasting for the next day. The natural gas market depends on forecasting in order to optimize the leasing of storage capacities. As such, natural gas distribution companies have an economic incentive to accurately forecast their future gas consumption. The authors proposed a forecasting model with the following properties: two submodels for the winter and summer seasons; input variables including past consumption data, weather data, weather forecasts and basic cycle indexes; and, a hierarchical forecasting structure in which a daily model was used as the basis, with the hourly forecast obtained by modeling the relative daily profile. This proposed method was illustrated by a forecasting example for Slovenia's natural gas market. 11 refs., 11 figs
Kolstad, Henrik; Olsen, Jørn
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...
Chang, K. L.; Petropavlovskikh, I. V.; Cooper, O. R.; Schultz, M.; Wang, T.
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
Schwenk, W; Haase, O; Neudecker, J; Müller, J M
Colorectal resections are common surgical procedures all over the world. Laparoscopic colorectal surgery is technically feasible in a considerable amount of patients under elective conditions. Several short-term benefits of the laparoscopic approach to colorectal resection (less pain, less morbidity, improved reconvalescence and better quality of life) have been proposed. This review compares laparoscopic and conventional colorectal resection with regards to possible benefits of the laparoscopic method in the short-term postoperative period (up to 3 months post surgery). We searched MEDLINE, EMBASE, CancerLit, and the Cochrane Central Register of Controlled Trials for the years 1991 to 2004. We also handsearched the following journals from 1991 to 2004: British Journal of Surgery, Archives of Surgery, Annals of Surgery, Surgery, World Journal of Surgery, Disease of Colon and Rectum, Surgical Endoscopy, International Journal of Colorectal Disease, Langenbeck's Archives of Surgery, Der Chirurg, Zentralblatt für Chirurgie, Aktuelle Chirurgie/Viszeralchirurgie. Handsearch of abstracts from the following society meetings from 1991 to 2004: American College of Surgeons, American Society of Colorectal Surgeons, Royal Society of Surgeons, British Assocation of Coloproctology, Surgical Association of Endoscopic Surgeons, European Association of Endoscopic Surgeons, Asian Society of Endoscopic Surgeons. All randomised-controlled trial were included regardless of the language of publication. No- or pseudorandomised trials as well as studies that followed patient's preferences towards one of the two interventions were excluded, but listed separately. RCT presented as only an abstract were excluded. Results were extracted from papers by three observers independently on a predefined data sheet. Disagreements were solved by discussion. 'REVMAN 4.2' was used for statistical analysis. Mean differences (95% confidence intervals) were used for analysing continuous variables. If
Felder, Martin; Sehnke, Frank; Kaifel, Anton
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
Del Frate, F.; Iapaolo, M.; Casadio, S.; Godin-Beekmann, S.; Petitdidier, M.
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
Scott, Brian H; Mishkin, Mortimer
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.
Phillips, Brandon N; Chun, Dal W
To report an unusual case of hydroxychloroquine toxicity after short-term therapy. Observational case report. A 56-year-old woman presented to the Ophthalmology Clinic at Walter Reed Army Medical Center (WRAMC) with a 6-month history of gradually decreasing vision in both eyes. The patient had been taking hydroxychloroquine for the preceding 48 months for the treatment of rheumatoid arthritis. Examination of the posterior segment revealed bilateral "bull's eye" macular lesions. Fundus autofluorescence revealed hyperfluorescence of well-defined bull's eye lesions in both eyes. Optical coherence tomography revealed corresponding parafoveal atrophy with a loss of the retinal inner segment/outer segment junction. Humphrey visual field 10-2 white showed significant central and paracentral defects with a generalized depression. The patient was on a standard dose of 400 mg daily, which was above her ideal dose. The patient had no history of kidney or liver dysfunction. There were no known risk factors but there were several possible confounding factors. The patient was started on high-dose nabumetone, a nonsteroidal antiinflammatory drug, at the same time she was started on hydroxychloroquine. She also reported taking occasional ibuprofen. Retinal toxicity from chloroquine has been recognized for decades with later reports showing retinopathy from long-term hydroxychloroquine (Plaquenil) use for the treatment of antiinflammatory diseases. Hydroxychloroquine is now widely used and retinal toxicity is relatively uncommon. However, it can cause serious vision loss and is usually irreversible. The risk of hydroxychloroquine toxicity rises to nearly 1% with a total cumulative dose of 1,000 g, which is ∼5 years to 7 years of normal use. Toxicity is rare under this dose. For this reason, the American Academy of Ophthalmology has revised its recommendations such that annual screenings begin 5 years after therapy with hydroxychloroquine has begun unless there are known risk
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
Timothy J. Ricker
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...
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
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.
Minkina, Irene; Rosenberg, Samantha; Kalinyak-Fliszar, Michelene; Martin, Nadine
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.
Jonides, John; Lewis, Richard L.; Nee, Derek Evan; Lustig, Cindy A.; Berman, Marc G.; Moore, Katherine Sledge
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...
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.
Pontes, Jose C.A. de; Batista, Antonio M.; Viana, Ricardo L.; Lopes, Sergio R.
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
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
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
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 (
Cooper, Alvin; McCormack, WIlliam A.
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)
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.
Kleijnen, J.P.C.; Gaury, E.G.A.
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
Leopold, Thomas; Raab, Marcel
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…
Binamé, Florence; Poncelet, Martine
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…
Bowers, Jeffrey S.; Damian, Markus F.; Davis, Colin J.
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…
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.
Raine, Adrian; And Others
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)
SOARES, L. J.
Full Text Available This paper studies the hourly electricity load demand in the area covered by a utility situated in the Seattle, USA, called Puget Sound Power and Light Company. Our proposal is put into proof with the famous dataset from this company. We propose a stochastic model which employs ANN (Artificial Neural Networks to model short-run dynamics and the dependence among adjacent hours. The model proposed treats each hour's load separately as individual single series. This approach avoids modeling the intricate intra-day pattern (load profile displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is evaluated in similiar mode a TLSAR (Two-Level Seasonal Autoregressive model proposed by Soares (2003 using the years of 1995 and 1996 as the holdout sample. Moreover, we conclude that non linearity is present in some series of these data. The model results are analyzed. The experiment shows that our tool can be used to produce load forecasting in tropical climate places.
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.
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.
Matsuda, Hidetoshi; Kitamura, Nobuto; Kurokawa, Takayuki; Arakaki, Kazunobu; Gong, Jian Ping; Kanaya, Fuminori; Yasuda, Kazunori
A double-network (DN) gel, which is composed of poly(2-acrylamido-2-methylpropanesulfonic acid) and poly(N,N'-dimethyl acrylamide), can induce hyaline cartilage regeneration in vivo in a large osteochondral defect. The purpose of this study was to clarify the influence of the thickness of the implanted DN gel on the induction ability of hyaline cartilage regeneration. Thirty-eight mature rabbits were used in this study. We created an osteochondral defect having a diameter of 4.3-mm in the patellofemoral joint. The knees were randomly divided into 4 groups (Group I: 0.5-mm thick gel, Group II: 1.0-mm thick gel, Group III: 5.0-mm thick gel, and Group IV: untreated control). Animals in each group were further divided into 3 sub-groups depending on the gel implant position (2.0-, 3.0-, or 4.0-mm depth from the articular surface) in the defect. The regenerated tissues were evaluated with the Wayne's gross and histological grading scales and real time PCR analysis of the cartilage marker genes at 4 weeks. According to the total Wayne's score, when the depth of the final vacant space was set at 2.0 mm, the scores in Groups I, II, and III were significantly greater than that Group IV (phyaline cartilage regeneration as the 5.0-mm thick DN gel plug. However, the induction ability of the 0.5-mm thick sheet was significantly lower when compared with the 1.0-mm thick gel sheet. The 1.0-mm DN gel sheet is a promising device to establish a cell-free cartilage regeneration strategy that minimizes bone loss from the gel implantation.
Full Text Available Abstract Background A double-network (DN gel, which is composed of poly(2-acrylamido-2-methylpropanesulfonic acid and poly(N,N’-dimethyl acrylamide, can induce hyaline cartilage regeneration in vivo in a large osteochondral defect. The purpose of this study was to clarify the influence of the thickness of the implanted DN gel on the induction ability of hyaline cartilage regeneration. Methods Thirty-eight mature rabbits were used in this study. We created an osteochondral defect having a diameter of 4.3-mm in the patellofemoral joint. The knees were randomly divided into 4 groups (Group I: 0.5-mm thick gel, Group II: 1.0-mm thick gel, Group III: 5.0-mm thick gel, and Group IV: untreated control. Animals in each group were further divided into 3 sub-groups depending on the gel implant position (2.0-, 3.0-, or 4.0-mm depth from the articular surface in the defect. The regenerated tissues were evaluated with the Wayne’s gross and histological grading scales and real time PCR analysis of the cartilage marker genes at 4 weeks. Results According to the total Wayne’s score, when the depth of the final vacant space was set at 2.0 mm, the scores in Groups I, II, and III were significantly greater than that Group IV (p Conclusions The 1.0-mm thick DN gel sheet had the same ability to induce hyaline cartilage regeneration as the 5.0-mm thick DN gel plug. However, the induction ability of the 0.5-mm thick sheet was significantly lower when compared with the 1.0-mm thick gel sheet. The 1.0-mm DN gel sheet is a promising device to establish a cell-free cartilage regeneration strategy that minimizes bone loss from the gel implantation.
Joseph Valentino Raimondo
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.
Codo de Oliveira, M.; Rico-Ramirez, M. A.
Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.
Kristina M Visscher
Full Text Available Are the information processing steps that support short-term sensory memory common to all the senses? Systematic, psychophysical comparison requires identical experimental paradigms and comparable stimuli, which can be challenging to obtain across modalities. Participants performed a recognition memory task with auditory and visual stimuli that were comparable in complexity and in their neural representations at early stages of cortical processing. The visual stimuli were static and moving Gaussian-windowed, oriented, sinusoidal gratings (Gabor patches; the auditory stimuli were broadband sounds whose frequency content varied sinusoidally over time (moving ripples. Parallel effects on recognition memory were seen for number of items to be remembered, retention interval, and serial position. Further, regardless of modality, predicting an item's recognizability requires taking account of (1 the probe's similarity to the remembered list items (summed similarity, and (2 the similarity between the items in memory (inter-item homogeneity. A model incorporating both these factors gives a good fit to recognition memory data for auditory as well as visual stimuli. In addition, we present the first demonstration of the orthogonality of summed similarity and inter-item homogeneity effects. These data imply that auditory and visual representations undergo very similar transformations while they are encoded and retrieved from memory.
Visscher, Kristina M; Kaplan, Elina; Kahana, Michael J; Sekuler, Robert
Are the information processing steps that support short-term sensory memory common to all the senses? Systematic, psychophysical comparison requires identical experimental paradigms and comparable stimuli, which can be challenging to obtain across modalities. Participants performed a recognition memory task with auditory and visual stimuli that were comparable in complexity and in their neural representations at early stages of cortical processing. The visual stimuli were static and moving Gaussian-windowed, oriented, sinusoidal gratings (Gabor patches); the auditory stimuli were broadband sounds whose frequency content varied sinusoidally over time (moving ripples). Parallel effects on recognition memory were seen for number of items to be remembered, retention interval, and serial position. Further, regardless of modality, predicting an item's recognizability requires taking account of (1) the probe's similarity to the remembered list items (summed similarity), and (2) the similarity between the items in memory (inter-item homogeneity). A model incorporating both these factors gives a good fit to recognition memory data for auditory as well as visual stimuli. In addition, we present the first demonstration of the orthogonality of summed similarity and inter-item homogeneity effects. These data imply that auditory and visual representations undergo very similar transformations while they are encoded and retrieved from memory.
PMD), has become a growing public health concern, as it may potentially result in the development of hearing difficulties. Objectives: The aim of the study was to determine the differential impact and short-term effects of simultaneous ...
Short-term treatment outcomes of children starting antiretroviral therapy in the intensive care unit, general medical wards and outpatient HIV clinics at Red Cross War Memorial Children's Hospital, Cape Town, South Africa: A retrospective cohort study.
Whitlow, Jesse William, Jr.
The present research evaluated the refractorylike response decrement, as found in habituation of auditory evoked peripheral vasoconstriction in rabbits, to determine whether or not it represents a short-term habituation process distinct from effector fatigue or sensory adaptation. (Editor)
Short-term outcome of patients with closed comminuted femoral shaft fracture treated with locking intramedullary sign nail at Muhimbili Orthopaedic Institute in Tanzania. Billy T. Haonga, Felix S. Mrita, Edmundo E. Ndalama, Jackline E. Makupa ...
Bhosle, N.B.; Rokade, M.A.; Zingde, M.D.
The particulate matter (PM) collected from Mahi River Estuary was analysed for organic carbon (POC), nitrogen (PON), and chlorophyll a (Chl a). The concentration of PM, POC, PON and Chl a showed short term variations. Average surface concentration...
Zhu, Xinxin; Genton, Marc G.
some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss
Memory and forgetting are inextricably intertwined. Any account of short-term memory (STM) should address the following question: If three, four, or five chunks are being held in STM, what happens after attention is diverted?
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.
Memory over time scales of seconds to tens of seconds is thought to be maintained by neural activity that is triggered by a memorized stimulus and persists long after the stimulus is turned off. This presents a challenge to current models of memory-storing mechanisms, because the typical time scales associated with cellular and synaptic dynamics are two orders of magnitude smaller than this. While such long time scales can easily be achieved by bistable processes that toggle like a flip-flop between a baseline and elevated-activity state, many neuronal systems have been observed experimentally to be capable of maintaining a continuum of stable states. For example, in neural integrator networks involved in the accumulation of evidence for decision making and in motor control, individual neurons have been recorded whose activity reflects the mathematical integral of their inputs; in the absence of input, these neurons sustain activity at a level proportional to the running total of their inputs. This represents an analog form of memory whose dynamics can be conceptualized through an energy landscape with a continuum of lowest-energy states. Such continuous attractor landscapes are structurally non-robust, in seeming violation of the relative robustness of biological memory systems. In this talk, I will present and compare different biologically motivated circuit motifs for the accumulation and storage of signals in short-term memory. Challenges to generating robust memory maintenance will be highlighted and potential mechanisms for ameliorating the sensitivity of memory networks to perturbations will be discussed. Funding for this work was provided by NIH R01 MH065034, NSF IIS-1208218, Simons Foundation 324260, and a UC Davis Ophthalmology Research to Prevent Blindness Grant.
Dupont, Benjamin; De Jonghe, Cedric; Kessels, Kris; Belmans, Ronnie
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...
Imanaka, Kuniyasu; Funase, Kozo; Yamauchi, Masaki
We examined in this review article the behavioural and conceptual models of motor control and short-term memory which have intensively been investigated since the 1970s. First, we reviewed both the dual-storage model of short-term memory in which movement information is stored and a typical model of motor control which emphasizes the importance of efferent factors. We then examined two models of preselection effects: a cognitive model and a cognitive/ efferent model. Following this we reviewe...
DeCamp, Matthew; Rodriguez, Joce; Hecht, Shelby; Barry, Michele; Sugarman, Jeremy
Background Interest in short-term global health training and service programs continues to grow, yet they can be associated with a variety of ethical issues for which trainees or others with limited global health experience may not be prepared to address. Therefore, there is a clear need for educational interventions concerning these ethical issues. Methods We developed and evaluated an introductory curriculum, ?Ethical Challenges in Short-term Global Health Training.? The curriculum was deve...
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.
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
Feurra, Matteo; Galli, Giulia; Pavone, Enea Francesco; Rossi, Alessandro; Rossi, Simone
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.
Maljkovic, Vera; Martini, Paolo
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...
Schmitt, David P.; Alcalay, Lidia; Allik, Jüri
Previous studies have documented links between sub-clinical narcissism and the active pursuit of short-term mating strategies (e.g., unrestricted sociosexuality, marital infidelity, mate poaching). Nearly all of these investigations have relied solely on samples from Western cultures. In the curr...... limitations of these cross-culturally universal findings and presents suggestions for future research into revealing the precise psychological features of narcissism that facilitate the strategic pursuit of short-term mating....
Regan, J. E.
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.
Deprez, Sabine; Vandenbulcke, Mathieu; Peeters, Ron; Emsell, Louise; Amant, Frederic; Sunaert, Stefan
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.
Stopfer, Mark; Laurent, Gilles
Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clo...
In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.
Ruey-Hsun Liang; Ching-Chi Cheng [National Yunlin University of Science and Technology (China). Dept. of Electrical Engineering
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)
Lyons, Lisa C; Roman, Gregg
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.
Sauseng, Paul; Klimesch, Wolfgang; Heise, Kirstin F; Gruber, Walter R; Holz, Elisa; Karim, Ahmed A; Glennon, Mark; Gerloff, Christian; Birbaumer, Niels; Hummel, Friedhelm C
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.
Potagas, Constantin; Kasselimis, Dimitrios; Evdokimidis, Ioannis
The aim of the present study is to investigate short-term memory and working memory deficits in aphasics in relation to the severity of their language impairment. Fifty-eight aphasic patients participated in this study. Based on language assessment, an aphasia score was calculated for each patient. Memory was assessed in two modalities, verbal and spatial. Mean scores for all memory tasks were lower than normal. Aphasia score was significantly correlated with performance on all memory tasks. Correlation coefficients for short-term memory and working memory were approximately of the same magnitude. According to our findings, severity of aphasia is related with both verbal and spatial memory deficits. Moreover, while aphasia score correlated with lower scores in both short-term memory and working memory tasks, the lack of substantial difference between corresponding correlation coefficients suggests a possible primary deficit in information retention rather than impairment in working memory. Copyright © 2011 Elsevier Ltd. All rights reserved.
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.
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
Papagno, C; Vallar, G
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.
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)
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)
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...
Cho, Su Jin; Shin, Jeonghee; Namgung, Ran
Survival of very-low-birth-weight infants (VLBWI) depends on professional perinatal management that begins at delivery. Korean Neonatal Network data on neonatal resuscitation management and initial care of VLBWI of less than 33 weeks gestation born from January 2013 to June 2014 were reviewed to investigate the current practice of neonatal resuscitation in Korea. Antenatal data, perinatal data, and short-term morbidities were analyzed. Out of 2,132 neonates, 91.7% needed resuscitation at birt...
Catalao, J.P.S.; Pousinho, H.M.I.; Mendes, V.M.F.
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)
Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan
As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...... of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset...
Lang, Fabian U; Otte, Stefanie; Vasic, Nenad; Jäger, Markus; Dudeck, Manuela
The study aimed to investigate the correlation between impulsiveness and the antisocial personality disorder among short-term prisoners. The impulsiveness was diagnosed by the Barratt Impulsiveness Scale (BIS). Short-term prisoners with antisocial personality disorder scored significant higher marks on the BIS total scale than those without any personality disorder. In detail, they scored higher marks on each subscale regarding attentional, motor and nonplanning impulsiveness. Moderate and high effects were calculated. It is to be considered to regard impulsivity as a conceptual component of antisociality. © Georg Thieme Verlag KG Stuttgart · New York.
Alter, Adam L.; Oppenheimer, Daniel M.
Three studies investigated the impact of the psychological principle of fluency (that people tend to prefer easily processed information) on short-term share price movements. In both a laboratory study and two analyses of naturalistic real-world stock market data, fluently named stocks robustly outperformed stocks with disfluent names in the short term. For example, in one study, an initial investment of $1,000 yielded a profit of $112 more after 1 day of trading for a basket of fluently named shares than for a basket of disfluently named shares. These results imply that simple, cognitive approaches to modeling human behavior sometimes outperform more typical, complex alternatives. PMID:16754871
Parra, Mario; Abrahams, S.; Fabi, K.; Logie, R.; Luzzi, S.; Della Sala, Sergio
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...
Yuan, Xiaohui; Chen, Chen; Yuan, Yanbin; Huang, Yuehua; Tan, Qingxiong
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
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...
Tahiroglu, Aysegul Yolga; Celik, Gonca Gul; Avci, Ayse; Seydaoglu, Gulsah; Uzel, Mehtap; Altunbas, Handan
Objective: The main aim of the present study is to investigate the short-term cognitive effects of computer games in children with different psychiatric disorders and normal controls. Method: One hundred one children are recruited for the study (aged between 9 and 12 years). All participants played a motor-racing game on the computer for 1 hour.…
Ross-Sheehy, Shannon; Oakes, Lisa M.; Luck, Steven J.
Two experiments examined the hypothesis that developing visual attentional mechanisms influence infants' Visual Short-Term Memory (VSTM) in the context of multiple items. Five- and 10-month-old infants (N = 76) received a change detection task in which arrays of three differently colored squares appeared and disappeared. On each trial one square…
Kang, Min-Suk; Choi, Joongrul
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.
Quinlan, Philip T.; Cohen, Dale J.
We conducted a series of recognition experiments that assessed whether visual short-term memory (VSTM) is sensitive to shared category membership of to-be-remembered (tbr) images of common objects. In Experiment 1 some of the tbr items shared the same basic level category (e.g., hand axe): Such items were no better retained than others. In the…
Wright, Anthony A; Elmore, L Caitlin
Three pigeons were trained to remember arrays of 2-6 colored squares and detect which of two squares had changed color to test their visual short-term memory. Procedures (e.g., stimuli, displays, viewing times, delays) were similar to those used to test monkeys and humans. Following extensive training, pigeons performed slightly better than similarly trained monkeys, but both animal species were considerably less accurate than humans with the same array sizes (2, 4 and 6 items). Pigeons and monkeys showed calculated memory capacities of one item or less, whereas humans showed a memory capacity of 2.5 items. Despite the differences in calculated memory capacities, the pigeons' memory results, like those from monkeys and humans, were all well characterized by an inverse power-law function fit to d' values for the five display sizes. This characterization provides a simple, straightforward summary of the fundamental processing of visual short-term memory (how visual short-term memory declines with memory load) that emphasizes species similarities based upon similar functional relationships. By closely matching pigeon testing parameters to those of monkeys and humans, these similar functional relationships suggest similar underlying processes of visual short-term memory in pigeons, monkeys and humans. Copyright © 2015 Elsevier B.V. All rights reserved.
Davis, Hasker P.; Rosenzweig, Mark R.; Jones, Oliver W.
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.
Kleijnen, J.P.C.; Gaury, E.G.A.
