Incidence of climate on common frog breeding: Long-term and short-term changes

Science.gov (United States)

Neveu, André

2009-09-01

In Brittany (northwest France), the climate is showing a trend toward warming. This change is increasingly suspected to have a role in driving amphibian decline, but it is very difficult to determine at what level the climate affects the future of species. Recently, some studies have detected some direct effects on breeding phenology and indirect effects on energy allocation. The present study explores some of these effects on the common frog ( Rana temporaria) from 1984 to 2007. The results show two trends: a long-term change in breeding activities and a short-term influence due to the 2003 climatic anomaly. For the period of study, the start of egg-laying shows a precocity that was correlated with thermal conditions during the preceding 40 days as well as milder springs during the previous year. This degree of precocity is currently the highest found in Europe (+26.6 days). As a result of the 2003 heat wave, the clutch mean fecundity in 2004 was smaller than for other years, the fecundity rates were reduced and abortions were numerous (unlike other years). Moreover, young females were the smallest observed in recent years and some females seemed to exhibit a trade-off between fecundity and growth. Before or after egg-laying, female body condition and mean weight of mature ovules were both lower. The year 2005 appears as a transition period before the recovery in 2006-2007. The results show that climate warming endangers the vital rates of the common frog, while the 2003 climatic events seem more detrimental than the long-term warming trend.

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

Directory of Open Access Journals (Sweden)

Chuan Ding

2016-10-01

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

Projected climate change impacts and short term predictions on staple crops in Sub-Saharan Africa

Science.gov (United States)

Mereu, V.; Spano, D.; Gallo, A.; Carboni, G.

2013-12-01

Agriculture in Sub-Saharan Africa (SSA) drives the economy of many African countries and it is mainly rain-fed agriculture used for subsistence. Increasing temperatures, changed precipitation patterns and more frequent droughts may lead to a substantial decrease of crop yields. The projected impacts of future climate change on agriculture are expected to be significant and extensive in the SSA due to the shortening of the growing seasons and the increasing of water-stress risk. Differences in Agro-Ecological Zones and geographical characteristics of SSA influence the diverse impacts of climate change, which can greatly differ across the continent and within countries. The vulnerability of African Countries to climate change is aggravated by the low adaptive capacity of the continent, due to the increasing of its population, the widespread poverty, and other social factors. In this contest, the assessment of climate change impact on agricultural sector has a particular interest to stakeholder and policy makers, in order to identify specific agricultural sectors and Agro-Ecological Zones that could be more vulnerable to changes in climatic conditions and to develop the most appropriate policies to cope with these threats. For these reasons, the evaluation of climate change impacts for key crops in SSA was made exploring climate uncertainty and focusing on short period monitoring, which is particularly useful for food security and risk management analysis. The DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, version 4.5 was used for the analysis. Crop simulation models included in DSSAT-CSM are tools that allow to simulate physiological process of crop growth, development and production, by combining genetic crop characteristics and environmental (soil and weather) conditions. For each selected crop, the models were used, after a parameterization phase, to evaluate climate change impacts on crop phenology and production

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

Directory of Open Access Journals (Sweden)

Rudolf Scitovski

2012-12-01

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

Short-term memory predictions across the lifespan: monitoring span before and after conducting a task.

Science.gov (United States)

Bertrand, Julie Marilyne; Moulin, Chris John Anthony; Souchay, Céline

2017-05-01

Our objective was to explore metamemory in short-term memory across the lifespan. Five age groups participated in this study: 3 groups of children (4-13 years old), and younger and older adults. We used a three-phase task: prediction-span-postdiction. For prediction and postdiction phases, participants reported with a Yes/No response if they could recall in order a series of images. For the span task, they had to actually recall such series. From 4 years old, children have some ability to monitor their short-term memory and are able to adjust their prediction after experiencing the task. However, accuracy still improves significantly until adolescence. Although the older adults had a lower span, they were as accurate as young adults in their evaluation, suggesting that metamemory is unimpaired for short-term memory tasks in older adults. •We investigate metamemory for short-term memory tasks across the lifespan. •We find younger children cannot accurately predict their span length. •Older adults are accurate in predicting their span length. •People's metamemory accuracy was related to their short-term memory span.

Predicting short-term weight loss using four leading health behavior change theories

Directory of Open Access Journals (Sweden)

Barata José T

2007-04-01

Full Text Available Abstract Background This study was conceived to analyze how exercise and weight management psychosocial variables, derived from several health behavior change theories, predict weight change in a short-term intervention. The theories under analysis were the Social Cognitive Theory, the Transtheoretical Model, the Theory of Planned Behavior, and Self-Determination Theory. Methods Subjects were 142