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
... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Short-term scholars. 62.21 Section 62.21 Foreign Relations DEPARTMENT OF STATE PUBLIC DIPLOMACY AND EXCHANGES EXCHANGE VISITOR PROGRAM Specific... programs, confer on common problems and projects, and promote professional relationships and communications...
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
Geesink, G.H.; Buren, van R.G.C.; Savenije, B.; Verstegen, M.W.A.; Ducro, B.J.; Palen, van der J.G.P.; Hemke, G.
Two experiments were done to determine whether short-term supplementation (5 days pre-slaughter) with magnesium acetate, or a combination of magnesium acetate, tryptophan, vitamin E and vitamin C would improve pork quality. In the first experiment the pigs (Pietrain x Yorkshire, n = 96) were fed a
Bankó, Eva M; Vidnyánszky, Zoltán
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.
Mercer, Tom; McKeown, Denis
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.
It is the purpose of this booklet to give the reader an overview of the variety, : type, and nature of short-term economic incentive programs that have been : introduced by transit properties over the past few years. 3054k, 55p.
Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark
The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…
Full Text Available The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.
Kavanagh, Dara O
Laparoscopic resection for colon cancer has been proven to have a similar oncological efficacy compared to open resection. Despite this, it is performed by a minority of colorectal surgeons. The aim of our study was to evaluate the short-term clinical, oncological and survival outcomes in all patients undergoing laparoscopic resection for colon cancer.
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
Hagen, John W.; And Others
A short-term memory task was used to explore the effects of verbal labeling and rehearsal on serial-position recall in mildly retarded 9-to 11-year-old children. Results support the view that verbal skills affect recall in mildly retarded children similarly to normal children. (Author/SDH)
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.
Dieleman, Nikki; Koek, Huiberdina L.; Hendrikse, Jeroen
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.
Burchuladze, A.A.; Pagava, S.V.; Jurina, V.; Povinec, P.; Usacev, S.
Radiocarbon variations related to the 11-year solar cycle during the last century are discussed. Previous investigations on short term 14 C variations in tree rings are compared with 14 C measurements in Georgian wine samples. The amplitude of 14 C variations as obtained by various authors ranges from 0.2 to about 1%. (author)
Dillon, Richard F.; Petrusic, William M.
Purpose of study was to (a) compare the rate of increase of proactive interference over the first few trials under recall and recognition memory test conditions, (2) determine the effects of two types of distractors on short-term recognition, and (3) test memory after proactive interference had reached a stable level under each of three test…
Liebert, Robert M.; Baron, Robert A.
Recently collected data appear to warrant advancing some tentative conslusions concerning the short-term effects of violence in television on children: 1) children are exposed to a substantial amount of violent content on television, and they can remember and learn from such exposure; 2) correlational studies have disclosed a regular association…
Background: Rotavirus infection is the single most common cause of acute gastroenteritis in children under five years of age. Rotavirus gastroenteritis has a high morbidity and mortality in children in Kenya. Objectives: To determine the short term clinical outcome for children admitted to Kenyatta National Hospital with ...
Petterson, Stine Asferg; Jakobsen, Ida Pind; Jensen, Stine Skov
was to investigate the short-term effects of radiation of spheroids containing tumor-initiating stem-like cells. We used a patient-derived glioblastoma stem cell enriched culture (T76) and the standard glioblastoma cell line U87. Primary spheroids were irradiated with doses between 2 and 50 Gy and assessed after two...
Against the background of an energy market beset by the Fukushima crisis, the Arab spring and economic uncertainty, 2011 saw dynamic growth in demand for natural gas, although developments varied widely from region to region. New trends are emerging in the gas market, and these will have both short-term and longer-term impacts on how the industry develops. (author)
Fructose feeding causes insulin resistance and invariably Non-Insulin Dependent Diabetes Mellitus (NIDDM) in rats and genetically predisposed humans. The effect of insulin resistance induced by short term fructose feeding on fertility in female rats was investigated using the following parameters: oestrous phase and ...
Introduction: Colorectal carcinoma (CRC) is generally a disease of persons older than 40 years. Concerning younger patients, controversies still exist regarding features and prognosis of CRC. We performed this study to characterise CRC in young patients (≤40 years) as well as to evaluate short-term outcome in ...
The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The principal users of the Outlook are managers and energy analysts in private industry and government. The forecast period for this issue of the Outlook extends from the third quarter of 1992 through the fourth quarter of 1993. Values for the second quarter of 1992, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding
The outlook for gas industry development in the short term is clouded by uncertainties (impact of the economic slowdown, competition between energies, price fluctuations, etc.). However, as in 2012, many favorable factors in terms of natural gas supply and demand point to sustained and sustainable growth of this energy. (author)
Dell'Acqua, Roberto; Sessa, Paola; Toffanin, Paolo; Luria, Roy; Joliccoeur, Pierre
We measured electroencephalographic activity during visual search of a target object among objects available to perception or among objects held in visual short-term memory (VSTM). For perceptual search, a single shape was shown first (pre-cue) followed by a search-array and the task was to decide
Short-term effect of Nigerian diesel oil was tested on the phytoplankton species in Great Kwa River ... aquatic environment. Plant life is the basis of all food web in nature and hence constitutes the makes this fundamental contribution by photosynthesis, utilizing radiant energy to .... (2 cells/ml) re-colonized the area. The three ...
Sligte, I.G.; Scholte, H.S.; Lamme, V.A.F.
Background: Classic work on visual short-term memory (VSTM) suggests that people store a limited amount of items for subsequent report. However, when human observers are cued to shift attention to one item in VSTM during retention, it seems as if there is a much larger representation, which keeps
Jaureguy, Beth M.; Evans, Ron L.
Short term group counseling via the telephone resulted in marked increases in activities of daily living among 12 legally blind veterans. Many subjects' personal coping goals were met as well, and social involvement also increased. No significant changes in levels of depression or agitation were noted. (CL)
Mathes, Eugene W
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.
Objective. To determine the effect of short-term creatine supplementation plus a protein-carbohydrate formula on high-intensity exercise performance and recovery. Design. A repeated-measures, experimental study, employing a randomised, double-blind, placebo-controlled, group comparison design was used.
to be promising short.term mortality markers in HIV patients apart from established factors like low CD4 counts, co.morbid conditions, and opportunistic infections like M. tuberculosis infection. This study warrants further studies with a larger sample size to establish HDL and triglyceride as markers of disease progression and ...
Boleti, Eirini; Hueglin, Christoph; Takahama, Satoshi
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
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
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: email@example.com.
Bavelier, Daphne; Newport, Elissa L.; Hall, Matt; Supalla, Ted; Boutla, Mrim
Capacity limits in linguistic short-term memory (STM) are typically measured with forward span tasks in which participants are asked to recall lists of words in the order presented. Using such tasks, native signers of American Sign Language (ASL) exhibit smaller spans than native speakers ([Boutla, M., Supalla, T., Newport, E. L., & Bavelier, D.…
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.
Etindele Sosso, Faustin Armel
Many neurodegenerative diseases have a memory component. Brain structures related to memory are affected by environmental stimuli, and it is difficult to dissociate effects of all behavior of neurons. Here, visual cortex of mice was stimulated with gratings and dot, and an observation of neuronal activity before and after was made. Bandwidth, firing rate and orientation selectivity index were evaluated. A primary communication between primary visual cortex and short-term memory appeared to show an interesting path to train cognitive circuitry and investigate the basics mechanisms of the neuronal learning. The findings also suggested the interplay between primary visual cortex and short-term plasticity. The properties inside a visual target shape the perception and affect the basic encoding. Using visual cortex, it may be possible to train the memory and improve the recovery of people with cognitive disabilities or memory deficit.
Hsieh, L.Y.; de Graffenried, C.L.
One of the major problems in analyzing the short-term uranium market is the lack of a well-defined spot market price. The two primary sources of price data covering the US uranium market are the series published by the US Dept. of Energy (DOE) and by the Nuclear Exchange Corporation (NUEXCO), a private brokerage firm. Because of the differences in both definition and coverage, these two series are not directly comparable. In this study, an econometric model was developed for analyzing the interrelationship between short-term uranium price (NUEXCO exchange value), supply, demand, and future price expectations formed by market participants. The validity of this model has been demonstrated by the fact that all simulation statistics derived are highly significant. Three forecasting scenarios were developed in this study
The emphasis on renewable energy and concerns about the environment have led to large-scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High-quality short-term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented. © 2012 The Authors. International Statistical Review © 2012 International Statistical Institute.
Zhu Shengnan; Gu Xiuling; Song Lian; Tong Jian; Li Jianxiang
Objective: To investigate the antagonistic effects of short-term fasting against 60 Co γ ray radiation. Methods: After fasting ICR mice were irradiated for 3 min at a dose rate of 2.5 Gy/min and then returned to normal diet. General situation, body weight changes, food consumption and toxic status were observed. WBC, organ index and anti-oxidative ability (ROS, SOD, MDA, T-AOC) were analyzed. Results: After 60 Co γ ray radiation, the mice exhibited severe toxic symptoms before death. The survival rates were 0 for control and 12 h group, 12.5% for 48 h group and 50% for 72 h group respectively. ROS production of 72 h group was reduced compared with 0 h group (P<0.05). Conclusion: Short-term fasting may attenuate radiation induced injuries, evidenced by a significant increase in mice survival rate. (authors)
Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.
We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.
Jarlbæk, Lene; Christensen, Linda; Bruera, Eduardo
Introduction. In this study, we present data from a population-based cohort of incident cancer patients separated in long- and short-term survivors. Our aim was to procure denominators for use in the planning of rehabilitation and palliative care programs. Material and methods. A registry......-linkage cohort study. All cancer patients, diagnosed from 1993 to 2003 from a 470 000 large population, were followed individually from diagnosis to death or until 31 December 2008. Long-term survivors lived five years or more after the time of the cancer diagnosis (TOCD). Short-term survivors died less than...... and sex. Two-year crude cancer survival seems as a clinically relevant cut point for characterizing potential "denominators" for rehabilitation or palliative care programs. From this cohort of incident cancer patients, and using two-year survival as a cut point, it could be estimated that 54% would...
Barzegarjalali, Saeid; Parker, Alice C
Research shows that the way we remember things for a few seconds is a different mechanism from the way we remember things for a longer time. Short-term memory is based on persistently firing neurons, whereas storing information for a longer time is based on strengthening the synapses or even forming new neural connections. Information about location and appearance of an object is segregated and processed by separate neurons. Furthermore neurons can continue firing using different mechanisms. Here, we have designed a biomimetic neuromorphic circuit that mimics short-term memory by firing neurons, using biological mechanisms to remember location and shape of an object. Our neuromorphic circuit has a hybrid architecture. Neurons are designed with CMOS 45nm technology and synapses are designed with carbon nanotubes (CNT).
Jarrold, Christopher; Hall, Debbora
Verbal short-term memory, as indexed by immediate serial recall tasks (in which participants must recall several stimuli in order, immediately after presentation), develops considerably across middle childhood. One explanation for this age-related change is that children's ability to rehearse verbal material increases during this period, and one particularly influential version of this account is that only older children engage in any form of rehearsal. In this article, we critique evidence t...
Easton, Zachary M; Kleinman, Peter J A; Buda, Anthony R; Goering, Dustin; Emberston, Nichole; Reed, Seann; Drohan, Patrick J; Walter, M Todd; Guinan, Pat; Lory, John A; Sommerlot, Andrew R; Sharpley, Andrew
The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Simoens, Veerle L; Tervaniemi, Mari
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.
Latruffe, Laure; LETORT, Elodie
The paper investigates short-term marginal costs in French agriculture for field cropping, beef cattle, and dairy farms during the period 1995-2006. The multi-input multi-output Symmetric Generalised MacFadden cost function is used, with three variable inputs (crop-specific, animal-specific, energy costs), four outputs and three quasi-fixed inputs. Results indicate that marginal costs are on average lower for crop farms than for livestock samples. However, for crop farms, Common Agricultural ...
Scott, Brian H.; Mishkin, Mortimer
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 sho...
Lödding , Hermann; Lohmann , Steffen
Abstract Inventory Based Capacity Control (INCAP) is a very simple method that allows inventory levels to be effectively controlled by using short-term capacity flexibility in make-to-stock settings. Moreover, INCAP can be used for finished goods inventories as well as for semi-finished goods inventories. The basic idea is to define upper and lower inventory limits and to adjust capacities if the inventory level reaches either limit. Should the inventory fall below the lower limit,...
The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). The forecast period for this issue of the Outlook extends from the first quarter of 1995 through the fourth quarter of 1996. Values for the fourth quarter of 1994, however, are preliminary EIA estimates or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the first quarter 1995 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service. The cases are produced using the Short-Term Integrated Forecasting System (STIFS). The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. The EIA model is available on computer tape from the National Technical Information Service
Arenkiel, Benjamin R
Adult neurogenesis has captivated neuroscientists for decades, with hopes that understanding the programs underlying this phenomenon may provide unique insight toward avenues for brain repair. Interestingly, however, despite intense molecular and cellular investigation, the evolutionary roles and biological functions for ongoing neurogenesis have remained elusive. Here I review recent work published in the Journal of Neuroscience that reveals a functional role for continued neurogenesis toward forming short-term olfactory memories.
Full Text Available To understand the molecular pathways underlying the cardiac preconditioning effect of short-term caloric restriction (CR.Lifelong CR has been suggested to reduce the incidence of cardiovascular disease through a variety of mechanisms. However, prolonged adherence to a CR life-style is difficult. Here we reveal the pathways that are modulated by short-term CR, which are associated with protection of the mouse heart from ischemia.Male 10-12 wk old C57bl/6 mice were randomly assigned to an ad libitum (AL diet with free access to regular chow, or CR, receiving 30% less food for 7 days (d, prior to myocardial infarction (MI via permanent coronary ligation. At d8, the left ventricles (LV of AL and CR mice were collected for Western blot, mRNA and microRNA (miR analyses to identify cardioprotective gene expression signatures. In separate groups, infarct size, cardiac hemodynamics and protein abundance of caspase 3 was measured at d2 post-MI.This short-term model of CR was associated with cardio-protection, as evidenced by decreased infarct size (18.5±2.4% vs. 26.6±1.7%, N=10/group; P=0.01. mRNA and miR profiles pre-MI (N=5/group identified genes modulated by short-term CR to be associated with circadian clock, oxidative stress, immune function, apoptosis, metabolism, angiogenesis, cytoskeleton and extracellular matrix (ECM. Western blots pre-MI revealed CR-associated increases in phosphorylated Akt and GSK3ß, reduced levels of phosphorylated AMPK and mitochondrial related proteins PGC-1α, cytochrome C and cyclooxygenase (COX IV, with no differences in the levels of phosphorylated eNOS or MAPK (ERK1/2; p38. CR regimen was also associated with reduced protein abundance of cleaved caspase 3 in the infarcted heart and improved cardiac function.
Subhani, Muhammad Imtiaz; Osman, Ms.Amber; Habib, Sukaina
This research investigates the interdependency between independent (Increase of pricing strategy adaptation, Increase of export intensity, Firm's commitment to exporting, Export market development, Export market competition, Past Pricing Strategy Adaptation, Past Export Performance Satisfaction, Past Export Intensity, Export market distance) and dependent variables (i.e. Expected Short-Term Export Performance improvement) of export performance. The framework is tested via a survey through que...
Feurra, Matteo; Galli, Giulia; Pavone, Enea Francesco; Rossi, Alessandro; Rossi, Simone
The digit span is one of the most widely used memory tests in clinical and experimental neuropsychology for reliably measuring short-term memory capacity. In the forward version, sequences of digits of increasing length have to be reproduced in the order in which they are presented, whereas in the backward version items must be reproduced in the reversed order. Here, we assessed whether transcranial alternating current stimulation (tACS) increases the memory span for digits of young and midlife adults. Imperceptibly weak electrical currents in the alpha (10 Hz), beta (20 Hz), theta (5 Hz), and gamma (40 Hz) range, as well as a sham stimulation, were delivered over the left posterior parietal cortex, a cortical region thought to sustain maintenance processes in short-term memory through oscillatory brain activity in the beta range. We showed a frequency-specific effect of beta-tACS that robustly increased the forward memory span of young, but not middle-aged, healthy individuals. The effect correlated with age: the younger the subjects, the greater the benefit arising from parietal beta stimulation. Our results provide evidence of a short-term memory capacity improvement in young adults by online frequency-specific tACS application. Copyright © 2016 the American Physiological Society.
Galster, Murray; Kahana, Michael J; Wilson, Hugh R; Sekuler, Robert
For some time, the relationship between processing of facial expression and facial identity has been in dispute. Using realistic synthetic faces, we reexamined this relationship for both perception and short-term memory. In Experiment 1, subjects tried to identify whether the emotional expression on a probe stimulus face matched the emotional expression on either of two remembered faces that they had just seen. The results showed that identity strongly influenced recognition short-term memory for emotional expression. In Experiment 2, subjects' similarity/dissimilarity judgments were transformed by multidimensional scaling (MDS) into a 2-D description of the faces' perceptual representations. Distances among stimuli in the MDS representation, which showed a strong linkage of emotional expression and facial identity, were good predictors of correct and false recognitions obtained previously in Experiment 1. The convergence of the results from Experiments 1 and 2 suggests that the overall structure and configuration of faces' perceptual representations may parallel their representation in short-term memory and that facial identity modulates the representation of facial emotion, both in perception and in memory. The stimuli from this study may be downloaded from http://cabn.psychonomic-journals.org/content/supplemental.
Fernhout, C. L. R.
Full Text Available The short-term reinsurance process usually involves three parties, namely the insurer, the reinsurer and the original policyholder, as the insurer cedes a part of the covered risk of the policyholder to the reinsurer. This research however addresses the perceptions of reinsurers regarding their reinsurance activities, where the reinsurer sells reinsurance to other insurance entities (viz. insurers and reinsurers, as well as buys reinsurance from other insurance entities. The crux of short-term reinsurance is therefore mutually loss sharing between the various insurance entities. The objective of this research focuses on the improvement of financial decision-making regarding the reinsurance operations of the reinsurers. To achieve this objective a literature study was undertaken to provide adequate background to compile a questionnaire for the empirical survey. The primary study embodies the perceptions of the South African short-term reinsurers regarding the following aspects: the various reasons why reinsurance occurs; the contracts / methods of reinsurance; the bases / forms of reinsurance; and the factors which determine the retention levels of a reinsurer. South Africa is classified as a developing economy, is a member of the BRICS countries and has an emerging market economy. The empirical results should therefore also be valuable to other countries which are classified similarly
Boonekamp, P.G.M.; Gerdes, J. [ECN Policy Studies, Petten (Netherlands); Faberi, S. [Institute of Studies for the Integration of Systems ISIS, Rome (Italy)
The ODYSSEE database on energy efficiency indicators (www.odyssee-indicators.org) has been set up to enable the monitoring and evaluation of realised energy efficiency improvements and related energy savings. The database covers the 27 EU countries as well as Norway and Croatia and data are available from 1990 on. This work contributes to the growing need for quantitative monitoring and evaluation of the impacts of energy policies and measures, both at the EU and national level, e.g. due to the Energy Services Directive and the proposed Energy Efficiency Directive. Because the underlying data become available only after some time, the savings figures are not always timely available. This is especially true for the ODEX efficiency indices per sector that rely on a number of indicators. Therefore, there is a need for so-called short-term indicators that become available shortly after the year has passed for which data are needed. The short term indicators do not replace the savings indicators but function as a proxy for the savings in the most recent year. This proxy value is faster available, but will be less accurate than the saving indicators themselves. The short term indicators have to be checked regularly with the ODEX indicators in order to see whether they can function still as a proxy.
Albouy, Philippe; Schulze, Katrin; Caclin, Anne; Tillmann, Barbara
Congenital amusia is a neuro-developmental disorder of music perception and production. Recent findings have demonstrated that this deficit is linked to an impaired short-term memory for tone sequences. As it has been shown before that non-musicians' implicit knowledge of musical regularities can improve short-term memory for tone information, the present study investigated if this type of implicit knowledge could also influence amusics' short-term memory performance. Congenital amusics and their matched controls, who were non-musicians, had to indicate whether sequences of five tones, presented in pairs, were the same or different; half of the pairs respected musical regularities (tonal sequences) and the other half did not (atonal sequences). As previously reported for non-musician participants, the control participants showed better performance (as measured with d') for tonal sequences than for atonal ones. While this improvement was not observed in amusics, both control and amusic participants showed faster response times for tonal sequences than for atonal sequences. These findings suggest that some implicit processing of tonal structures is potentially preserved in congenital amusia. This observation is encouraging as it strengthens the perspective to exploit implicit knowledge to help reducing pitch perception and memory deficits in amusia. © 2013 Elsevier B.V. All rights reserved.
Singh, Yogesh; Singh, Jayvardhan; Sharma, Ratna; Talwar, Anjana
The EEG is considered as building block of functional signaling in the brain. The role of EEG oscillations in human information processing has been intensively investigated. To study the quantitative EEG correlates of short term memory load as assessed through Sternberg memory test. The study was conducted on 34 healthy male student volunteers. The intervention consisted of Sternberg memory test, which runs on a version of the Sternberg memory scanning paradigm software on a computer. Electroencephalography (EEG) was recorded from 19 scalp locations according to 10-20 international system of electrode placement. EEG signals were analyzed offline. To overcome the problems of fixed band system, individual alpha frequency (IAF) based frequency band selection method was adopted. The outcome measures were FFT transformed absolute powers in the six bands at 19 electrode positions. Sternberg memory test served as model of short term memory load. Correlation analysis of EEG during memory task was reflected as decreased absolute power in Upper alpha band in nearly all the electrode positions; increased power in Theta band at Fronto-Temporal region and Lower 1 alpha band at Fronto-Central region. Lower 2 alpha, Beta and Gamma band power remained unchanged. Short term memory load has distinct electroencephalographic correlates resembling the mentally stressed state. This is evident from decreased power in Upper alpha band (corresponding to Alpha band of traditional EEG system) which is representative band of relaxed mental state. Fronto-temporal Theta power changes may reflect the encoding and execution of memory task.
The Energy Information Administration (EIA) prepares quarterly short-term energy supply, demand, and price projections for printed publication in January, April, July, and October in the Short-Term Energy Outlook. The details of these projections, as well as monthly updates on or about the 6th of each interim month, are available on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. The forecast period for this issue of the Outlook extends from the fourth quarter of 1997 through the fourth quarter of 1998. Values for the fourth quarter of 1997, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the fourth quarter 1997 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. 19 tabs.
Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo
Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations
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
Fiebig, Florian; Lansner, Anders
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
Wilms, Inge L; Petersen, Anders; Vangkilde, Signe
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.
Bavelier, Daphne; Newport, Elissa L.; Hall, Matt; Supalla, Ted; Boutla, Mrim
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...
Zhang, Duo; Lindholm, Geir; Ratnaweera, Harsha
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.
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.
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.
Sun, Weifeng; Wang, Haotian; Piao, Xianglan; Qiu, Tie
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.
Soezen, Adnan; Arcaklioglu, Erol; Oezalp, Mehmet
This paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANNs are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R 2 value are 99.999%. As seen from the results obtained the thermodynamic equations for each pair by using the weights of network have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations
Jia, Jason; Fernandes, Yohaan; Gerlai, Robert
Learning and memory represent perhaps the most complex behavioral phenomena. Although their underlying mechanisms have been extensively analyzed, only a fraction of the potential molecular components have been identified. The zebrafish has been proposed as a screening tool with which mechanisms of complex brain functions may be systematically uncovered. However, as a relative newcomer in behavioral neuroscience, the zebrafish has not been well characterized for its cognitive and mnemonic features, thus learning and/or memory screens with adults have not been feasible. Here we study short-term memory of adult zebrafish. We show animated images of conspecifics (the stimulus) to the experimental subject during 1 min intervals on ten occasions separated by different (2, 4, 8 or 16 min long) inter-stimulus intervals (ISI), a between subject experimental design. We quantify the distance of the subject from the image presentation screen during each stimulus presentation interval, during each of the 1-min post-stimulus intervals immediately following the stimulus presentations and during each of the 1-min intervals furthest away from the last stimulus presentation interval and just before the next interval (pre-stimulus interval), respectively. Our results demonstrate significant retention of short-term memory even in the longest ISI group but suggest no acquisition of reference memory. Because in the employed paradigm both stimulus presentation and behavioral response quantification is computer automated, we argue that high-throughput screening for drugs or mutations that alter short-term memory performance of adult zebrafish is now becoming feasible. Copyright © 2014 Elsevier B.V. All rights reserved.
Parra, Mario A; Abrahams, Sharon; Fabi, Katia; Logie, Robert; Luzzi, Simona; Della Sala, Sergio
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 studied visual arrays of objects (six for healthy elderly and four for Alzheimer's disease patients), colours (six for healthy elderly and four for Alzheimer's disease patients), unbound objects and colours (three for healthy elderly and two for Alzheimer's disease patients in each of the two categories), or objects bound with colours (three for healthy elderly and two for Alzheimer's disease patients). They were then asked to recall the items verbally. The memory of patients with Alzheimer's disease for objects bound with colours was significantly worse than for single or unbound features whereas healthy elderly's memory for bound and unbound features did not differ. Experiment 2: 21 Alzheimer's disease patients and 20 matched healthy elderly were recruited. Memory load was increased for the healthy elderly group to eight items in the conditions assessing memory for single or unbound features and to four items in the condition assessing memory for the binding of these features. For Alzheimer's disease patients the task remained the same. This manipulation permitted the performance to be equated across groups in the conditions assessing memory for single or unbound features. The impairment in Alzheimer's disease patients in recalling bound objects reported in Experiment 1 was replicated. The binding cost was greater than that observed in the healthy elderly group, who did not differ in their performance for bound and unbound features. Alzheimer's disease grossly impairs the
Wang, He; Lam, Kin; Fung, C. C. Alan; Wong, K. Y. Michael; Wu, Si
In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred stimuli of the neurons. We investigate how short-term synaptic depression (STD) can reshape the intrinsic dynamics of the CANN model and its responses to a single static input. In particular, CANNs with STD can support various complex firing patterns and chaotic behaviors. These chaotic behaviors have the potential to encode various stimuli in the neuronal system.
Pousinho, H.M.I.; Mendes, V.M.F.; Catalao, J.P.S.
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.
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)
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)
Popovic, D P
An efficient approach is presented to the solution of the short-term dynamics model in power systems. It consists of an adequate algebraic treatment of the original system of nonlinear differential equations, using linearization, decomposition and Cauchy's formula. The simple difference equations obtained in this way are incorporated into a model of the electrical network, which is of a low order compared to the ones usually used. Newton's method is applied to the model formed in this way, which leads to a simple and reliable iterative procedure. The characteristics of the procedure developed are demonstrated on examples of transient stability analysis of real power systems. 12 refs.
Watson, Andrew I.; Lopez, Raul E.; Holle, Ronald L.; Daugherty, John R.; Ortiz, Robert
Techniques incorporating wind convergence that can be used for the short-term prediction of thunderstorm development are described. With these techniques, the convergence signal is sensed by the wind network array 15 to 90 min before actual storm development. Particular attention is given to the convergence cell technique (which has been applied at the Kennedy Space Center) where each convective region is analyzed independently. It is noted that, while the monitoring of areal and cellular convergence can be used to help locate the seeds of developing thunderstorms and pinpoint the lightning threat areas, this forecasting aid cannot be used in isolation.
Todd, J Jay; Fougnie, Daryl; Marois, René
The right temporo-parietal junction (TPJ) is critical for stimulus-driven attention and visual awareness. Here we show that as the visual short-term memory (VSTM) load of a task increases, activity in this region is increasingly suppressed. Correspondingly, increasing VSTM load impairs the ability of subjects to consciously detect the presence of a novel, unexpected object in the visual field. These results not only demonstrate that VSTM load suppresses TPJ activity and induces inattentional blindness, but also offer a plausible neural mechanism for this perceptual deficit: suppression of the stimulus-driven attentional network.
Kantasamy, Nesamalar; Sumari, S.M.; Salam, S.M.; Riniswani Aziz
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)
Schweickert, Richard; Hayt, Cathrin
In a test of short-term memory recall, two subjects attempted to recall various lists. For unpracticed subjects, the time it took to read the list is a better predictor of immediate recall than the number of items on the list. For practiced subjects, the two predictors do about equally well. If the items that must be recalled are unfamiliar, it is advantageous to keep the items short to pronounce. On the other hand, if the same items will be encountered over and over again, it is advantageous to make them distinctive, even at the cost of adding to the number of syllables.
Gillebert, Celine; Dyrholm, Mads; Vangkilde, Signe Allerup
The intraparietal sulcus (IPS) has been implicated in selective attention as well as visual short-term memory (VSTM). To contrast mechanisms of target selection, distracter filtering, and access to VSTM, we combined behavioral testing, computational modeling and functional magnetic resonance......, thereby displaying a significant interaction between the two factors. The interaction between target and distracter set size in IPS could not be accounted for by a simple explanation in terms of number of items accessing VSTM. Instead, it led us to a model where items accessing VSTM receive differential...
Purser, H. R.; Farran, E. K.; Courbois, Y.; Lemahieu, A.; Mellier, D.; Sockeel, P.; Blades, M.
The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and visuospatial long-term memory; the route-learning task was conducted using a maze in a virtual environment. In contrast to previous research, correlation...
Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.
Bancroft, Tyler D; Jones, Jeffery A; Ensor, Tyler M; Hockley, William E; Servos, Philip
Studies of interference in working and short-term memory suggest that irrelevant information may overwrite the contents of memory or intrude into memory. While some previous studies have reported greater interference when irrelevant information is similar to the contents of memory than when it is dissimilar, other studies have reported greater interference for dissimilar distractors than for similar distractors. In the present study, we find the latter effect in a paradigm that uses auditory tones as stimuli. We suggest that the effects of distractor similarity to memory contents are mediated by the type of information held in memory, particularly the complexity or simplicity of information.
Manohar, Sanjay G; Pertzov, Yoni; Husain, Masud
Space and time appear to play key roles in the way that information is organized in short-term memory (STM). Some argue that they are crucial contexts within which other stored features are embedded, allowing binding of information that belongs together within STM. Here we review recent behavioral, neurophysiological and imaging studies that have sought to investigate the nature of spatial, sequential and duration representations in STM, and how these might break down in disease. Findings from these studies point to an important role of the hippocampus and other medial temporal lobe structures in aspects of STM, challenging conventional accounts of involvement of these regions in only long-term memory.
The Energy Information Administration (EIA) prepares quarterly short-term energy supply, demand, and price projections. The details of these projections, as well as monthly updates, are available on the Internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. The paper discusses outlook assumptions; US energy prices; world oil supply and the oil production cutback agreement of March 1998; international oil demand and supply; world oil stocks, capacity, and net trade; US oil demand and supply; US natural gas demand and supply; US coal demand and supply; US electricity demand and supply; US renewable energy demand; and US energy demand and supply sensitivities. 29 figs., 19 tabs.
Fleten, Stein-Erik; Kristoffersen, Trine
-term production planning a matter of spatial distribution among the reservoirs of the plant. Day-ahead market prices and reservoir inflows are, however, uncertain beyond the current operation day and water must be allocated among the reservoirs in order to strike a balance between current profits and expected......Within the framework of multi-stage mixed-integer linear stochastic programming we develop a short-term production plan for a price-taking hydropower plant operating under uncertainty. Current production must comply with the day-ahead commitments of the previous day which makes short...
Ito, Asuka; Soda, Jiro
We reveal the universality of short-term anisotropic inflation. As a demonstration, we study inflation with an exponential type gauge kinetic function which is ubiquitous in models obtained by dimensional reduction from higher dimensional fundamental theory. It turns out that an anisotropic inflation universally takes place in the later stage of conventional inflation. Remarkably, we find that primordial gravitational waves with a peak amplitude around 10 −26 ∼10 −27 are copiously produced in high-frequency bands 10 MHz∼100 MHz. If we could detect such gravitational waves in future, we would be able to probe higher dimensional fundamental theory.
Poston, T.M.; Klopfer, D.C.; Simmons, M.A.
Short-term laboratory exposures were conducted to determine the potential accumulation of Np in aquatic organisms. Concentration factors were highest in green algae. Daphnia magna, a filter-feeding crustacean, accumulated Np at levels one order of magnitude greater than the amphipod Gammarus sp., an omnivorous substrate feeder. Accumulation of Np in juvenile rainbow trout (Oncorhynchus mykiss) was highest in carcass (generally greater than 78% of the total body burden) and lowest in fillets. Recommended concentration factors for Np, based on fresh weight, were 300 for green algae, 100 for filter-feeding invertebrates, for nonfilter-feeding invertebrates, 10 for whole fish, and one for fish flesh
FIREMAN, GARY; KOPLEWICZ, HAROLD S.
To examine the effectiveness of a short-term behavioral treatment of encopresis, 52 encopretic children were evaluated and treated according to a standardized protocol. The treatment was highly effective, with a significant decrease in soiling during the first month (P < 0.01). Of the children who began treatment, 84.6% successfully reached the criterion of 2 consecutive weeks with no soiling accidents in a mean time of 28 days, and 78.8% successfully completed an additional 7-week phaseout period. The evaluations provided rich descriptive information regarding the characteristics of encopretic children. In agreement with the literature, no specific pattern of behavioral pathology was apparent. PMID:22700057
Yang, Jingfei; Stenzel, Juergen [Darmstadt University of Techonology, Darmstadt 64283 (Germany)
This paper presents a new regression tree method for short-term load forecasting. Both increment and non-increment tree are built according to the historical data to provide the data space partition and input variable selection. Support vector machine is employed to the samples of regression tree nodes for further fine regression. Results of different tree nodes are integrated through weighted average method to obtain the comprehensive forecasting result. The effectiveness of the proposed method is demonstrated through its application to an actual system. (author)
Kouhi, Sajjad; Keynia, Farshid
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
Full Text Available Technology driven interventions provide us with an increasing amount of fine-grained data about the patient. This data includes regular ecological momentary assessments (EMA but also response times to EMA questions by a user. When observing this data, we see a huge variation between the patterns exhibited by different patients. Some are more stable while others vary a lot over time. This poses a challenging problem for the domain of artificial intelligence and makes on wondering whether it is possible to predict the future mental state of a patient using the data that is available. In the end, these predictions could potentially contribute to interventions that tailor the feedback to the user on a daily basis, for example by warning a user that a fall-back might be expected during the next days, or by applying a strategy to prevent the fall-back from occurring in the first place.In this work, we focus on short term mood prediction by considering the adherence and usage data as an additional predictor. We apply recurrent neural networks to handle the temporal aspects best and try to explore whether individual, group level, or one single predictive model provides the highest predictive performance (measured using the root mean squared error (RMSE. We use data collected from patients from five countries who used the ICT4Depression/MoodBuster platform in the context of the EU E-COMPARED project. In total, we used the data from 143 patients (with between 9 and 425days of EMA data who were diagnosed with a major depressive disorder according to DSM-IV.Results show that we can make predictions of short term mood change quite accurate (ranging between 0.065 and 0.11. The past EMA mood ratings proved to be the most influential while adherence and usage data did not improve prediction accuracy. In general, group level predictions proved to be the most promising, however differences were not significant.Short term mood prediction remains a difficult task
Full Text Available Short-term monocular deprivation alters visual perception in adult humans, increasing the dominance of the deprived eye, for example, as measured with binocular rivalry. This form of plasticity may depend upon the inhibition/excitation balance in the visual cortex. Recent work suggests that cortical excitability is reliably tracked by dilations and constrictions of the pupils of the eyes. Here, we ask whether monocular deprivation produces a systematic change of pupil behavior, as measured at rest, that is independent of the change of visual perception. During periods of minimal sensory stimulation (in the dark and task requirements (minimizing body and gaze movements, slow pupil oscillations, “hippus,” spontaneously appear. We find that hippus amplitude increases after monocular deprivation, with larger hippus changes in participants showing larger ocular dominance changes (measured by binocular rivalry. This tight correlation suggests that a single latent variable explains both the change of ocular dominance and hippus. We speculate that the neurotransmitter norepinephrine may be implicated in this phenomenon, given its important role in both plasticity and pupil control. On the practical side, our results indicate that measuring the pupil hippus (a simple and short procedure provides a sensitive index of the change of ocular dominance induced by short-term monocular deprivation, hence a proxy for plasticity.
Zhai Renyou; Dai Dingke; Wang Jianfeng; Yu Ping; Wei Baojie
Objective: To analyze the method and short term efficacy of interventional therapy for hilar biliary obstructive jaundice. Methods: 100 consecutive patients with perihilar biliary obstruction admitted before May 2004 were treated with percutaneous transhepatic biliary drainage (PTBD) or placement of metallic stents. Among them, 39 patients were found with bile duct cancer, 6 with adenocarcinoma of gallbladder, 22 with metastatic carcinoma, 15 with primary liver carcinoma and 18 with bile duct strait after liver transplantation. Serum total bilirubin before operation and 3-7 days, 8-14 days after procedure were analysed by t test. Results: 79 patients with PTBD (including simple external drainage and combined internal and external drainage), and 21 patients with stents placement (including 31 stents of 4 different kinds) were all carried out successfully. There were significant differences in serum total bilirubin before and 3-7 days, 8-14 days after the procedure, P<0.05 vs P<0.01. Conclusion: Interventional therapy is simple, safe, and effective for hilar biliary obstruction, the latter showed more significance than the former with short term satisfaction. (authors)
Morris, John N.; Steel, Knight; Strout, Kelley A.; Fries, Brant E.; Moore, Alice; Garms-Homolová, Vjenka
Cognitive decline impacts older adults, particularly their independence. The goal of this project was to increase understanding of how short-term, everyday lifestyle options, including physical activity, help an older adult sustain cognitive independence. Using a secondary analysis of lifestyle choices, we drew on a dataset of 4,620 community-dwelling elders in the US, assessed at baseline and one year later using 2 valid and reliable tools, the interRAI Community Health Assessment and the interRAI Wellness tool. Decline or no decline on the Cognitive Performance Scale was the dependent variable. We examined sustaining one's status on this measure over a one-year period in relation to key dimensions of wellness through intellectual, physical, emotional, social, and spiritual variables. Engaging in physical activity, formal exercise, and specific recreational activities had a favorable effect on short-term cognitive decline. Involvement with computers, crossword puzzles, handicrafts, and formal education courses also were protective factors. The physical and intellectual domains of wellness are prominent aspects in protection from cognitive decline. Inherent in these two domains are mutable factors suitable for targeted efforts to promote older adult health and well-being. PMID:27891520
Full Text Available Abstract Background Exposure to cooking fumes may have different deleterious effects on the respiratory system. The aim of this study was to look at possible effects from inhalation of cooking fumes on pulmonary function. Methods Two groups of 12 healthy volunteers (A and B stayed in a model kitchen for two and four hours respectively, and were monitored with spirometry four times during twenty four hours, on one occasion without any exposure, and on another with exposure to controlled levels of cooking fumes. Results The change in spirometric values during the day with exposure to cooking fumes, were not statistically significantly different from the changes during the day without exposure, with the exception of forced expiratory time (FET. The change in FET from entering the kitchen until six hours later, was significantly prolonged between the exposed and the unexposed day with a 15.7% increase on the exposed day, compared to a 3.2% decrease during the unexposed day (p-value = 0.03. The same tendency could be seen for FET measurements done immediately after the exposure and on the next morning, but this was not statistically significant. Conclusion In our experimental setting, there seems to be minor short term spirometric effects, mainly affecting FET, from short term exposure to cooking fumes.
Noreen, Saima; Ridout, Nathan
The study aimed to determine if the memory bias for negative faces previously demonstrated in depression and dysphoria generalises from long- to short-term memory. A total of 29 dysphoric (DP) and 22 non-dysphoric (ND) participants were presented with a series of faces and asked to identify the emotion portrayed (happiness, sadness, anger, or neutral affect). Following a delay, four faces were presented (the original plus three distractors) and participants were asked to identify the target face. Half of the trials assessed memory for facial emotion, and the remaining trials examined memory for facial identity. At encoding, no group differences were apparent. At memory testing, relative to ND participants, DP participants exhibited impaired memory for all types of facial emotion and for facial identity when the faces featured happiness, anger, or neutral affect, but not sadness. DP participants exhibited impaired identity memory for happy faces relative to angry, sad, and neutral, whereas ND participants exhibited enhanced facial identity memory when faces were angry. In general, memory for faces was not related to performance at encoding. However, in DP participants only, memory for sad faces was related to sadness recognition at encoding. The results suggest that the negative memory bias for faces in dysphoria does not generalise from long- to short-term memory.
LeCompte, D C; Watkins, M J
The role of stimulus similarity as an organising principle in short-term memory was explored in a series of seven experiments. Each experiment involved the presentation of a short sequence of items that were drawn from two distinct physical classes and arranged such that item class changed after every second item. Following presentation, one item was re-presented as a probe for the 'target' item that had directly followed it in the sequence. Memory for the sequence was considered organised by class if probability of recall was higher when the probe and target were from the same class than when they were from different classes. Such organisation was found when one class was auditory and the other was visual (spoken vs. written words, and sounds vs. pictures). It was also found when both classes were auditory (words spoken in a male voice vs. words spoken in a female voice) and when both classes were visual (digits shown in one location vs. digits shown in another). It is concluded that short-term memory can be organised on the basis of sensory modality and on the basis of certain features within both the auditory and visual modalities.
Elizabeth P. Howard
Full Text Available Cognitive decline impacts older adults, particularly their independence. The goal of this project was to increase understanding of how short-term, everyday lifestyle options, including physical activity, help an older adult sustain cognitive independence. Using a secondary analysis of lifestyle choices, we drew on a dataset of 4,620 community-dwelling elders in the US, assessed at baseline and one year later using 2 valid and reliable tools, the interRAI Community Health Assessment and the interRAI Wellness tool. Decline or no decline on the Cognitive Performance Scale was the dependent variable. We examined sustaining one’s status on this measure over a one-year period in relation to key dimensions of wellness through intellectual, physical, emotional, social, and spiritual variables. Engaging in physical activity, formal exercise, and specific recreational activities had a favorable effect on short-term cognitive decline. Involvement with computers, crossword puzzles, handicrafts, and formal education courses also were protective factors. The physical and intellectual domains of wellness are prominent aspects in protection from cognitive decline. Inherent in these two domains are mutable factors suitable for targeted efforts to promote older adult health and well-being.
The main aim of the present study is to investigate the short-term cognitive effects of computer games in children with different psychiatric disorders and normal controls. One hundred one children are recruited for the study (aged between 9 and 12 years). All participants played a motor-racing game on the computer for 1 hour. The TBAG form of the Stroop task was administered to all participants twice, before playing and immediately after playing the game. Participants with improved posttest scores, compared to their pretest scores, used the computer on average 0.67 +/- 1.1 hr/day, while the average administered was measured at 1.6 +/- 1.4 hr/day and 1.3 +/- 0.9 hr/day computer use for participants with worse or unaltered scores, respectively. According to the regression model, male gender, younger ages, duration of daily computer use, and ADHD inattention type were found to be independent risk factors for worsened posttest scores. Time spent playing computer games can exert a short-term effect on attention as measured by the Stroop test.
Kavé, Gitit; Ze'ev, Hagit Bar; Lev, Anita
This paper investigates the processing of Hebrew derivational morphology in an individual (S.E.) with deficient phonological short-term memory. In comparison to 10 age- and education-matched men, S.E. was impaired on digit span tasks and demonstrated no recency effect in word list recall. S.E. had low word retention span, but he exhibited phonological similarity and word length effects. His ability to make lexical decisions was intact. In a paired-associate test S.E. successfully learned semantically and morphologically related pairs but not phonologically related pairs, and his learning of nonwords was facilitated by the presence of Hebrew consonant roots. Semantic and morphological similarity enhanced immediate word recall. Results show that S.E. is capable of conducting morphological decomposition of Hebrew-derived words despite his phonological deficit, suggesting that transient maintenance of morphological constituents is independent of temporary storage and rehearsal of phonological codes, and that each is processed separately within short-term memory.
Gorin, Simon; Mengal, Pierre; Majerus, Steve
Recent theoretical accounts of verbal and visuo-spatial short-term memory (STM) have proposed the existence of domain-general mechanisms for the maintenance of serial order information. These accounts are based on the observation of similar behavioural effects across several modalities, such as temporal grouping effects. Across two experiments, the present study aimed at extending these findings, by exploring a STM modality that has received little interest so far, STM for musical information. Given its inherent rhythmic, temporal and serial organisation, the musical domain is of interest for investigating serial order STM processes such as temporal grouping. In Experiment 1, the data did not allow to determine the presence or the absence of temporal grouping effects. In Experiment 2, we observed that temporal grouping of tone sequences during encoding improves short-term recognition for serially presented probe tones. Furthermore, the serial position curves included micro-primacy and micro-recency effects, which are the hallmark characteristic of temporal grouping. Our results suggest that the encoding of serial order information in musical STM may be supported by temporal positional coding mechanisms similar to those reported in the verbal domain.
Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V.; Laitinen, E.; Haekli, J.; Antila, E.
In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market
Koreneff, G; Seppaelae, A; Lehtonen, M; Kekkonen, V [VTT Energy, Espoo (Finland); Laitinen, E; Haekli, J [Vaasa Univ. (Finland); Antila, E [ABB Transmit Oy (Finland)
Ensuring energy security has been at the centre of the IEA mission since its inception, following the oil crises of the early 1970s. While the security of oil supplies remains important, contemporary energy security policies must address all energy sources and cover a comprehensive range of natural, economic and political risks that affect energy sources, infrastructures and services. In response to this challenge, the IEA is currently developing a Model Of Short-term Energy Security (MOSES) to evaluate the energy security risks and resilience capacities of its member countries. The current version of MOSES covers short-term security of supply for primary energy sources and secondary fuels among IEA countries. It also lays the foundation for analysis of vulnerabilities of electricity and end-use energy sectors. MOSES contains a novel approach to analysing energy security, which can be used to identify energy security priorities, as a starting point for national energy security assessments and to track the evolution of a country's energy security profile. By grouping together countries with similar 'energy security profiles', MOSES depicts the energy security landscape of IEA countries. By extending the MOSES methodology to electricity security and energy services in the future, the IEA aims to develop a comprehensive policy-relevant perspective on global energy security. This Working Paper is intended for readers who wish to explore the MOSES methodology in depth; there is also a brochure which provides an overview of the analysis and results.
Fritsch, P.; Beauvallet, M.; Masse, R.; Lafuma, J.
The genetic effects induced by α irradiation were examined using short term assays in Procaryotes and Eucaryotes. Irradiation was produced by 239 Pu dissolved as a DTPA equimolar complex in the culture medium. Induced mutagenesis was not observed with Ames' test or when test for ouabain resistance in CHO cells was used: GTG resistance and chromosome aberrations in Eucaryote cells were increased at dose rate exposure down to 5 R.day -1 . Until an optimal delivered dose, these two biological effects have shown a linear increase as a function of the dose. In our experimental conditions α irradiation has appeared to be much more lethal than mutagenic. Using lower dose rate, corresponding to 1 and 3 R a day we could also demonstrate a linear increase with dose of the induced TG resistant cells. Efficiency per unit dose was 3 to 5 times superior to what was observed at 5 R.day -1 . This phenomenon could correspond to an induced cell sensitivity, and clearly pointed out that for chronic and low delivered doses, informations deduced from flash or short term α exposure are not valuable
Egsgaard, Line Lindhardt; Buchgreitz, Line; Wang, Li
To investigate the effects of homotopic and heterotopic conditioning pain modulation (CPM) on short-term cortical plasticity. Glutamate (tonic pain) or isotonic saline (sham) was injected in the upper trapezius (homotopic) and in the thenar (heterotopic) muscles. Intramuscular electrical stimulat......To investigate the effects of homotopic and heterotopic conditioning pain modulation (CPM) on short-term cortical plasticity. Glutamate (tonic pain) or isotonic saline (sham) was injected in the upper trapezius (homotopic) and in the thenar (heterotopic) muscles. Intramuscular electrical......, and after homotopic and heterotopic CPM versus control. Peak latencies at N100, P200, and P300 were extracted and the location/strength of corresponding dipole current sources and multiple dipoles were estimated. Homotopic CPM caused hypoalgesia (P = 0.032, 30.6% compared to baseline) to electrical...... stimulation. No cortical changes were found for homotopic CPM. A positive correlation at P200 between electrical pain threshold after tonic pain and the z coordinate after tonic pain (P = 0.032) was found for homotopic CPM. For heterotopic CPM, no significant hypoalgesia was found and a dipole shift of the P...
Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad
This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes
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
Eben, Kryštof; Juruš, Pavel; Resler, Jaroslav; Belda, Michal; Pelikán, Emil; Krüger, B.C.; Keder, J.
Roč. 131, č. 613 (2005), s. 3313-3322 ISSN 0035-9009 R&D Projects: GA AV ČR 1ET400300414; GA ČR GA205/02/1488 Institutional research plan: CEZ:AV0Z10300504 Keywords : air quality * data assimilation Subject RIV: BA - General Mathematics Impact factor: 2.033, year: 2005
Pires, J C M; Gonçalves, B; Azevedo, F G; Carneiro, A P; Rego, N; Assembleia, A J B; Lima, J F B; Silva, P A; Alves, C; Martins, F G
This study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O(3)) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons. Two of the methodologies define threshold models, which assume that the behaviour of the dependent variable (O(3) concentrations) changes when it enters in a different regime (two and four regimes were considered in this study). The change from one regime to another depends on a specific value (threshold value) of an explanatory variable (threshold variable), which is also defined by GAs. The predictor variables were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide, nitrogen dioxide (NO(2)), and O(3) (recorded in the previous day at an urban site with traffic influence) and also meteorological data (hourly averages of temperature, solar radiation, relative humidity and wind speed). The study was performed for the period from May to August 2004. Several models were achieved and only the best model of each methodology was analysed. In threshold models, the variables selected by GAs to define the O(3) regimes were temperature, CO and NO(2) concentrations, due to their importance in O(3) chemistry in an urban atmosphere. In the prediction of O(3) concentrations, the threshold model that considers two regimes was the one that fitted the data most efficiently.
Jackson, D. D.; Kagan, Y. Y.; Schorlemmer, D.; Zechar, J. D.; Wang, Q.; Wong, K.
Earthquake induced Coulomb stresses, whether static or dynamic, suddenly change the probability of future earthquakes. Models to estimate stress and the resulting seismicity changes could help to illuminate earthquake physics and guide appropriate precautionary response. But do these models have improved forecasting power compared to empirical statistical models? The best answer lies in prospective testing in which a fully specified model, with no subsequent parameter adjustments, is evaluated against future earthquakes. The Center of Study of Earthquake Predictability (CSEP) facilitates such prospective testing of earthquake forecasts, including several short term forecasts. Formulating Coulomb stress models for formal testing involves several practical problems, mostly shared with other short-term models. First, earthquake probabilities must be calculated after each “perpetrator” earthquake but before the triggered earthquakes, or “victims”. The time interval between a perpetrator and its victims may be very short, as characterized by the Omori law for aftershocks. CSEP evaluates short term models daily, and allows daily updates of the models. However, lots can happen in a day. An alternative is to test and update models on the occurrence of each earthquake over a certain magnitude. To make such updates rapidly enough and to qualify as prospective, earthquake focal mechanisms, slip distributions, stress patterns, and earthquake probabilities would have to be made by computer without human intervention. This scheme would be more appropriate for evaluating scientific ideas, but it may be less useful for practical applications than daily updates. Second, triggered earthquakes are imperfectly recorded following larger events because their seismic waves are buried in the coda of the earlier event. To solve this problem, testing methods need to allow for “censoring” of early aftershock data, and a quantitative model for detection threshold as a function of
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.
Cheng, T.C.; Fischer, S.R.
Following the transient of a hypothetical loss-of-coolant accident (LOCA) in a nuclear reactor, peak pressures are reached within the first 0.03 s at different locations inside the reactor cavity. Due to the complicated multidimensional nature of the reactor cavity, the short-term analysis of the LOCA transient cannot be performed by using traditional containment codes, such as CONTEMPT. The advanced containment code, BEACON/MOD3, developed at the Idaho National Engineering Laboratory (INEL), can be adapted for such analysis. This code provides Eulerian, one and two-dimensional, nonhomogeneous, nonequilibrium flow modeling as well as lumped parameter, homogeneous, equilibrium flow modeling for the solution of two-component, two-phase flow problems. The purpose of this paper is to demonstrate the capability of the BEACON code to analyze complex containment geometry such as a reactor cavity
Galatti, Laura; Polimeni, Giovanni; Salvo, Francesco; Romani, Marcello; Sessa, Aurelio; Spina, Edoardo
Memory loss and cognitive impairment have been reported in the literature in association with several 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins), but we found no published case reports associated with rosuvastatin. To our knowledge, this is the first reported case of rosuvastatin-related short-term memory loss. A 53-year-old Caucasian man with hypercholesterolemia experienced memory loss after being treated with rosuvastatin 10 mg/day. He had no other concomitant conditions or drug therapies. After discontinuation of rosuvastatin, the neuropsychiatric adverse reaction resolved gradually, suggesting a probable drug association. During the following year, the patient remained free from neuropsychiatric disturbances. Clinicians should be aware of possible adverse cognitive reactions during statin therapy, including rosuvastatin.
Elizabeth Griffin brings a personal insight to the hurdles that women seeking a scientific career face, arguing that the only gender differences are those of attitude, tradition and style. The policy of employing some but not all academic researchers through short-term contracts is highly divisive, in that it creates a two-tier system not only of opportunities and expectations but also of personal worth and value. Far more women than men are trapped in these career cul-de-sacs, and a seriously large fraction is unable to stay in research until retirement. It is the employment policy that is at fault, not the potential of the researchers or the quality of their research.
Knežević, Goran; Baus, Zoran; Nikolovski, Srete
In this paper short-term planning algorithm for hybrid power system consist of different types of cascade hydropower plants (run-of-the river, pumped storage, conventional), thermal power plants (coal-fired power plants, combined cycle gas-fired power plants) and wind farms is presented. The optimization process provides a joint bid of the hybrid system, and thus making the operation schedule of hydro and thermal power plants, the operation condition of pumped-storage hydropower plants with the aim of maximizing profits on day ahead market, according to expected hourly electricity prices, the expected local water inflow in certain hydropower plants, and the expected production of electrical energy from the wind farm, taking into account previously contracted bilateral agreement for electricity generation. Optimization process is formulated as hourly-discretized mixed integer linear optimization problem. Optimization model is applied on the case study in order to show general features of the developed model.
Butterworth, B; Cipolotti, L; Warrington, E K
We document the dissociation of preserved calculation skills in a patient with impaired auditory short-term memory. The patient (MRF) had a memory span of three digits. Furthermore, he showed rapid decrement in performance of single digits and letters with both auditory and visual presentation in the Brown-Peterson forgetting task. Analysis of his calculation skills revealed a normal ability to solve auditorily presented multidigit addition and subtraction problems such as 173 + 68 and to execute the Paced Auditory Serial Addition Task (Sampson, 1956, 1958; Gronwall, 1977). In addition, his performance on other tests, including arithmetic manipulation of natural numbers, decimals and fractions, approximation, magnitude, ratio, and percentage, appeared to be normal (Hitch, 1978b). It is argued that these findings require a revision of Baddeley and Hitch's (1974) concept of the function of working memory.
Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei
To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.
Qi, Yi; de Ruiter, Anton
In this paper, the short-term capture (STC) of an asteroid in the Earth-Moon system is proposed and investigated. First, the space condition of STC is analysed and five subsets of the feasible region are defined and discussed. Then, the time condition of STC is studied by parameter scanning in the Sun-Earth-Moon-asteroid restricted four-body problem. Numerical results indicate that there is a clear association between the distributions of the time probability of STC and the five subsets. Next, the influence of the Jacobi constant on STC is examined using the space and time probabilities of STC. Combining the space and time probabilities of STC, we propose a STC index to evaluate the probability of STC comprehensively. Finally, three potential STC asteroids are found and analysed.
Berdan, Radu; Vasilaki, Eleni; Khiat, Ali; Indiveri, Giacomo; Serb, Alexandru; Prodromakis, Themistoklis
Neuromorphic architectures offer great promise for achieving computation capacities beyond conventional Von Neumann machines. The essential elements for achieving this vision are highly scalable synaptic mimics that do not undermine biological fidelity. Here we demonstrate that single solid-state TiO2 memristors can exhibit non-associative plasticity phenomena observed in biological synapses, supported by their metastable memory state transition properties. We show that, contrary to conventional uses of solid-state memory, the existence of rate-limiting volatility is a key feature for capturing short-term synaptic dynamics. We also show how the temporal dynamics of our prototypes can be exploited to implement spatio-temporal computation, demonstrating the memristors full potential for building biophysically realistic neural processing systems.
Andersen, Torben Gustav; Fusari, Nicola; Todorov, Viktor
a direct way to study volatility and jump risks. Unlike longer-dated options, they are largely insensitive to the risk of intertemporal shifts in the economic environment. Adopting a novel semi-nonparametric approach, we uncover variation in the negative jump tail risk which is not spanned by market......We study short-term market risks implied by weekly S&P 500 index options. The introduction of weekly options has dramatically shifted the maturity profile of traded options over the last five years, with a substantial proportion now having expiry within one week. Such short-dated options provide......" by the level of market volatility and elude standard asset pricing models....
2009 was a particularly bad year for the natural gas industry, with demand falling dramatically by 2.8% as a result of the world economic crisis. However, 2010 appears to have been a very positive year for the industry, with a sustained increase in production and trade. Increased economic activity, together with harsh winters and competitive gas prices are the reasons for the markets having rediscovered their buoyancy. Although the economic recovery has shown signs of fragility in OECD countries, global natural gas demand should continue to grow rapidly in the short-term, driven by consumption in developing countries, suggesting that the gas bubble will be reabsorbed faster than expected on the international markets. (author)
Song, Changwei; Zheng, Yuan
This paper proposes a new method for short-term load forecasting based on the similar day method, correlation coefficient and Fast Fourier Transform (FFT) to achieve the precision analysis of load variation from three aspects (typical day, correlation coefficient, spectral analysis) and three dimensions (time dimension, industry dimensions, the main factors influencing the load characteristic such as national policies, regional economic, holidays, electricity and so on). First, the branch algorithm one-class-SVM is adopted to selection the typical day. Second, correlation coefficient method is used to obtain the direction and strength of the linear relationship between two random variables, which can reflect the influence caused by the customer macro policy and the scale of production to the electricity price. Third, Fourier transform residual error correction model is proposed to reflect the nature of load extracting from the residual error. Finally, simulation result indicates the validity and engineering practicability of the proposed method.
Kumar, Arjun; Jiang, Yuhong
The capacity of visual short-term memory (VSTM) for a single visual display has been investigated in past research, but VSTM for multiple sequential arrays has been explored only recently. In this study, we investigate the capacity of VSTM across two sequential arrays separated by a variable stimulus onset asynchrony (SOA). VSTM for spatial locations (Experiment 1), colors (Experiments 2-4), orientations (Experiments 3 and 4), and conjunction of color and orientation (Experiment 4) were tested, with the SOA across the two sequential arrays varying from 100 to 1,500 msec. We find that VSTM for the trailing array is much better than VSTM for the leading array, but when averaged across the two arrays VSTM has a constant capacity independent of the SOA. We suggest that multiple displays compete for retention in VSTM and that separating information into two temporally discrete groups does not enhance the overall capacity of VSTM.
Murray, Alexandra M; Nobre, Anna C; Clark, Ian A; Cravo, André M; Stokes, Mark G
When a memory is forgotten, is it lost forever? Our study shows that selective attention can restore forgotten items to visual short-term memory (VSTM). In our two experiments, all stimuli presented in a memory array were designed to be equally task relevant during encoding. During the retention interval, however, participants were sometimes given a cue predicting which of the memory items would be probed at the end of the delay. This shift in task relevance improved recall for that item. We found that this type of cuing improved recall for items that otherwise would have been irretrievable, providing critical evidence that attention can restore forgotten information to VSTM. Psychophysical modeling of memory performance has confirmed that restoration of information in VSTM increases the probability that the cued item is available for recall but does not improve the representational quality of the memory. We further suggest that attention can restore discrete items to VSTM.
Koontz, E; Cox, D; Hastings, S
1. Although family involvement has been increasingly recognized as a vital component in the treatment and care of the mentally ill, little has been written about efforts to provide education and support to the families of patients hospitalized for short-term evaluation and treatment. 2. The family education and support group provided emotional support and critical information to increase family members' coping and problem solving abilities, and enabled them to return to a pre-crisis or higher level of functioning. 3. The family education and support group not only enhances the assessment and planning phases of the nursing process, but it also can serve as a useful intervention for strengthening the patient's major support system.
The author expresses the opinion that rather than focusing market discussions around short-term versus long-term procurement strategies, the parties need to be focusing on how long it is going to take to get to a predominantly market-based price both in uranium and enrichment. Long-term contracts are going to be around and will always be an important part of buyers' and sellers' strategies. It is evident that the annual term contract price renegotiations around the world are resulting in continually lower prices. When these price negotiations finally arrive in the range of the market price, a commodity market that resembles other energy commodity markets can be obtained
The author presents his opinion on the issue of short-term versus long-term procurement of uranium and enrichment and the impact on reliability of supply. The progression of the market has been one of increasing commoditization. Utility buyers have moved towards purchasing uranium on the spot market and linking long-term contracts to spot-market pricing. There is some logic to the argument that utilities and the industry in general would be best served by this approach. Inventories would be worked off much more quickly, and unnecessary supply would be shut off until prices recovered to profitable levels. The result would be a healthier market with no detriment to the reliability of supply
Presented is a Safety Evaluation Report (SER) prepared by the Office of Nuclear Reactor Regulation addressing the Short Term Program (STP) reassessment of the containment systems of operating Boiler Water Reactor (BWR) facilities with the Mark I containment system design. The information presented in this SER establishes the basis for the NRC staff's conclusion that licensed Mark I BWR facilities can continue to operate safely, without undue risk to the health and safety of the public, during an interim period of approximately two years while a methodical, comprehensive Long Term Program (LTP) is conducted. This SER also provides one of the basic foundations for the NRC staff review of the Mark I containment systems for facilities not yet licensed for operation
Oda, M.; Doi, K.; Ogawara, Y.
The short-term X-ray variability distinguishes Cyg X-1, which is the most likely candidate for a black hole, from other X-ray sources. The present status of our knowledge on this short-term variation, mainly from the UHURU, the MIT and the GSFC observations, is reviewed. The nature of impulsive variations which compose the time variation exceeding the statistical fluctuation is discussed. There are indications that the energy spectrum of large pulses is harder than the average spectrum, or that the large pulses are the characteristics of the hard component of the spectrum if it is composed of two, soft and hard, components. Features of the variations may be partly simulated by the superposition of random shot-noise pulses with a fraction of a second duration. However, the autocorrelation analysis and the dynamic spectrum analysis indicate that the correlation lasts for several seconds and in the variation are buried some regularities which exhibit power concentrations in several frequency bands; 0.2-0.3, 0.4-0.5, 0.8, 1.2-1.5 Hz. There are several possible interpretations of these results in terms of; e.g. (a) a mixture of shot-noise pulses with two or more constant durations, (b) the shape of the basic shot-noise pulse, (c) bunching of the pulses, (d) superposition of wave-packets or temporal oscillations. But we have not yet reached any definite understandings in the nature of the variabilities. The substructure of the fluctuations on a time scale of milliseconds suggested by two investigations is also discussed. (Auth.)
Oda, M.; Doi, K.; Ogawara, Y.; Takagishi, K.; Wada, M.
The short-term X-ray variability distinguishes Cyg X-1, which is the most likely candidate of the black hole, from other X-ray sources. Present status of our knowledge on this short-term variation mainly from the Uhuru, the MIT and the GSFC observations is reviewed. The nature of impulsive variations which compose the time variation exceeding the statistical fluctuation is discussed. There are indications that the energy spectrum of large pulses is harder than the average spectrum or the large pulses are the characteristics of the hard component of the spectrum if it is composed of two, soft and hard, components. Features of the variations may be partly simulated by the superposition of random short-noise pulses with a fraction of a second duration. However, the autocorrelation analysis and the dynamic spectrum analysis indicate that the correlation lasts for several seconds and in the variation buried are some regularities which exhibit power concentrations in several frequency bands; 0.2 -- 0.3, 0.4 -- 0.5, 0.8, 1.2 -- 1.5 Hz. There are several possible interpretation of these results in terms of: e.g. a) a mixture of short-noise pulses with two or more constant durations, b) the shape of the basic shot-noise pulse, c) bunching of the pulses, d) superposition of wave-packets or temporal oscillations. But we have not yet reached any definite understandings in the nature of the variabilities. The sub-structure of the fluctuations on a time scale of milli-second suggested by two investigations is also discussed. (auth.)
Lutsey, Pamela L; Parrinello, Christina M; Misialek, Jeffrey R; Hoofnagle, Andy N; Henderson, Clark M; Laha, Thomas J; Michos, Erin D; Eckfeldt, John H; Selvin, Elizabeth
Quantifying the variability of biomarkers is important, as high within-person variability can lead to misclassification of individuals. Short-term variability of important markers of vitamin D metabolism is relatively unknown. A repeatability study was conducted in 160 Atherosclerosis Risk in Communities study participants (60% female, 28% black, mean age 76 years). Fasting serum was drawn at 2 time points, a median of 6 (range 3-13) weeks apart. Vitamin D binding protein (VDBP) and 25-hydroxyvitamin D [25(OH)D] were measured by LC-MS, fibroblast growth factor (FGF23) and parathyroid hormone (PTH) by enzyme-linked immunoassay, and calcium and phosphorus by Roche Cobas 6000. Free and bioavailable 25(OH)D were calculated. We calculated the within-person CV (CV W ), intraclass correlation coefficient (ICC), Spearman rank correlation coefficient (r), and percent reclassified. The CV W was lowest for calcium (2.0%), albumin (3.6%), 25(OH)D (6.9%), VDBP (7.0%) and phosphorus (7.6%); intermediate for free 25(OH)D (9.0%) and bioavailable 25(OH)D (9.9%); and highest for PTH (16.7%) and FGF23 (17.8%). Reclassification was highest for PTH, VDBP, and phosphorus (all 7.5%). The ICC and r were highest (≥0.80) for 25(OH)D, free 25(OH)D, bioavailable 25(OH)D and PTH, but somewhat lower (approximately 0.60-0.75) for the other biomarkers. Six-week short-term variability, as assessed by CV W , was quite low for VDBP, calcium and phosphorus, but fairly high for FGF23 and PTH. As such, multiple measurements of FGF23 and PTH may be needed to minimize misclassification. These results provide insight into the extent of potential misclassification of vitamin D markers in research and clinical settings. © 2016 American Association for Clinical Chemistry.
Stevens, Madelyn N; Baudhuin, Jacqueline E; Hullar, Timothy E
Cochlear implantation is a highly effective intervention for hearing loss, but insertion of an implant into the cochlea is often accompanied by loss of residual hearing function. Sometimes, postoperative testing also shows loss of function in the semicircular canals or otolith organs. The effect of this loss on equilibrium, particularly in the short term following surgery, and the risk of falling due to this loss is unknown. We prospectively measured balance in 16 consecutive adult cochlear implant patients before and 2 weeks after surgery. Subjects stood on a foam pad with eyes closed, feet together and arms at the side. The length of time over which this posture could be maintained was recorded up to a maximum value of 30 s indicating normal performance. Ten of 16 subjects reached a maximal time on preoperative testing. Nine of 16 subjects lost balance function after surgery. Four of the 10 subjects with normal preoperative balance function lost function. Subjects older than the age of 60 were more likely to lose balance function than younger subjects. We used previously published values relating balance performance on foam to risk of falling to calculate the fall risk among our subjects. The relative risk of falling increased after surgery by more than threefold in some patients. Imbalance after cochlear implantation may be much more common, particularly in the short term, than previously appreciated. This imbalance is accompanied by an increased risk of falling in many patients. Careful preoperative counseling before implantation and postoperative therapeutic intervention to improve comfort and reduce the chance of falling may be warranted, particularly in patients at a risk for injuries from falls (level of evidence: 2b). © 2014 S. Karger AG, Basel.
Ates, Ufuk; Ergun, Ergun; Gollu, Gulnur; Sozduyar, Sumeyye; Can, Ozlem Selvi; Yagmurlu, Aydin
Obesity is one of the most rapidly increasing health problems in children. Laparoscopic sleeve gastrectomy (LSG) is one of the best treatment options and is feasible and safe in children. The aim of this study was to present the short-term results of a laparoscopic sleeve gastrectomy series in children. Children who underwent LSG in 2014-2017 were included in the study. Charts were investigated retrospectively and short-term weight loss was analyzed. Patients who had surgery in 2014-2017 were included in the study. There were six girls and two boys, and the median age was 15 years (range, 11-18 years). Mean weight was 159.25 ± 19.78 kg, and mean body mass index was 61.05 ± 8.5 kg/m 2 . Mean operation time was 70 min (range, 65-90 min), mean hospital stay was 5.1 days (range, 3-7 days), and mean follow up was 19.2 months (range, 1-43 months). Of these patients, five had hypertension and were under medication and two of these five also had hyperinsulinemia. One of the five children had Bardet-Biedl syndrome and one had bronchial asthma. After operation, medication was stopped in four of the eight children. At the time of writing, six patients were doing well without postoperative complications, or the need for reoperation. Even though the follow-up period was short and the number of patients was small, LSG was a feasible and promising surgical method for morbidly obese children. A multidisciplinary approach and lifelong behavior therapy are key steps for success. © 2018 Japan Pediatric Society.
Banta Lavenex, Pamela; Boujon, Valérie; Ndarugendamwo, Angélique; Lavenex, Pierre
Here, we aimed to determine the capacity of human short-term memory for allocentric spatial information in a real-world setting. Young adults were tested on their ability to learn, on a trial-unique basis, and remember over a 1-min interval the location(s) of 1, 3, 5, or 7 illuminating pads, among 23 pads distributed in a 4m×4m arena surrounded by curtains on three sides. Participants had to walk to and touch the pads with their foot to illuminate the goal locations. In contrast to the predictions from classical slot models of working memory capacity limited to a fixed number of items, i.e., Miller's magical number 7 or Cowan's magical number 4, we found that the number of visited locations to find the goals was consistently about 1.6 times the number of goals, whereas the number of correct choices before erring and the number of errorless trials varied with memory load even when memory load was below the hypothetical memory capacity. In contrast to resource models of visual working memory, we found no evidence that memory resources were evenly distributed among unlimited numbers of items to be remembered. Instead, we found that memory for even one individual location was imprecise, and that memory performance for one location could be used to predict memory performance for multiple locations. Our findings are consistent with a theoretical model suggesting that the precision of the memory for individual locations might determine the capacity of human short-term memory for spatial information. Copyright © 2015 Elsevier Inc. All rights reserved.
Schwarb, Hillary; Nail, Jayde; Schumacher, Eric H
Since antiquity, philosophers, theologians, and scientists have been interested in human memory. However, researchers today are still working to understand the capabilities, boundaries, and architecture. While the storage capabilities of long-term memory are seemingly unlimited (Bahrick, J Exp Psychol 113:1-2, 1984), working memory, or the ability to maintain and manipulate information held in memory, seems to have stringent capacity limits (e.g., Cowan, Behav Brain Sci 24:87-185, 2001). Individual differences, however, do exist and these differences can often predict performance on a wide variety of tasks (cf. Engle What is working-memory capacity? 297-314, 2001). Recently, researchers have promoted the enticing possibility that simple behavioral training can expand the limits of working memory which indeed may also lead to improvements on other cognitive processes as well (cf. Morrison and Chein, Psychol Bull Rev 18:46-60 2011). However, initial investigations across a wide variety of cognitive functions have produced mixed results regarding the transferability of training-related improvements. Across two experiments, the present research focuses on the benefit of working memory training on visual short-term memory capacity-a cognitive process that has received little attention in the training literature. Data reveal training-related improvement of global measures of visual short-term memory as well as of measures of the independent sub-processes that contribute to capacity (Awh et al., Psychol Sci 18(7):622-628, 2007). These results suggest that the ability to inhibit irrelevant information within and between trials is enhanced via n-back training allowing for selective improvement on untrained tasks. Additionally, we highlight a potential limitation of the standard adaptive training procedure and propose a modified design to ensure variability in the training environment.
Nuclear emergency planning, preparedness, response, and management, in general, are essential elements of any country's nuclear power programme. Part of nuclear emergency planning and preparedness is the implementation of national emergency plans, including detailed procedures for the implementation of short-term countermeasures, before during, and after the release of radioactive substances. The timely and appropriate implementation of short-term countermeasures, such as sheltering, evacuation, and iodine prophylaxis, can, in case of a nuclear emergency with a release of radioactive material, considerably reduce the doses to the public in the vicinity of the nuclear installation. Although international guidelines exist, national procedures and practices may differ due to different national habits, cultural specificity, and societal needs. Different national procedures and practices may, however, in the case of a radioactive release affecting two neighbouring countries, lead to different decisions in the implementation of countermeasures. In order to better understand existing approaches and to facilitate the comparison of national practices, the NEA decided to launch a questionnaire on current practices regarding short-term countermeasures, updating a similar survey performed in 1994 and 2003, as countries' practices have since evolved and been modified. In 2012, it was decided to reevaluate the country approaches in light of the early lessons learnt from the Fukushima Daiichi NPP accident. The information collected may be used to understand the basis for decisions in various countries, and, if deemed appropriate, as a basis for international harmonisation. This may also assist member countries to explain to the public affected by an emergency why the decisions in neighbouring countries may vary. This report summarises the information given by member countries and includes nine sections to explore the different aspects, covering the following topics: member
Vazquez, Miguel; Hallack, Michelle
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
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%
Hou, F. Z.; Li, F. W.; Wang, J.; Yan, F. R.
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.
Miller, Kari Knutson; Gonzalez, Amber M.
This paper examines outcomes associated with participation in short-term, international internship experiences. Results suggest short-term international internship experiences contribute to rich personal and professional development outcomes. Findings highlight participant challenges associated with initial internship experiences, professional…
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
Blonbou, Ruddy; Monjoly, Stephanie; Dorville, Jean-Francois
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.
Soezen, Adnan; Arcaklioglu, Erol; Oezalp, Mehmet
Thermodynamic analysis of absorption thermal systems is too complex because the analytic functions calculating the thermodynamic properties of fluid couples involve the solution of complex differential equations and simulation programs. This study aims at easing this complex situation and consists of three cases: (i) A special ejector, located at the absorber inlet, instead of the common location at the condenser inlet, to increase overall performance was used in the ejector absorption heat pump (EAHP). The ejector has two functions: Firstly, it aids the pressure recovery from the evaporator and then upgrades the mixing process and pre-absorption by the weak solution of the methanol coming from the evaporator. (ii) Use of artificial neural networks (ANNs) has been proposed to determine the properties of the liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiCl), which does not cause ozone depletion. (iii) A comparative performance study of the EAHP was performed between the analytic functions and the values predicted by the ANN for the properties of the couple. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. In the input layer, there are temperature, pressure and concentration of the couples. Specific volume is in the output layer. After training, it was found that the maximum error was less than 3%, the average error was less than 1.2% and the R 2 values were about 0.9999. Additionally, in comparison of the analysis results between analytic equations obtained by using experimental data and by means of the ANN, the deviations of the refrigeration effectiveness of the system for cooling (COP r ), exergetic coefficient of performance of the system for cooling (ECOP r ) and circulation ratio (F) for all working temperatures were
Jones, Gary; Macken, Bill
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...
Balduck, Anne-Line; Buelens, Marc; Philippaerts, Renaat
The present study addressed the issue of short-term performance effects of midseason coach turnover in soccer. The goal of this study was to examine this effect on subsequent short-term team performance. The purposes of this study were to (a) examine whether midseason coach turnover improved results in the short term, and (b) examine how team…
Lammers, Laureen A.; Achterbergh, Roos; de Vries, Emmely M.; van Nierop, F. Samuel; Klümpen, Heinz-Josef; Soeters, Maarten R.; Boelen, Anita; Romijn, Johannes A.; Mathôt, Ron A. A.
Experimental studies indicate that short-term fasting alters drug metabolism. However, the effects of short-term fasting on drug metabolism in humans need further investigation. Therefore, the aim of this study was to evaluate the effects of short-term fasting (36 h) on P450-mediated drug
The effects of decreased protection from ultraviolet radiation are as troubling as the continuing depletion of stratospheric ozone. Evidence exists to clearly link ozone depletion to changes in the antarctic marine environment. Results of two 1992 papers are summarized here. Enhanced exposure to mid-range UV radiation was found to be affecting marine ecosystems with a recorded 6-12 percent reduction in primary productivity directly related to the ozone layer depletion. In another experiment, a model was developed indicating that the ozone hole could reduce near-surface photosynthesis by as much as 12-15 percent. The NSF UV monitoring system in place for these and other experiments uses a spectroradiometer, making hourly, high-resolution measurements of the distribution of UV surface irradiance
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
Pioggia, G.; Ferro, M.; Di Francesco, F.; DeRossi, D.
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.
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.
Full Text Available Background: “Sleep-low” consists of a sequential periodization of carbohydrate (CHO availability—low glycogen recovery after “train high” glycogen-depleting interval training, followed by an overnight-fast and light intensity training (“train low” the following day. This strategy leads to an upregulation of several exercise-responsive signaling proteins, but the chronic effect on performance has received less attention. We investigated the effects of short-term exposure to this strategy on endurance performance. Methods: Following training familiarization, 11 trained cyclists were divided into two groups for a one-week intervention—one group implemented three cycles of periodized CHO intake to achieve the sleep-low strategy over six training sessions (SL, CHO intake: 6 g·kg−1·day−1, whereas the control group consumed an even distribution of CHO over the day (CON. Tests were a 2 h submaximal ride and a 20 km time trial. Results: SL improved their performance (mean: +3.2%; p < 0.05 compared to CON. The improvement was associated with a change in pacing strategy with higher power output during the second part of the test. No change in substrate utilization was observed after the training period for either group. Conclusion: Implementing the “sleep-low” strategy for one week improved performance by the same magnitude previously seen in a three-week intervention, without any significant changes in selected markers of metabolism.
Boland, John; David, Mathieu; Lauret, Philippe
Due its intermittency, the large-scale integration of solar energy into electricity grids is an issue and more specifically in an insular context. Thus, forecasting the output of solar energy is a key feature to efficiently manage the supply-demand balance. In this paper, three short term forecasting procedures are applied to island locations in order to see how they perform in situations that are potentially more volatile than continental locations. Two continental locations, one coastal and one inland are chosen for comparison. At the two time scales studied, ten minute and hourly, the island locations prove to be more difficult to forecast, as shown by larger forecast errors. It is found that the three methods, one purely statistical combining Fourier series plus linear ARMA models, one combining clear sky index models plus neural net models, and a third using a clear sky index plus ARMA, give similar forecasting results. It is also suggested that there is great potential of merging modelling approaches on different horizons. - Highlights: • Solar energy forecasting is more difficult for insular than continental sites. • Fourier series plus linear ARMA models are one forecasting method tested. • Clear sky index models plus neural net models are also tested. • Clear sky index models plus linear ARMA is also an option. • All three approaches have similar skill.
Dakic, Tamara B; Jevdjovic, Tanja V; Peric, Mina I; Bjelobaba, Ivana M; Markelic, Milica B; Milutinovic, Bojana S; Lakic, Iva V; Jasnic, Nebojsa I; Djordjevic, Jelena D; Vujovic, Predrag Z
In the hypothalamus, insulin takes on many roles involved in energy homoeostasis. Therefore, the aim of this study was to examine hypothalamic insulin expression during the initial phase of the metabolic response to fasting. Hypothalamic insulin content was assessed by both radioimmunoassay and Western blot. The relative expression of insulin mRNA was examined by qPCR. Immunofluorescence and immunohistochemistry were used to determine the distribution of insulin immunopositivity in the hypothalamus. After 6-h fasting, both glucose and insulin levels were decreased in serum but not in the cerebrospinal fluid. Our study showed for the first time that, while the concentration of circulating glucose and insulin decreased, both insulin mRNA expression and insulin content in the hypothalamic parenchyma were increased after short-term fasting. Increased insulin immunopositivity was detected specifically in the neurons of the hypothalamic periventricular nucleus and in the ependymal cells of fasting animals. These novel findings point to the complexity of mechanisms regulating insulin expression in the CNS in general and in the hypothalamus in particular. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Papagno, Costanza; Minniti, Giovanna; Mattavelli, Giulia C; Mantovan, Lara; Cecchetto, Carlo
To verify whether loosing a sense or two has consequences on a spared sensory modality, namely touch, and whether these consequences depend on practice or are biologically determined, we investigated 13 deafblind participants, 16 deaf participants, 15 blind participants, and 13 matched normally sighted and hearing controls on a tactile short-term memory task, using checkerboard matrices of increasing length in which half of the squares were made up of a rough texture and half of a smooth one. Time of execution of a fixed matrix, number of correctly reproduced matrices, largest matrix correctly reproduced and tactile span were recorded. The three groups of sensory-deprived individuals did not differ in any measure, while blind and deaf participants outscored controls in all parameters except time of execution; the difference approached significance for deafblind people compared to controls only in one measure, namely correctly reproduced matrices. In blind and deafblind participants, performance negatively correlated with age of Braille acquisition, the older being the subject when acquiring Braille, the lower the performance, suggesting that practice plays a role. However, the fact that deaf participants, who did not share tactile experience, performed similarly to blind participants and significantly better than controls highlights that practice cannot be the only contribution to better tactile memory.
Noyce, Abigail L; Cestero, Nishmar; Shinn-Cunningham, Barbara G; Somers, David C
Vision and audition have complementary affinities, with vision excelling in spatial resolution and audition excelling in temporal resolution. Here, we investigated the relationships among the visual and auditory modalities and spatial and temporal short-term memory (STM) using change detection tasks. We created short sequences of visual or auditory items, such that each item within a sequence arose at a unique spatial location at a unique time. On each trial, two successive sequences were presented; subjects attended to either space (the sequence of locations) or time (the sequence of inter item intervals) and reported whether the patterns of locations or intervals were identical. Each subject completed blocks of unimodal trials (both sequences presented in the same modality) and crossmodal trials (Sequence 1 visual, Sequence 2 auditory, or vice versa) for both spatial and temporal tasks. We found a strong interaction between modality and task: Spatial performance was best on unimodal visual trials, whereas temporal performance was best on unimodal auditory trials. The order of modalities on crossmodal trials also mattered, suggesting that perceptual fidelity at encoding is critical to STM. Critically, no cost was attributable to crossmodal comparison: In both tasks, performance on crossmodal trials was as good as or better than on the weaker unimodal trials. STM representations of space and time can guide change detection in either the visual or the auditory modality, suggesting that the temporal or spatial organization of STM may supersede sensory-specific organization.
The Virtual Power Plant (VPP) has gained an increasing interest over the last few years. A VPP is a flexible representation of a portfolio of Distributed Energy Resources (DER: distributed generation, demand response and electricity storage). One of the key activities of a VPP is the delivery of (near-)real-time balancing services. In order to operate such a (near-)real-time coordination activity optimally, the VPP needs to maintain a dynamic merit-order list of all DER participating in the VPP. In order to make optimal decisions based on this list, the merit order needs to be based on the true marginal cost (or marginal benefit in case of demand response) of the individual DER units. The marginal electricity costs of most types of DER are highly dependent on local context and, hence, change over time. From analysis of the short-term bid strategies of various DER units, the existence of a bid strategy spectrum becomes clear. On one end of the spectrum, bidding strategies are based straightforwardly on true marginal cost or benefit. Further along the spectrum, optimal bidding strategies become less dependent on marginal cost levels and more on the price dynamics in the (VPP) market context. These results are relevant for VPP operations both from business and technical perspectives.
Soler Crespo, L.; Martín Muñoz, F. J.; Gómez Briceño, D.
Martensitic steels have been proposed to be used as structural materials and as spallation target window in hybrid systems devoted to the transmutation of radioactive waste of long life and high activity. However, their compatibility with lead-bismuth in the operating conditions of these systems depends on the existence of a protective layer such as an oxide film. The feasibility of forming and maintaining an oxide layer or maintaining a pre-oxidised one has been studied. Martensitic steel F82Hmod. (8% Cr) has been tested in lead-bismuth under static and isothermal conditions at 400°C and 600°C. In order to study the first stages of the interaction between the steel and the eutectic, short-term tests (100 and 665 h) have been carried out. Pre-oxidised and as-received samples have been tested in atmospheres with different oxidant potential. For low oxygen concentration in lead-bismuth due to unexpected oxygen consumption in the experimental device, dissolution of as-received F82Hmod. occurs and pre-oxidation does not prevent the material dissolution. For high oxygen concentration, the pre-oxidation layer seems to improve the feasibility of protecting stainless steels controlling the oxygen potential of lead-bismuth with a gas phase.
Soler Crespo, L.; Martin Munoz, F.J.; Gomez Briceno, D.
Martensitic steels have been proposed to be used as structural materials and as spallation target window in hybrid systems devoted to the transmutation of radioactive waste of long life and high activity. However, their compatibility with lead-bismuth in the operating conditions of these systems depends on the existence of a protective layer such as an oxide film. The feasibility of forming and maintaining an oxide layer or maintaining a pre-oxidised one has been studied. Martensitic steel F82Hmod. (8% Cr) has been tested in lead-bismuth under static and isothermal conditions at 400 o C and 600 o C. In order to study the first stages of the interaction between the steel and the eutectic, short-term tests (100 and 665 h) have been carried out. Pre-oxidised and as-received samples have been tested in atmospheres with different oxidant potential. For low oxygen concentration in lead-bismuth due to unexpected oxygen consumption in the experimental device, dissolution of as-received F82Hmod. occurs and pre-oxidation does not prevent the material dissolution. For high oxygen concentration, the pre-oxidation layer seems to improve the feasibility of protecting stainless steels controlling the oxygen potential of lead-bismuth with a gas phase
Caplan, David; Waters, Gloria; Howard, David
The model of performance in short-term memory (STM) tasks that has been most influential in cognitive neuropsychological work on deficits of STM is the "working memory" model mainly associated with the work of Alan Baddeley and his colleagues. This paper reviews the model. We examine the development of this theory in studies that account for STM performances in normal (non-brain-damaged) individuals, and then review the application of this theory to neuropsychological cases and specifications, modifications, and extensions of the theory that have been suggested on the basis of these cases. Our approach is to identify the major phenomena that have been discussed and to examine selected papers dealing with those phenomena in some detail. The main contribution is a review of the WM model that includes both normative and neuropsychological data. We conclude that the WM model has many inconsistencies and empirical inadequacies, and that cognitive neuropsychologists might benefit from considering other models when they attempt to describe and explain patients' performances on STM tasks.
Zhang, Shusheng; Wang, Dangxiao; Afzal, Naqash; Zhang, Yuru; Wu, Ruilin
Brainwave entrainment using rhythmic visual and/or auditory stimulation has shown its efficacy in modulating neural activities and cognitive ability. In the presented study, we aim to investigate whether rhythmic haptic stimulation could enhance short-term attention. An experiment with sensorimotor rhythm (SMR) increasing protocol was performed in which participants were presented sinusoidal vibrotactile stimulus of 15 Hz on their palm. Test of Variables of Attention (T.O.V.A.) was performed before and after the stimulating session. Electroencephalograph (EEG) was recorded across the stimulating session and the two attention test sessions. SMR band power manifested a significant increase after stimulation. Results of T.O.V.A. tests indicated an improvement in the attention of participants who had received the stimulation compared to the control group who had not received the stimulation. The D prime score of T.O.V.A. reveals that participants performed better in perceptual sensitivity and sustaining attention level compared to their baseline performance before the stimulating session. These findings highlight the potential value of using haptics-based brainwave entrainment for cognitive training.
The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It's filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter's. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach's and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author)
Noyce, Abigail L.; Cestero, Nishmar; Shinn-Cunningham, Barbara G.; Somers, David C.
Vision and audition have complementary affinities, with vision excelling in spatial resolution and audition excelling in temporal resolution. Here, we investigate the relationships among visual and auditory modalities and spatial and temporal short-term memory (STM) using change detection tasks. We created short sequences of visual or auditory items, such that each item within a sequence arose at a unique spatial location at a unique time. On each trial, two successive sequences were presented; subjects attended to either space (the sequence of locations), or time (the sequence of inter-item intervals), and reported whether the patterns of locations or intervals were identical. Each subject completed blocks of unimodal trials (both sequences presented in the same modality) and crossmodal trials (sequence 1 visual and sequence 2 auditory, or vice versa) for both spatial and temporal tasks. We found a strong interaction between modality and task: spatial performance was best on unimodal visual trials, while temporal performance was best on unimodal auditory trials. The order of modalities on crossmodal trials also mattered, suggesting that perceptual fidelity at encoding is critical to STM. Critically, there was no cost attributable to crossmodal comparison: in both tasks, performance on crossmodal trials was as good or better than on the weaker unimodal trials. STM representations of space and time can guide change detection in either the visual or the auditory modality, suggesting that temporal or spatial organization of STM may supersede sensory-specific organization. PMID:26791231
Seo, Heung Suk; Koh, Byung Hee; Cho, On Koo; Hahm, Chang Kok; Rhee, Jong Chul; Lee, Min Ho; Kee, Choon Suhk [Hanyang University College of Medicine, Seoul (Korea, Republic of)
Anticancer effect and complications were evaluated after transcatheter arterial embolization (TAE) in 12 patients with hepatocellular carcinoma until 2 weeks and 4 weeks after TAE, respectively. The results were as follows; 1. Serum alpha-fetoprotein value decreased in 7 out of 9 patients with high value prior to TAE. 2. Loss of enhancement and better definition on enhanced computed tomography (CT) were seen in the tumors in all caes, and low density areas in 9/10.Gas bubbles were seen in low-density areas in 4/10 and high density areas caused by lipiodol in 6/10. 3. Post-embolization syndrome was developed in most patients but improved clinically within a week after TAE. 4. On laboratory examination, impairment of liver function was developed in most patients but improved within 4 weeks after TAE. 5. Complications on CT included splenic infarction and thickening of wall of the gallbladder, which didn't require specific treatment. The authors conclude that TAE for hepatocellular carcinoma reveals apparent anticancer effect on short-term evaluation, and resultant complications are transient and improved by conservative treatment.
Seo, Heung Suk; Koh, Byung Hee; Cho, On Koo; Hahm, Chang Kok; Rhee, Jong Chul; Lee, Min Ho; Kee, Choon Suhk
Anticancer effect and complications were evaluated after transcatheter arterial embolization (TAE) in 12 patients with hepatocellular carcinoma until 2 weeks and 4 weeks after TAE, respectively. The results were as follows; 1. Serum alpha-fetoprotein value decreased in 7 out of 9 patients with high value prior to TAE. 2. Loss of enhancement and better definition on enhanced computed tomography (CT) were seen in the tumors in all caes, and low density areas in 9/10.Gas bubbles were seen in low-density areas in 4/10 and high density areas caused by lipiodol in 6/10. 3. Post-embolization syndrome was developed in most patients but improved clinically within a week after TAE. 4. On laboratory examination, impairment of liver function was developed in most patients but improved within 4 weeks after TAE. 5. Complications on CT included splenic infarction and thickening of wall of the gallbladder, which didn't require specific treatment. The authors conclude that TAE for hepatocellular carcinoma reveals apparent anticancer effect on short-term evaluation, and resultant complications are transient and improved by conservative treatment
Caplan, David; Waters, Gloria; Howard, David
Background The model of performance in short-term memory (STM) tasks that has been most influential in cognitive neuropsychological work on deficits of STM is the “working memory” model mainly associated with the work of Alan Baddeley and his colleagues. Aim This paper reviews the model. We examine the development of this theory in studies that account for STM performances in normal (non-brain-damaged) individuals, and then review the application of this theory to neuropsychological cases and specifications, modifications, and extensions of the theory that have been suggested on the basis of these cases. Our approach is to identify the major phenomena that have been discussed and to examine selected papers dealing with those phenomena in some detail. Main Contribution The main contribution is a review of the WM model that includes both normative and neuropsychological data. Conclusions We conclude that the WM model has many inconsistencies and empirical inadequacies, and that cognitive neuropsychologists might benefit from considering other models when they attempt to describe and explain patients’ performances on STM tasks. PMID:24347786
Caplan, David; Michaud, Jennifer; Hufford, Rebecca
Sixty one people with aphasia were tested on ten tests of short term memory (STM) and for the ability to use syntactic structure to determine the meanings of eleven types of sentences in three tasks – object manipulation, picture matching and picture matching with self-paced listening. Multilevel models showed relationships between measures of the ability to retain and manipulate item and order information in STM and accuracy and RT, and a greater relationship between these STM measures and accuracy and RT for several more complex sentence types in individual tasks. There were no effects of measures of STM that reflect the use of phonological codes or rehearsal on comprehension. There was only one effect of STM measures on self-paced listening times. There were double dissociations between performance on STM and individual comprehension tasks, indicating that normal STM is not necessary to perform normally on these tasks. The results are most easily related to the view that STM plays a facilitatory role in supporting the use of the products of the comprehension process to accomplish operations related to tasks. PMID:23865692
Sewell, David K; Lilburn, Simon D; Smith, Philip L
Research suggests that visual short-term memory (VSTM) has both an item capacity, of around 4 items, and an information capacity. We characterize the information capacity limits of VSTM using a task in which observers discriminated the orientation of a single probed item in displays consisting of 1, 2, 3, or 4 orthogonally oriented Gabor patch stimuli that were presented in noise for 50 ms, 100 ms, 150 ms, or 200 ms. The observed capacity limitations are well described by a sample-size model, which predicts invariance of ∑(i)(d'(i))² for displays of different sizes and linearity of (d'(i))² for displays of different durations. Performance was the same for simultaneous and sequentially presented displays, which implicates VSTM as the locus of the observed invariance and rules out explanations that ascribe it to divided attention or stimulus encoding. The invariance of ∑(i)(d'(i))² is predicted by the competitive interaction theory of Smith and Sewell (2013), which attributes it to the normalization of VSTM traces strengths arising from competition among stimuli entering VSTM. PsycINFO Database Record (c) 2014 APA, all rights reserved.
Boulet-Craig, Aubrée; Robaey, Philippe; Lacourse, Karine; Jerbi, Karim; Oswald, Victor; Krajinovic, Maja; Laverdière, Caroline; Sinnett, Daniel; Jolicoeur, Pierre; Lippé, Sarah
Previous research suggests visual short-term memory (VSTM) capacity and mathematical abilities are significantly related. Moreover, both processes activate similar brain regions within the parietal cortex, in particular, the intraparietal sulcus; however, it is still unclear whether the neuronal underpinnings of VSTM directly correlate with mathematical operation and reasoning abilities. The main objective was to investigate the association between parieto-occipital brain activity during the retention period of a VSTM task and performance in mathematics. The authors measured mathematical abilities and VSTM capacity as well as brain activity during memory maintenance using magnetoencephalography (MEG) in 19 healthy adult participants. Event-related magnetic fields (ERFs) were computed on the MEG data. Linear regressions were used to estimate the strength of the relation between VSTM related brain activity and mathematical abilities. The amplitude of parieto-occipital cerebral activity during the retention of visual information was related to performance in 2 standardized mathematical tasks: mathematical reasoning and calculation fluency. The findings show that brain activity during retention period of a VSTM task is associated with mathematical abilities. Contributions of VSTM processes to numerical cognition should be considered in cognitive interventions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Quinlan, Philip T; Cohen, Dale J
Findings of 2 experiments are reported that challenge the current understanding of visual short-term memory (VSTM). In both experiments, a single study display, containing 6 colored shapes, was presented briefly and then probed with a single colored shape. At stake is how VSTM retains a record of different objects that share common features: In the 1st experiment, 2 study items sometimes shared a common feature (either a shape or a color). The data revealed a color sharing effect, in which memory was much better for items that shared a common color than for items that did not. The 2nd experiment showed that the size of the color sharing effect depended on whether a single pair of items shared a common color or whether 2 pairs of items were so defined-memory for all items improved when 2 color groups were presented. In explaining performance, an account is advanced in which items compete for a fixed number of slots, but then memory recall for any given stored item is prone to error. A critical assumption is that items that share a common color are stored together in a slot as a chunk. The evidence provides further support for the idea that principles of perceptual organization may determine the manner in which items are stored in VSTM. PsycINFO Database Record (c) 2012 APA, all rights reserved.
The short-term outlook for the Western Canadian natural gas industry was reviewed. Based on the dramatic growth in the demand for gas, and the explosive growth of the industry in response to export opportunities, the outlook for the remainder of this decade remains good, notwithstanding low internal returns, below-average returns on equity, and increased competition for U.S. markets. The competition will come from offshore wells in the US Gulf, from offshore wells of Sable Island, and from offshore wells in the Gulf of Mexico. Despite the increasing sources of supply gas prices are actually expected to improve slightly in 1997 and beyond, giving rise to cautious optimism. Nevertheless, more favorable tax treatment is urgently required to head off the likelihood of investment dollars moving to Africa, the Middle East, South America, and most especially China and Russia. Once those countries open up for business in the not-too-distant future, the likelihood of them offering substantially better tax treatment than what is available in Canada could do serious damage to domestic gas industry development. 12 figs
Leach, C. S.
Clinical laboratory data from blood samples obtained from astronauts before and after 28 flights (average duration = 6 days) of the Space Shuttle were analyzed by the paired t-test and the Wilcoxon signed-rank test and compared with data from the Skylab flights (duration approximately 28, 59, and 84 days). Angiotensin I and aldosterone were elevated immediately after short-term space flights, but the response of angiotensin I was delayed after Skylab flights. Serum calcium was not elevated after Shuttle flights, but magnesium and uric acid decreased after both Shuttle and Skylab. Creatine phosphokinase in serum was reduced after Shuttle but not Skylab flights, probably because exercises to prevent deconditioning were not performed on the Shuttle. Total cholesterol was unchanged after Shuttle flights, but low density lipoprotein cholesterol increased and high density lipoprotein cholesterol decreased. The concentration of red blood cells was elevated after Shuttle flights and reduced after Skylab flights. Reticulocyte count was decreased after both short- and long-term flights, indicating that a reduction in red blood cell mass is probably more closely related to suppression of red cell production than to an increase in destruction of erythrocytes. Serum ferritin and number of platelets were also elevated after Shuttle flights. In determining the reasons for postflight differences between the shorter and longer flights, it is important to consider not only duration but also countermeasures, differences between spacecraft, and procedures for landing and egress.
Bigelow, James; Poremba, Amy
Many human behaviors are known to benefit from audiovisual integration, including language and communication, recognizing individuals, social decision making, and memory. Exceptionally little is known about the contributions of audiovisual integration to behavior in other primates. The current experiment investigated whether short-term memory in nonhuman primates is facilitated by the audiovisual presentation format. Three macaque monkeys that had previously learned an auditory delayed matching-to-sample (DMS) task were trained to perform a similar visual task, after which they were tested with a concurrent audiovisual DMS task with equal proportions of auditory, visual, and audiovisual trials. Parallel to outcomes in human studies, accuracy was higher and response times were faster on audiovisual trials than either unisensory trial type. Unexpectedly, two subjects exhibited superior unimodal performance on auditory trials, a finding that contrasts with previous studies, but likely reflects their training history. Our results provide the first demonstration of a bimodal memory advantage in nonhuman primates, lending further validation to their use as a model for understanding audiovisual integration and memory processing in humans.
In 2010, the share of greenhouse gas (GHG) emissions from power generation in the total emissions at the global level was about 25 percent. From January 1st, 2013 Croatian facilities have been involved in the European Union Emissions Trading System (EU ETS). The share of the ETS sector in total GHG emissions in Croatia in 2012 was about 30 percent, where power plants and heat generation facilities contributed to almost 50 percent. Since 2013 power plants are obliged to purchase all emission allowances. The paper describes the short-term climate forecasting model of greenhouse gas emissions from power plants while covering the daily load diagram of the system. Forecasting is done on an hourly domain typically for one day, it is possible and more days ahead. Forecasting GHG emissions in this way would enable power plant operators to purchase additional or sell surplus allowances on the market at the time. Example that describes the operation of the above mentioned forecasting model is given at the end of the paper.(author).
The forecast period for this issue of the Outlook extends from the first quarter of 1998 through the fourth quarter of 1999. Values for the fourth quarter of 1997, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the first quarter 1998 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are adjusted by EIA to reflect EIA assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 24 figs., 19 tabs.
Although optimism prevailed in the 1970s, the present consensus on earthquake prediction appears to be quite pessimistic. However, short-term prediction based on geoelectric potential monitoring has stood the test of time in Greece for more than a decade [VarotsosandKulhanek, 1993] Lighthill, 1996]. The method used is called the VAN method.The geoelectric potential changes constantly due to causes such as magnetotelluric effects, lightning, rainfall, leakage from manmade sources, and electrochemical instabilities of electrodes. All of this noise must be eliminated before preseismic signals are identified, if they exist at all. The VAN group apparently accomplished this task for the first time. They installed multiple short (100-200m) dipoles with different lengths in both north-south and east-west directions and long (1-10 km) dipoles in appropriate orientations at their stations (one of their mega-stations, Ioannina, for example, now has 137 dipoles in operation) and found that practically all of the noise could be eliminated by applying a set of criteria to the data.
The utilization of periphyton for the removal of heavy metals from enriched small streams has been examined. By means of short-term batch laboratory experiments the courses of metal uptake have been studied. For uptake study naturally growing periphyton community and periphytic filamentous algae Cladophora glomerata and Oedogonium rivulare have been used. Uptakes of nine heavy metals (Pb, Cd, Cu, Co, Cr, Ni, Zn, Fe and Mn) have been determined during four hours exposure. In addition the influence of humic substances on heavy metals uptake has been determined. Uptake of all metals increased during four hours exposure but not in the same way. Some metals were removed continuously (Ni, Cr, Fe and Mn), other metals were removed more rapidly during the first hour or first two hours of exposure and then only slight removal continued (Cu, Pb, Cd, Co). Uptake of Zn was rather unambiguous. Results of these experiments suggest that the course of uptake for individual metals could be similar for most periphyton algae. It was established that humic substances significantly reduce heavy metals uptake. The highest decrease of uptake was observed in Cu, Cr, Co and Cd. The results of model experiments are being tested in a pilot scale with respect to the demands of engineering practice. (J.R.)
Phillips, B.J.; Elias, P.S.
Although traditional long-term animal tests are likely to continue to be required, these are not only extremely costly but are coming more and more to be recognised as an imprecise and unsatisfactory method of testing the safety of irradiated foods for human consumption. It is therefore clearly advisable to include a selection of quicker and more direct testing methods in any toxicological assessment procedures. The International Project has therefore undertaken a study of the feasibility of using the newer systems for investigation of irradiated foodstuffs. Although some work in this field has already been carried out, some shortcomings in the published work can be identified which justify a more detailed and intensive research programme. As expected, little difficulty has been encountered in testing food by methods involving mammals, but considerable effort has been required to adapt in vitro systems. The use of enzymatic digestion in vitro to provide food samples for testing in mammalian cell cultures has never been attempted before and the procedures developed by the Project represent a positive contribution to methodology in this field. A series of foodstuffs is being tested by a wide spectrum of short-term tests and the first results are now being obtained. (orig./MG) [de
Wang Jinwen [School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)], E-mail: firstname.lastname@example.org
The Fujian hydropower system (FHS) is one of the provincial hydropower systems with the most complicated hydraulic topology in China. This paper describes an optimization program that is required by Fujian Electric Power Company Ltd. (FEPCL) to aid the shift engineers in making decisions with the short-term hydropower scheduling such that the generation benefit can be maximal. The problem involves 27 reservoirs and is formulated as a nonlinear and discrete programming. It is a very challenging task to solve such a large-scale problem. In this paper, the Lagrangian multipliers are introduced to decompose the primal problem into a hydro subproblem and many individual plant-based subproblems, which are respectively solved by the improved simplex-like method (SLM) and the dynamic programming (DP). A numerical example is given and the derived solution is very close to the optimal one, with the distance in benefit less than 0.004%. All the data needed for the numerical example are presented in detail for further tests and studies from more experts and researchers.
Wang, Jinwen [School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)
The Fujian hydropower system (FHS) is one of the provincial hydropower systems with the most complicated hydraulic topology in China. This paper describes an optimization program that is required by Fujian Electric Power Company Ltd. (FEPCL) to aid the shift engineers in making decisions with the short-term hydropower scheduling such that the generation benefit can be maximal. The problem involves 27 reservoirs and is formulated as a nonlinear and discrete programming. It is a very challenging task to solve such a large-scale problem. In this paper, the Lagrangian multipliers are introduced to decompose the primal problem into a hydro subproblem and many individual plant-based subproblems, which are respectively solved by the improved simplex-like method (SLM) and the dynamic programming (DP). A numerical example is given and the derived solution is very close to the optimal one, with the distance in benefit less than 0.004%. All the data needed for the numerical example are presented in detail for further tests and studies from more experts and researchers. (author)
Mitsven, Samantha G; Cantrell, Lisa M; Luck, Steven J; Oakes, Lisa M
Adults' visual attention is guided by the contents of visual short-term memory (VSTM). Here we asked whether 10-month-old infants' (N = 41) visual attention is also guided by the information stored in VSTM. In two experiments, we modified the one-shot change detection task (Oakes, Baumgartner, Barrett, Messenger, & Luck, 2013) to create a simplified cued visual search task to ask how information stored in VSTM influences where infants look. A single sample item (e.g., a colored circle) was presented at fixation for 500 ms, followed by a brief (300 ms) retention interval and then a test array consisting of two items, one on each side of fixation. One item in the test array matched the sample stimulus and the other did not. Infants were more likely to look at the non-matching item than at the matching item, demonstrating that the information stored rapidly in VSTM guided subsequent looking behavior. Copyright © 2018 Elsevier B.V. All rights reserved.
Pilling, Michael; Barrett, Doug J K
We investigated how dimension-based attention influences visual short-term memory (VSTM). This was done through examining the effects of cueing a feature dimension in two perceptual comparison tasks (change detection and sameness detection). In both tasks, a memory array and a test array consisting of a number of colored shapes were presented successively, interleaved by a blank interstimulus interval (ISI). In Experiment 1 (change detection), the critical event was a feature change in one item across the memory and test arrays. In Experiment 2 (sameness detection), the critical event was the absence of a feature change in one item across the two arrays. Auditory cues indicated the feature dimension (color or shape) of the critical event with 80 % validity; the cues were presented either prior to the memory array, during the ISI, or simultaneously with the test array. In Experiment 1, the cue validity influenced sensitivity only when the cue was given at the earliest position; in Experiment 2, the cue validity influenced sensitivity at all three cue positions. We attributed the greater effectiveness of top-down guidance by cues in the sameness detection task to the more active nature of the comparison process required to detect sameness events (Hyun, Woodman, Vogel, Hollingworth, & Luck, Journal of Experimental Psychology: Human Perception and Performance, 35; 1140-1160, 2009).
Roper, Zachary J J; Vecera, Shaun P
Perceptual load theory accounts for many attentional phenomena; however, its mechanism remains elusive because it invokes underspecified attentional resources. Recent dual-task evidence has revealed that a concurrent visual short-term memory (VSTM) load slows visual search and reduces contrast sensitivity, but it is unknown whether a VSTM load also constricts attention in a canonical perceptual load task. If attentional selection draws upon VSTM resources, then distraction effects-which measure attentional "spill-over"-will be reduced as competition for resources increases. Observers performed a low perceptual load flanker task during the delay period of a VSTM change detection task. We observed a reduction of the flanker effect in the perceptual load task as a function of increasing concurrent VSTM load. These findings were not due to perceptual-level interactions between the physical displays of the two tasks. Our findings suggest that perceptual representations of distractor stimuli compete with the maintenance of visual representations held in memory. We conclude that access to VSTM determines the degree of attentional selectivity; when VSTM is not completely taxed, it is more likely for task-irrelevant items to be consolidated and, consequently, affect responses. The "resources" hypothesized by load theory are at least partly mnemonic in nature, due to the strong correspondence they share with VSTM capacity.
Cleide A.S. Tirloni
Full Text Available Abstract Casearia sylvestris Sw., Salicaceae, is an important medicinal plant widely used in Brazil for the treatment of various cardiovascular disorders. This species was included as of interest by Brazilian Unified Health System. Although preclinical studies described cardiovascular protective effects and apparent absence of toxicity, no studies have evaluated its carcinogenic potential. In this study, we proposed a short-term carcinogenesis evaluation of C. sylvestris in Wistar rats, aiming to check the safety of this species to use it as proposed by Brazilian Unified Health System. C. sylvestris leaves were obtained and the crude extract was prepared by maceration from methanol/water. Wistar rats were orally treated for 12 weeks with 50, 250 or 500 mg kg−1 of crude extract or vehicle. Body weight, daily morbidity and mortality were monitored. Blood and bone marrow samples were collect for micronucleus test, comet assay and tumor markers evaluation. Vital organs were removed to macro and histopathological analyses. The crude extract did not induce mutagenic and genotoxic effects and no alterations were observed in important tumor markers. Finally, no detectable signs of injury through gross pathology or histopathological examinations were observed. Our results certify the absence of the crude extract toxicity, indicating its safety, even at prolonged exposure as proposed by Brazilian Unified Health System.
Marquet, Laurie-Anne; Hausswirth, Christophe; Molle, Odeline; Hawley, John A.; Burke, Louise M.; Tiollier, Eve; Brisswalter, Jeanick
Background: “Sleep-low” consists of a sequential periodization of carbohydrate (CHO) availability—low glycogen recovery after “train high” glycogen-depleting interval training, followed by an overnight-fast and light intensity training (“train low”) the following day. This strategy leads to an upregulation of several exercise-responsive signaling proteins, but the chronic effect on performance has received less attention. We investigated the effects of short-term exposure to this strategy on endurance performance. Methods: Following training familiarization, 11 trained cyclists were divided into two groups for a one-week intervention—one group implemented three cycles of periodized CHO intake to achieve the sleep-low strategy over six training sessions (SL, CHO intake: 6 g·kg−1·day−1), whereas the control group consumed an even distribution of CHO over the day (CON). Tests were a 2 h submaximal ride and a 20 km time trial. Results: SL improved their performance (mean: +3.2%; p < 0.05) compared to CON. The improvement was associated with a change in pacing strategy with higher power output during the second part of the test. No change in substrate utilization was observed after the training period for either group. Conclusion: Implementing the “sleep-low” strategy for one week improved performance by the same magnitude previously seen in a three-week intervention, without any significant changes in selected markers of metabolism. PMID:27897989
Liang, Y; Sun, Y; Ren, L; Qi, X-W; Li, Y; Zhang, F
We wished to explore short-term efficacy of surgical treatment of secondary hyperparathyroidism in patients with the end-stage renal disease. The treatment methods were subtotal or total parathyroidectomy, or total parathyroidectomy and autotransplantation. 63 patients with secondary hyperparathyroidism were randomly divided into three groups which were respectively treated with subtotal parathyroidectomy (SPTX group), total parathyroidectomy (TPTX group), or total parathyroidectomy and autotransplantation (TPTX+AT group). The surgical outcomes included operating time, transoperative bleeding volume, length of stay, and cost of hospitalization. In addition, complication (e.g., postoperative wound infection, hematoma, hypocalcemia in perioperative period) rates were compared among groups. Blood levels of calcium and parathyroid hormone were assessed before the surgery, and 1 day, 1 months, 3 months and 6 months after the surgery. The follow-up period comprised 6 months. Surgical outcomes were the lowest in SPTX group and the highest in in TPTX+AT group. There were no significant differences among groups in treatment efficacy. Complication rates were also comparable among the three groups. The occurrence of hypocalcemia was the lowest in SPTX group (p < 0.05 vs. other groups). However, postoperative relapse rate was the highest in this group (p < 0.05 vs. other groups). There were no correlations between the levels of blood calcium and PTH preoperatively and postoperatively. Appropriate surgical treatment is selected in accordance with the patient's condition and willingness, with the attention paid to the prevention of hypocalcemia.
Al Azawi, Mayce
The goal of this work is to develop statistical natural language models and processing techniques based on Recurrent Neural Networks (RNN), especially the recently introduced Long Short- Term Memory (LSTM). Due to their adapting and predicting abilities, these methods are more robust, and easier to train than traditional methods, i.e., words list and rule-based models. They improve the output of recognition systems and make them more accessible to users for browsing and reading...
Full Text Available The diet is essential to the recovery process in athletes, especially those undergoing intensive training. The continuous imbalance between loading and recovery leads to development of overtraining syndrome. The purpose of this study was to establish the changes in the nutritional status of short-term overtrained athletes. Twelve boxers from the team of National Spoils Academy Sofia, Bulgaria during their preparation for the National Championship 2016 were studied. The measurements were conducted three times.in the beginning of preparation (T1, 22 days later (2 and 10 days after (32 days after first measurement, in the beginning of the recovery period, one week prior the competition (T3.The measurements included basic anthropometric data, overtraining questionnaire RESTO-Sport and nutrition questionnaire, plasma concentration of testosterone and cortisol.On the data of dietary survey the percent proportion and the amount of daily consumed proteins, fats and carbohydrates were defined and the energy intake of the tested athletes was calculated. According to the RESTO-Sport a significant decrease in the ratio stress/recovery was observed in the period with the heaviest training load T2, and an increase was estimated in the precompetition recovery period T3. It was found a typical for the overtraining syndrome decrease in the concentration of testosterone and the ratio of testosterone/cortisol in T3. In some respondents a reduction in carbohydrates and proteins intake was observed in T2 and especially in T3, which correlates with the hormonal changes. In this work the diet changes was discussed as a possible consequence and/or a cause of the overtraining syndrome.
Alexandrova, Albena; Petrov, Lubomir; Zaekov, Nikolay; Bozhkov, Borislav; Zsheliaskova-Koynova, Zshivka
The diet is essential to the recovery process in athletes, especially those undergoing intensive training. The continuous imbalance between loading and recovery leads to development of overtraining syndrome. The purpose of this study was to establish the changes in the nutritional status of short-term overtrained athletes. Twelve boxers from the team of National Spoils Academy Sofia, Bulgaria during their preparation for the National Championship 2016 were studied. The measurements were conducted three times.in the beginning of preparation (T1), 22 days later (2) and 10 days after (32 days after first measurement), in the beginning of the recovery period, one week prior the competition (T3).The measurements included basic anthropometric data, overtraining questionnaire RESTO-Sport and nutrition questionnaire, plasma concentration of testosterone and cortisol.On the data of dietary survey the percent proportion and the amount of daily consumed proteins, fats and carbohydrates were defined and the energy intake of the tested athletes was calculated. According to the RESTO-Sport a significant decrease in the ratio stress/recovery was observed in the period with the heaviest training load T2, and an increase was estimated in the precompetition recovery period T3. It was found a typical for the overtraining syndrome decrease in the concentration of testosterone and the ratio of testosterone/cortisol in T3. In some respondents a reduction in carbohydrates and proteins intake was observed in T2 and especially in T3, which correlates with the hormonal changes. In this work the diet changes was discussed as a possible consequence and/or a cause of the overtraining syndrome.
Full Text Available From birth onwards, the human gut microbiota rapidly increases in diversity and reaches an adult-like stage at three years of age. After this age, the composition may fluctuate in response to external factors such as antibiotics. Previous studies have shown that resilience is not complete months after cessation of the antibiotic intake. However, little is known about the short-term effects of antibiotic intake on the gut microbial community. Here we examined the load and composition of the fecal microbiota immediately after treatment in 21 patients, who received broad-spectrum antibiotics such as fluoroquinolones and β-lactams. A fecal sample was collected from all participants before treatment and one week after for microbial load and community composition analyses by quantitative PCR and pyrosequencing of the 16S rRNA gene, respectively. Fluoroquinolones and β-lactams significantly decreased microbial diversity by 25% and reduced the core phylogenetic microbiota from 29 to 12 taxa. However, at the phylum level, these antibiotics increased the Bacteroidetes/Firmicutes ratio (p = 0.0007, FDR = 0.002. At the species level, our findings unexpectedly revealed that both antibiotic types increased the proportion of several unknown taxa belonging to the Bacteroides genus, a Gram-negative group of bacteria (p = 0.0003, FDR<0.016. Furthermore, the average microbial load was affected by the treatment. Indeed, the β-lactams increased it significantly by two-fold (p = 0.04. The maintenance of or possible increase detected in microbial load and the selection of Gram-negative over Gram-positive bacteria breaks the idea generally held about the effect of broad-spectrum antibiotics on gut microbiota.
Building up and producing hydro power causes environmental effects, which are directed at the geomorfology, hydrology, water quality, organisms and landscape of the water system. To reduce and eliminate these various effects there are available an abundance of technical remedial measures, many of which contribute to several effects at the same time. In Finland a lot of remedial measures have been carried out at voluntary or obligatory bases. The information concerning remedial measures implemented in large build-up rivers were collected as a part of the study of the effects of the short-term regulation of hydro power plants. Material for the study was collected via literature, postal inquiry and terrain visits. Measures handled in the study were protection and reinforcement of shores, boating projects, submerged weirs, improvement of water turnover, fishery, clearing of peat rafts and stubs, landscaping, maintaining ice roads and shaping river banks. Nowadays planning and implementation of the remedial measures varies greatly depending on the nature and extent of the project. Large projects, which are more expensive, are naturally planned more carefully and comprehensively than simple routine measures. Also the quality of follow-up of the sites changes and the main portion of the information is received through terrain checks and direct feed-back from the users of the water system. In the future there is a need for model plans of the different routine measures. Also a systemic method to evaluate and compare different actions is needed to help decision making and to solve possible conflicts between different interests. Fishery, which is generally managed well, must in the future utilize better possibilities offered by other measures. According to the study there is no particular need to develop the follow-up systems. However, if the follow-up information is going to be used to develop the measures further, more systematic systems are needed for follow-up. (author)
Xie, Weizhen; Zhang, Weiwei
Existing long-term memory (LTM) can boost the number of retained representations over a short delay in visual short-term memory (VSTM). However, it is unclear whether and how prior LTM affects the initial process of transforming fragile sensory inputs into durable VSTM representations (i.e., VSTM consolidation). The consolidation speed hypothesis predicts faster consolidation for familiar relative to unfamiliar stimuli. Alternatively, the perceptual boost hypothesis predicts that the advantage in perceptual processing of familiar stimuli should add a constant boost for familiar stimuli during VSTM consolidation. To test these competing hypotheses, the present study examined how the large variance in participants' prior multimedia experience with Pokémon affected VSTM for Pokémon. In Experiment 1, the amount of time allowed for VSTM consolidation was manipulated by presenting consolidation masks at different intervals after the onset of to-be-remembered Pokémon characters. First-generation Pokémon characters that participants were more familiar with were consolidated faster into VSTM as compared with recent-generation Pokémon characters that participants were less familiar with. These effects were absent in participants who were unfamiliar with both generations of Pokémon. Although familiarity also increased the number of retained Pokémon characters when consolidation was uninterrupted but still incomplete due to insufficient encoding time in Experiment 1, this capacity effect was absent in Experiment 2 when consolidation was allowed to complete with sufficient encoding time. Together, these results support the consolidation speed hypothesis over the perceptual boost hypothesis and highlight the importance of assessing experimental effects on both processing and representation aspects of VSTM. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Delkash, Madjid; Zhou, Bowen; Han, Byunghyun; Chow, Fotini K; Rella, Chris W; Imhoff, Paul T
Short-term (2-10h) variations of whole-landfill methane emissions have been observed in recent field studies using the tracer dilution method for emissions measurement. To investigate the cause of these variations, the tracer dilution method is applied using 1-min emissions measurements at Sandtown Landfill (Delaware, USA) for a 2-h measurement period. An atmospheric dispersion model is developed for this field test site, which is the first application of such modeling to evaluate atmospheric effects on gas plume transport from landfills. The model is used to examine three possible causes of observed temporal emissions variability: temporal variability of surface wind speed affecting whole landfill emissions, spatial variability of emissions due to local wind speed variations, and misaligned tracer gas release and methane emissions locations. At this site, atmospheric modeling indicates that variation in tracer dilution method emissions measurements may be caused by whole-landfill emissions variation with wind speed. Field data collected over the time period of the atmospheric model simulations corroborate this result: methane emissions are correlated with wind speed on the landfill surface with R(2)=0.51 for data 2.5m above ground, or R(2)=0.55 using data 85m above ground, with emissions increasing by up to a factor of 2 for an approximately 30% increase in wind speed. Although the atmospheric modeling and field test are conducted at a single landfill, the results suggest that wind-induced emissions may affect tracer dilution method emissions measurements at other landfills. Copyright © 2016 Elsevier Ltd. All rights reserved.
Lin, Cheng-Ting; Chou, Li-Der
Highlights: ► We combine MA line of TAIEX and SVR to overcome the load demands over-prediction problems caused by the economic downturn. ► The Taiwan island-wide electricity power system was used as the case study. ► Short- to middle-term MA lines of TAIEX are found to be good economic input variables for load forecasting models. - Abstract: The global economic downturn in 2008 and 2009, which was spurred by the bankruptcy of Lehman Brothers, sharply reduced the demand for electricity load. Conventional load-forecasting approaches were unable to respond to sudden changes in the economy, because these approaches do not consider the effect of economic factors. Therefore, the over-prediction problem occurred. To overcome this problem, this paper proposes a novel, economy-reflecting, short-term load forecasting (STLF) approach based on theories of moving average (MA) line of stock index and machine learning. In this approach, the stock indices decision model is designed to reflect fluctuations in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) series, which is selected as an optimal input variable in support vector regression load forecasting model at an appropriate timing. The Taiwan island-wide hourly electricity load demands from 2008 to 2010 are used as the case study for performance benchmarking. Results show that the proposed approach with a 60-day MA of the TAIEX as economic learning pattern achieves good forecasting performance. It outperforms the conventional approach by 29.16% on average during economic downturn-affected days. Overall, the proposed approach successfully overcomes the over-prediction problems caused by the economic downturn. To the best of our knowledge, this paper is the first attempt to apply MA line theory of stock index on STLF.
Full Text Available Previous studies have shown that in vitro thyroid peroxidase (TPO iodide oxidation activity is decreased and thyroid T4-5'-deiodinase activity is increased 15 days after induction of experimental diabetes mellitus (DM. In the present study we used thyroid histoautoradiography, an indirect assay of in vivo TPO activity, to determine the possible parallelism between the in vitro and in vivo changes induced by experimental DM. DM was induced in male Wistar rats (about 250 g body weight by a single ip streptozotocin injection (45 mg/kg, while control (C animals received a single injection of the vehicle. Seven and 30 days after diabetes induction, each diabetic and control animal was given ip a tracer dose of 125I (2 µCi, 2.5 h before thyroid excision. The glands were counted, weighed, fixed in Bouin's solution, embedded in paraffin and cut. The sections were stained with HE and exposed to NTB-2 emulsion (Kodak. The autohistograms were developed and the quantitative distribution of silver grains was evaluated with a computerized image analyzer system. Thyroid radioiodine uptake was significantly decreased only after 30 days of DM (C: 0.38 ± 0.05 vs DM: 0.20 ± 0.04%/mg thyroid, P<0.05 while in vivo TPO activity was significantly decreased 7 and 30 days after DM induction (C: 5.3 and 4.5 grains/100 µm2 vs DM: 2.9 and 1.6 grains/100 µm2, respectively, P<0.05 . These data suggest that insulin deficiency first reduces in vivo TPO activity during short-term experimental diabetes mellitus
Su, Yuling; Lai, Yunpeng; Huang, Wanyi; Tan, Wei; Qu, Zhe; Ding, Yulong
Although some studies showed that training can improve the ability of cross-dimension conjunction search, less is known about the underlying mechanism. Specifically, it remains unclear whether training of visual conjunction search can successfully bind different features of separated dimensions into a new function unit at early stages of visual processing. In the present study, we utilized stimulus specificity and generalization to provide a new approach to investigate the mechanisms underlying perceptual learning (PL) in visual conjunction search. Five experiments consistently showed that after 40 to 50 min of training of color-shape/orientation conjunction search, the ability to search for a certain conjunction target improved significantly and the learning effects did not transfer to a new target that differed from the trained target in both color and shape/orientation features. However, the learning effects were not strictly specific. In color-shape conjunction search, although the learning effect could not transfer to a same-shape different-color target, it almost completely transferred to a same-color different-shape target. In color-orientation conjunction search, the learning effect partly transferred to a new target that shared same color or same orientation with the trained target. Moreover, the sum of transfer effects for the same color target and the same orientation target in color-orientation conjunction search was algebraically equivalent to the learning effect for trained target, showing an additive transfer effect. The different transfer patterns in color-shape and color-orientation conjunction search learning might reflect the different complexity and discriminability between feature dimensions. These results suggested a feature-based attention enhancement mechanism rather than a unitization mechanism underlying the short-term PL of color-shape/orientation conjunction search.
Cieliński, Łukasz; Kusz, Damian; Wojciechowski, Piotr; Dziuba, Anna
This paper discusses the authors' experience with hip resurfacing arthroplasty. Although introduced many years ago, the method did not gain wide popularity because of poor long-term outcomes. At present, owing to the introduction of metal-on-metal bearings and hybrid fixation techniques, short- and mid-term results are very good and encourage wider use of this technique, especially in the younger and more active patients whose results with standard total hip replacements would be unsatisfactory. We performed 13 hip resurfacing arthroplasties at our institution between August 1, 2005, and May 1, 2006. Twelve patients reported for the scheduled follow-up and were included in the study. Treatment outcomes were assessed according to the Harris Hip Score. The short-term outcomes of hip resurfacing arthroplasties are encouraging. In the study group there were no intraoperative complications, infections, peripheral nerve palsy, hip dislocations or clinically overt vein thrombosis. All of the patients reported complete or major pain relief. Clinical assessment according to the Harris Hip Score revealed improvement from an average of 57.7 (20.1) points preoperatively to an average of 87.7 (12) points after the surgery. Crutches were used for a maximum of 6 weeks postoperatively. All of the patients are currently able to walk without crutches with full weight-bearing. 1) Hip resurfacing arthroplasty seems to be an advisable method of operative management of younger, active patients, in whom standard THR would be associated with a high risk of failure; it allows THR to be postponed and carried out as a revision surgery with the acetabular component already in place. 2) Despite the good short- and mid-term results, the utility of this method should be evaluated with caution due to the lack of adequate long-term follow-up data.
Potter, Mary C
Conceptual short term memory (CSTM) is a theoretical construct that provides one answer to the question of how perceptual and conceptual processes are related. CSTM is a mental buffer and processor in which current perceptual stimuli and their associated concepts from long term memory (LTM) are represented briefly, allowing meaningful patterns or structures to be identified (Potter, 1993, 1999, 2009). CSTM is different from and complementary to other proposed forms of working memory: it is engaged extremely rapidly, has a large but ill-defined capacity, is largely unconscious, and is the basis for the unreflective understanding that is characteristic of everyday experience. The key idea behind CSTM is that most cognitive processing occurs without review or rehearsal of material in standard working memory and with little or no conscious reasoning. When one perceives a meaningful stimulus such as a word, picture, or object, it is rapidly identified at a conceptual level and in turn activates associated information from LTM. New links among concurrently active concepts are formed in CSTM, shaped by parsing mechanisms of language or grouping principles in scene perception and by higher-level knowledge and current goals. The resulting structure represents the gist of a picture or the meaning of a sentence, and it is this structure that we are conscious of and that can be maintained in standard working memory and consolidated into LTM. Momentarily activated information that is not incorporated into such structures either never becomes conscious or is rapidly forgotten. This whole cycle - identification of perceptual stimuli, memory recruitment, structuring, consolidation in LTM, and forgetting of non-structured material - may occur in less than 1 s when viewing a pictured scene or reading a sentence. The evidence for such a process is reviewed and its implications for the relation of perception and cognition are discussed.
Mary C. Potter
Full Text Available Conceptual short term memory (CSTM is a theoretical construct that provides one answer to the question of how perceptual and conceptual processes are related. CSTM is a mental buffer and processor in which current perceptual stimuli and their associated concepts from long term memory (LTM are represented briefly, allowing meaningful patterns or structures to be identified (Potter, 1993, 1999, 2009. CSTM is different from and complementary to other proposed forms of working memory: it is engaged extremely rapidly, has a large but ill-defined capacity, is largely unconscious, and is the basis for the unreflective understanding that is characteristic of everyday experience. The key idea behind CSTM is that most cognitive processing occurs without review or rehearsal of material in standard working memory and with little or no conscious reasoning. When one perceives a meaningful stimulus such as a word, picture, or object, it is rapidly identified at a conceptual level and in turn activates associated information from long term memory. New links among concurrently active concepts are formed in CSTM, shaped by parsing mechanisms of language or grouping principles in scene perception and by higher-level knowledge and current goals. The resulting structure represents the gist of a picture or the meaning of a sentence, and it is this structure that we are conscious of and that can be maintained in standard working memory and consolidated into long term memory. Momentarily activated information that is not incorporated into such structures either never becomes conscious or is rapidly forgotten. This whole cycle--identification of perceptual stimuli, memory recruitment, structuring, consolidation in long term memory, and forgetting of nonstructured material--may occur in less than 1 second when viewing a pictured scene or reading a sentence. The evidence for such a process is reviewed and its implications for the relation of perception and cognition are
Tisthammer, K.; Richmond, R. H.
Corals in Maunalua Bay, Hawaii are under chronic pressures from sedimentation and terrestrial runoffs containing multiple pollutants as a result of large scale urbanization that has taken place in the last 100 years. However, some individual corals thrive despite the prolonged exposure to these environmental stressors, which suggests that these individuals may have adapted to withstand such stressors. A recent survey showed that the lobe coral Porites lobata from the `high-stress' nearshore site had an elevated level of stress ixnduced proteins, compared to those from the `low-stress,' less polluted offshore site. To understand the genetic basis for the observed differential stress responses between the nearshore and offshore P. lobata populations, an analysis of the lineage-scale population genetic structure, as well as a reciprocal transplant experiment were conducted. The result of the genetic analysis revealed a clear genetic differentiation between P. lobata from the nearshore site and the offshore site. Following the 30- day reciprocal transplant experiment, protein expression profiles and other stress-related physiological characteristics were compared between the two populations. The experimental results suggest that the nearshore genotype can cope better with sedimentation/pollutants than the offshore genotype. This indicates that the observed genetic differentiation is due to selection for tolerance to these environmental stressors. Understanding the little-known, linage-scale genetic variation in corals offers a critical insight into their short-term adaptive ability, which is indispensable for protecting corals from impending environmental and climate change. The results of this study also offer a valuable tool for resource managers to make effective decisions on coral reef conservation, such as designing marine protected areas that incorporate and maintain such genetic diversity, and establishing acceptable pollution run-off levels.
Chen, Kuilin; Yu, Jie
Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations
M. Estrella Santamaria
Full Text Available Plant–pest relationships involve complex processes encompassing a network of molecules, signals, and regulators for overcoming defenses they develop against each other. Phytophagous arthropods identify plants mainly as a source of food. In turn, plants develop a variety of strategies to avoid damage and survive. The success of plant defenses depends on rapid and specific recognition of the phytophagous threat. Subsequently, plants trigger a cascade of short-term responses that eventually result in the production of a wide range of compounds with defense properties. This review deals with the main features involved in the interaction between plants and phytophagous insects and acari, focusing on early responses from the plant side. A general landscape of the diverse strategies employed by plants within the first hours after pest perception to block the capability of phytophagous insects to develop mechanisms of resistance is presented, with the potential of providing alternatives for pest control.
Mills, Andrew; Wiser, Ryan
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
Valori, Luca; Picciolo, Francesco; Allansdottir, Agnes; Garlaschelli, Diego
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.
Ljungman, Petter L.; Wilker, Elissa H.; Gold, Diane R.; Schwartz, Joel D.; Koutrakis, Petros; Washko, George R.; O’Connor, George T.; Mittleman, Murray A.
Rationale: Short-term exposure to ambient air pollution has been associated with lower lung function. Few studies have examined whether these associations are detectable at relatively low levels of pollution within current U.S. Environmental Protection Agency (EPA) standards. Objectives: To examine exposure to ambient air pollutants within EPA standards and lung function in a large cohort study. Methods: We included 3,262 participants of the Framingham Offspring and Third Generation cohorts living within 40 km of the Harvard Supersite monitor in Boston, Massachusetts (5,358 examinations, 1995–2011) who were not current smokers, with previous-day pollutant levels in compliance with EPA standards. We compared lung function (FEV1 and FVC) after previous-day exposure to particulate matter less than 2.5 μm in diameter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in the “moderate” range of the EPA Air Quality Index to exposure in the “good” range. We also examined linear relationships between moving averages of pollutant concentrations 1, 2, 3, 5, and 7 days before spirometry and lung function. Measurements and Main Results: Exposure to pollutant concentrations in the “moderate” range of the EPA Air Quality Index was associated with a 20.1-ml lower FEV1 for PM2.5 (95% confidence interval [CI], −33.4, −6.9), a 30.6-ml lower FEV1 for NO2 (95% CI, −60.9, −0.2), and a 55.7-ml lower FEV1 for O3 (95% CI, −100.7, −10.8) compared with the “good” range. The 1- and 2-day moving averages of PM2.5, NO2, and O3 before testing were negatively associated with FEV1 and FVC. Conclusions: Short-term exposure to PM2.5, NO2, and O3 within current EPA standards was associated with lower lung function in this cohort of adults. PMID:24200465
Shah Baki, S.R.; Saibon, H.; Lo, K.L.
A combination of Kalman-Fuzzy-Neural is developed to forecast the next 24 hours load. The input data fed to neural network are presented with training data set composed of historical load data, weather, day of the week, month of the year and holidays. The load data is fed through Kalman-Fuzzy filter before being applied to Neural Network for training. With this techniques Neural Network converges faster and the mean percentage error of predicted load is reduced as compared to the classical ANN technique
Rhoten, William B. (Principal Investigator); Sergeev, Igor N. (Principal Investigator)
The exposure of the body to microgravity during space flight causes a series of well-documented changes in Ca(2+) metabolism, yet the cellular/molecular mechanisms leading to these changes are poorly understood. There is some evidence for microgravity-induced alterations in the vitamin D endocrine system, which is known to be primarily involved in the regulation of Ca(2+) metabolism. Vitamin D-dependent Ca(2+) binding proteins, or calbindins, are believed to have a significant role in maintaining cellular Ca(2+) homeostasis. We used immunocytochemical, biochemical and molecular approaches to analyze the expression of calbindin-D(sub 28k) and calbindin-D(sub 9k) in kidneys and intestines of rats flown for 9 days aboard the Spacelab 3 mission. The effects of microgravity on calbindins in rats in space vs. 'grounded' animals (synchronous Animal Enclosure Module controls and tail suspension controls) were compared. Exposure to microgravity resulted in a significant decrease in calbindin-D(sub 28k) content in kidneys and calbindin-D(sub 9k) in the intestine of flight and suspended animals, as measured by enzyme-linked immunosorbent assay (ELISA). Immunocytochemistry (ICC) in combination with quantitative computer image analysis was used to measure in situ the expression of calbindins in kidneys and intestine, and insulin in pancreas. There was a large decrease in the distal tubular cell-associated calbindin-D(sub 28k) and absorptive cell-associated calbindin-D(sub 9k) immunoreactivity in the space and suspension kidneys and intestine, as compared with matched ground controls. No consistent differences in pancreatic insulin immunoreactivity between space, suspension and ground controls was observed. There were significant correlations between results by quantitative ICC and ELISA. Western blot analysis showed no consistent changes in the low levels of intestinal and renal vitamin D receptors. These findings suggest that a decreased expression of calbindins after a short-term
Saur, Günter; Krüger, Wolfgang
In the last years, there has been an increased use of unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. An important application in this context is change detection in UAV video data. Here we address short-term change detection, in which the time between observations ranges from several minutes to a few hours. We distinguish this task from video motion detection (shorter time scale) and from long-term change detection, based on time series of still images taken between several days, weeks, or even years. Examples for relevant changes we are looking for are recently parked or moved vehicles. As a pre-requisite, a precise image-to-image registration is needed. Images are selected on the basis of the geo-coordinates of the sensor's footprint and with respect to a certain minimal overlap. The automatic imagebased fine-registration adjusts the image pair to a common geometry by using a robust matching approach to handle outliers. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed length of shadows, and compression or transmission artifacts. To detect changes in image pairs we analyzed image differencing, local image correlation, and a transformation-based approach (multivariate alteration detection). As input we used color and gradient magnitude images. To cope with local misalignment of image structures we extended the approaches by a local neighborhood search. The algorithms are applied to several examples covering both urban and rural scenes. The local neighborhood search in combination with intensity and gradient magnitude differencing clearly improved the results. Extended image differencing performed better than both the correlation based approach and the multivariate alternation detection. The algorithms are adapted to be used in semi-automatic workflows for the ABUL video exploitation system of Fraunhofer
Yang, Xiaofeng; Bognar, Joseph; He, Tianyu; Mohammed, Mouhari; Niespodziany, Isabelle; Wolff, Christian; Esguerra, Manuel; Rothman, Steven M; Dubinsky, Janet M
Brivaracetam (BRV) decreases seizure activity in a number of epilepsy models and binds to the synaptic vesicle glycoprotein 2A (SV2A) with a higher affinity than the antiepileptic drug levetiracetam (LEV). Experiments were performed to determine if BRV acted similarly to LEV to induce or augment short-term depression (STD) under high-frequency neuronal stimulation and slow synaptic vesicle recycling. Electrophysiologic field excitatory postsynaptic potential (fEPSP) recordings were made from CA1 synapses in rat hippocampal slices loaded with BRV or LEV during intrinsic activity or with BRV actively loaded during hypertonic stimulation. STD was examined in response to 5 or 40 Hz stimulus trains. Presynaptic release of FM1-43 was visualized using two-photon microscopy to assess drug effects upon synaptic vesicle mobilization. When hippocampal slices were incubated in 0.1-30 μm BRV or 30 μm-1 mm LEV for 3 h, the relative CA1 field EPSPs decreased over the course of a high-frequency train of stimuli more than for control slices. This STD was frequency- and concentration-dependent, with BRV being 100-fold more potent than LEV. The extent of STD depended on the length of the incubation time for both drugs. Pretreatment with LEV occluded the effects of BRV. Repeated hypertonic sucrose treatments and train stimulation successfully unloaded BRV from recycling vesicles and reversed BRVs effects on STD, as previously reported for LEV. At their maximal concentrations, BRV slowed FM1-43 release to a greater extent than in slices loaded with LEV during prolonged stimulation. BRV, similar to LEV, entered into recycling synaptic vesicles and produced a frequency-dependent decrement of synaptic transmission at 100-fold lower concentrations than LEV. In addition, BRV slowed synaptic vesicle mobilization more effectively than LEV, suggesting that these drugs may modify multiple functions of the synaptic vesicle protein SV2A to curb synaptic transmission and limit epileptic activity
Sligte, Ilja G; Scholte, H Steven; Lamme, Victor A F
Classic work on visual short-term memory (VSTM) suggests that people store a limited amount of items for subsequent report. However, when human observers are cued to shift attention to one item in VSTM during retention, it seems as if there is a much larger representation, which keeps additional items in a more fragile VSTM store. Thus far, it is not clear whether the capacity of this fragile VSTM store indeed exceeds the traditional capacity limits of VSTM. The current experiments address this issue and explore the capacity, stability, and duration of fragile VSTM representations. We presented cues in a change-detection task either just after off-set of the memory array (iconic-cue), 1,000 ms after off-set of the memory array (retro-cue) or after on-set of the probe array (post-cue). We observed three stages in visual information processing 1) iconic memory with unlimited capacity, 2) a four seconds lasting fragile VSTM store with a capacity that is at least a factor of two higher than 3) the robust and capacity-limited form of VSTM. Iconic memory seemed to depend on the strength of the positive after-image resulting from the memory display and was virtually absent under conditions of isoluminance or when intervening light masks were presented. This suggests that iconic memory is driven by prolonged retinal activation beyond stimulus duration. Fragile VSTM representations were not affected by light masks, but were completely overwritten by irrelevant pattern masks that spatially overlapped the memory array. We find that immediately after a stimulus has disappeared from view, subjects can still access information from iconic memory because they can see an after-image of the display. After that period, human observers can still access a substantial, but somewhat more limited amount of information from a high-capacity, but fragile VSTM that is overwritten when new items are presented to the eyes. What is left after that is the traditional VSTM store, with a limit of
Ilja G Sligte
Full Text Available BACKGROUND: Classic work on visual short-term memory (VSTM suggests that people store a limited amount of items for subsequent report. However, when human observers are cued to shift attention to one item in VSTM during retention, it seems as if there is a much larger representation, which keeps additional items in a more fragile VSTM store. Thus far, it is not clear whether the capacity of this fragile VSTM store indeed exceeds the traditional capacity limits of VSTM. The current experiments address this issue and explore the capacity, stability, and duration of fragile VSTM representations. METHODOLOGY/PRINCIPAL FINDINGS: We presented cues in a change-detection task either just after off-set of the memory array (iconic-cue, 1,000 ms after off-set of the memory array (retro-cue or after on-set of the probe array (post-cue. We observed three stages in visual information processing 1 iconic memory with unlimited capacity, 2 a four seconds lasting fragile VSTM store with a capacity that is at least a factor of two higher than 3 the robust and capacity-limited form of VSTM. Iconic memory seemed to depend on the strength of the positive after-image resulting from the memory display and was virtually absent under conditions of isoluminance or when intervening light masks were presented. This suggests that iconic memory is driven by prolonged retinal activation beyond stimulus duration. Fragile VSTM representations were not affected by light masks, but were completely overwritten by irrelevant pattern masks that spatially overlapped the memory array. CONCLUSIONS/SIGNIFICANCE: We find that immediately after a stimulus has disappeared from view, subjects can still access information from iconic memory because they can see an after-image of the display. After that period, human observers can still access a substantial, but somewhat more limited amount of information from a high-capacity, but fragile VSTM that is overwritten when new items are presented
Pinal, Diego; Zurrón, Montserrat; Díaz, Fernando; Sauseng, Paul
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.
Bois de Fer, Béatrice; Host, Sabine; Chardon, Benoît; Chatignoux, Edouard; Beaujouan, Laure; Brun-Ney, Dominique; Grémy, Isabelle
The study of the short-term effects and health impact of air pollution is carrier out by the ERPURS regional surveillance program which utilizes hospitalization data obtained from the French hospital information system (PMSI) to determine these links. This system does not permit the distinction between emergency hospital admissions from scheduled ones, which cannot be related to short term changes in air pollution levels. This study examines how scheduled admissions affect the quality of the health indicators used to estimate air pollution effects. This indicator is compared to three new emergency hospitalisation indicators reconstructed based on data from the public hospitals in Paris, partly from the PMSI data and partly with data from an on-line emergency network that regroups all of the computerized emergency services. According to the pathology, scheduled admissions present a difficulty which affects the capacity to highlight the weakest risks with any precision.
David P. Schmitt
Full Text Available As part of the International Sexuality Description Project, 13,243 participants from 46 nations responded to self-report measures of personality and mating behavior. Several traits showed consistent links with short-term mating. Extraversion positively correlated with interest in short-term mating, unrestricted sociosexuality, having engaged in short-term mate poaching attempts, having succumbed to short-term poaching attempts of others, and lacking relationship exclusivity. Low levels of agreeableness and conscientiousness also related to short-term mating, especially with extra-pair mating. Neuroticism and openness were associated with short-term mating as well, but these links were less consistent across sex and nation. Nation-level links between personality and sexuality replicated within-region findings, such as the strong association between national extraversion and national sociosexuality. Discussion focuses on the origins of personality-sexuality links and their implications across nations.
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)
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
Gibson, Brett; Wasserman, Edward; Luck, Steven J.
Visual short-term memory plays a key role in guiding behavior, and individual differences in visual short-term memory capacity are strongly predictive of higher cognitive abilities. To provide a broader evolutionary context for understanding this memory system, we directly compared the behavior of pigeons and humans on a change detection task. Although pigeons had a lower storage capacity and a higher lapse rate than humans, both species stored multiple items in short-term memory and conforme...
Daie, Kayvon; Goldman, Mark S; Aksay, Emre R F
A short-term memory can be evoked by different inputs and control separate targets in different behavioral contexts. To address the circuit mechanisms underlying context-dependent memory function, we determined through optical imaging how memory is encoded at the whole-network level in two behavioral settings. Persistent neural activity maintaining a memory of desired eye position was imaged throughout the oculomotor integrator after saccadic or optokinetic stimulation. While eye position was encoded by the amplitude of network activity, the spatial patterns of firing were context dependent: cells located caudally generally were most persistent following saccadic input, whereas cells located rostrally were most persistent following optokinetic input. To explain these data, we computationally identified four independent modes of network activity and found these were differentially accessed by saccadic and optokinetic inputs. These results show how a circuit can simultaneously encode memory value and behavioral context, respectively, in its amplitude and spatial pattern of persistent firing. Copyright © 2015 Elsevier Inc. All rights reserved.
Zhang, Qin; Wang, Hui; Dong, Junyu; Zhong, Guoqiang; Sun, Xin
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.
Chen, Ling; Li, Chuandong; Huang, Tingwen; Ahmad, Hafiz Gulfam; Chen, Yiran
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 . • A new Hopfield neural network with the forgetting ability is built by the new memristor model
Li, Wenyuan; Wilker, Elissa H; Dorans, Kirsten S; Rice, Mary B; Schwartz, Joel; Coull, Brent A; Koutrakis, Petros; Gold, Diane R; Keaney, John F; Lin, Honghuang; Vasan, Ramachandran S; Benjamin, Emelia J; Mittleman, Murray A
Short-term exposure to elevated air pollution has been associated with higher risk of acute cardiovascular diseases, with systemic oxidative stress induced by air pollution hypothesized as an important underlying mechanism. However, few community-based studies have assessed this association. Two thousand thirty-five Framingham Offspring Cohort participants living within 50 km of the Harvard Boston Supersite who were not current smokers were included. We assessed circulating biomarkers of oxidative stress including blood myeloperoxidase at the seventh examination (1998-2001) and urinary creatinine-indexed 8-epi-prostaglandin F2α (8-epi-PGF2α) at the seventh and eighth (2005-2008) examinations. We measured fine particulate matter (PM2.5), black carbon, sulfate, nitrogen oxides, and ozone at the Supersite and calculated 1-, 2-, 3-, 5-, and 7-day moving averages of each pollutant. Measured myeloperoxidase and 8-epi-PGF2α were loge transformed. We used linear regression models and linear mixed-effects models with random intercepts for myeloperoxidase and indexed 8-epi-PGF2α, respectively. Models were adjusted for demographic variables, individual- and area-level measures of socioeconomic position, clinical and lifestyle factors, weather, and temporal trend. We found positive associations of PM2.5 and black carbon with myeloperoxidase across multiple moving averages. Additionally, 2- to 7-day moving averages of PM2.5 and sulfate were consistently positively associated with 8-epi-PGF2α. Stronger positive associations of black carbon and sulfate with myeloperoxidase were observed among participants with diabetes than in those without. Our community-based investigation supports an association of select markers of ambient air pollution with circulating biomarkers of oxidative stress. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
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...
Full Text Available The tendency of psychotherapy modern concepts referring to the short-term forms having been taken into account, we carried out the research aimed at the study of short-term form personality-oriented psychotherapy effect on the anxiety disorder dynamics. 103 patients with neurotic disorders were examined in the neurosis and psychotherapy department of the Bekhterev Psychoneurological Research Institute. The findings revealed the situational and personal anxiety level to be objectively decreased in the short-term group psychotherapy course. The short-term group psychotherapy was proved to bean effective method in anxiety disorders treatment considering indications and limitations.
Spironelli, Chiara; Galfano, Giovanni; Umiltà, Carlo; Angrilli, Alessandro
The present study was aimed at investigating the short-term plastic changes that follow word learning at a neurophysiological level. The main hypothesis was that word position (left or right visual field, LVF/RH or RVF/LH) in the initial learning phase would leave a trace that affected, in the subsequent recognition phase, the Recognition Potential (i.e., the first negative component distinguishing words from other stimuli) elicited 220-240 ms after centrally presented stimuli. Forty-eight students were administered, in the learning phase, 125 words for 4s, randomly presented half in the left and half in the right visual field. In the recognition phase, participants were split into two equal groups, one was assigned to the Word task, the other to the Picture task (in which half of the 125 pictures were new, and half matched prior studied words). During the Word task, old RVF/LH words elicited significantly greater negativity in left posterior sites with respect to old LVF/RH words, which in turn showed the same pattern of activation evoked by new words. Therefore, correspondence between stimulus spatial position and hemisphere specialized in automatic word recognition created a robust prime for subsequent recognition. During the Picture task, pictures matching old RVF/LH words showed no differences compared with new pictures, but evoked significantly greater negativity than pictures matching old LVF/RH words. Thus, the priming effect vanished when the task required a switch from visual analysis to stored linguistic information, whereas the lack of correspondence between stimulus position and network specialized in automatic word recognition (i.e., when words were presented to the LVF/RH) revealed the implicit costs for recognition. Results support the view that short-term plastic changes occurring in a linguistic learning task interact with both stimulus position and modality (written word vs. picture representation). Copyright © 2011 Elsevier B.V. All rights
Full Text Available The rat globus pallidus (GP is one of the nuclei of the basal ganglia and plays an important role in a variety of motor and cognitive processes. In vivo studies have shown that repetitive stimulation evokes complex modulations of GP activity. In vitro and computational studies have suggested that short-term synaptic plasticity (STP could be one of the underlying mechanisms. The current study used simplified single compartment modeling to explore the possible effect of STP on the activity of GP neurons during low and high frequency stimulation. To do this we constructed a model of a GP neuron connected to a small network of neurons from the three major input sources to GP neurons: striatum (Str, subthalamic nucleus (STN and GP collaterals. All synapses were implemented with a kinetic model of STP. The in vitro recordings of responses to low frequency repetitive stimulation were highly reconstructed, including rate changes and locking to the stimulus. Mainly involved were fast forms of plasticity which have been found at these synapses. . The simulations were qualitatively compared to a data set previously recorded in vitro in our lab. Reconstructions of experimental responses to high frequency stimulation required adding slower forms of plasticity to the STN and GP collateral synapses, as well as adding metabotropic receptors to the STN-GP synapses. These finding suggest the existence of as yet unreported slower short-term dynamics in the GP. The computational model made additional predictions about GP activity during low and high frequency stimulation that may further our understanding of the mechanisms underlying repetative stimulation of the GP.
Hammerton, J; Chen, SH; Cheng, HD; Chiu, DKY; Das, S; Duro, R; Kerre, EE; Leong, HV; Li, Q; Lu, M; Romay, MG; Ventura, D; Wu, J
Applying Artificial Neural Networks (ANNs) to language learning has been an active area of research in connectionism. However much of this work has involved small and/or artificially created data sets, whilst other approaches to language learning are now routinely applied to large real-world
Williams, David E; Henshaw, Geoff S; Bart, Mark; Laing, Greer; Wagner, John; Naisbitt, Simon; Salmond, Jennifer A
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)
Zhang, Q.; Fitzek, Frank; Katz, Marcos
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...
Steinbrecht, W; Claude, H; Schönenborn, F; McDermid, I S; Leblanc, T; Godin, S; Song, T; Swart, D P J; Meijer, Y J; Bodeker, G E; Connor, B J; Kämpfer, N; Hocke, K; Calisesi, Y; Schneider, N; Noë, J de la; Parrish, A D; Boyd, I S; Brühl, C; Steil, B; Giorgetta, M A; Manzini, E; Thomason, L W; Zawodny, J M; McCormick, M P; Russell, J M; Bhartia, P K; Stolarski, R S; Hollandsworth-Frith, S M
The long-term evolution of upper stratospheric ozone has been recorded by lidars and microwave radiometers within the ground-based Network for the Detection of Stratospheric Change (NDSC), and by the space-borne Solar Backscatter Ultra-Violet instruments (SBUV), Stratospheric Aerosol and Gas
Pelikán, Emil; Eben, Kryštof; Vondráček, Jiří; Krejčíř, Pavel; Keder, J.
Roč. 3, č. 2 (2000), s. 3-8 ISSN 1335-339X Grant - others:APPETISE(XE) IST-99-11764; MŽP ČR(CZ) ZZ520/2/97; MŠMT ČR(CZ) VS96008 Institutional research plan: AV0Z1030915 Keywords : ozone forecast * neural classifications * Kalman filter * genetic algorithms * Kohonen maps * Czech Republic Subject RIV: BB - Applied Statistics, Operational Research
Holben, B. N.; Tanre, D.; Reagan, J. A.; Eck, T. F.; Setzer, A.; Kaufman, Y. A.; Vermote, E.; Vassiliou, G. D.; Lavenu, F.
A new generation of automatic sunphotometers is used to systematically monitor clear sky total column aerosol concentration and optical properties, precipitable water and total column ozone diurnally and annually in West Africa and South America. The instruments are designed to measure direct beam sun, solar aureole and sky radiances in nine narrow spectral bands from the UV to the near infrared on an hourly basis. The instrumentation and the algorithms required to reduce the data for subsequent analysis are described.
Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime
Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime
Background Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. Methods and Results We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. PMID:26254303
Quinault, J.-M.; Caput, Claude; Belot, Yves.
A computer program was developed in order to evaluate pollutant distributions in an area affected by multiple point sources, such as a typical industrial area. Classical gaussian relationships were used with some improvements taking account of ground roughness, pollutant deposition and sampling duration. The program written in A.P.L., is easy to use by a non-specialized operator on a console linked to a computer through the telephone network [fr
Crabtree, Gregg W.; Gogos, Joseph A.
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
Usaola, J.; Ravelo, O.; Gonzalez, G.; Soto, F.; Davila, M.C.; Diaz-Guerra, B.
One of the characteristics of wind energy, from the grid point of view, is its non-dispatchability, i.e. generation cannot be ordered, hence integration in electrical networks may be difficult. Short-term wind power prediction-tools could make this integration easier, either by their use by the grid System Operator, or by promoting the participation of wind farms in the electricity markets and using prediction tools to make their bids in the market. In this paper, the importance of a short-term wind power-prediction tool for the participation of wind energy systems in electricity markets is studied. Simulations, according to the current Spanish market rules, have been performed to the production of different wind farms, with different degrees of accuracy in the prediction tool. It may be concluded that income from participation in electricity markets is increased using a short-term wind power prediction-tool of average accuracy. This both marginally increases income and also reduces the impact on system operation with the improved forecasts. (author)
Ward, Anthony R; Alarcón, Gabriela; Nigg, Joel T; Musser, Erica D
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.
Kurdziel, Magdalena; Filek, Maria; Łabanowska, Maria
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.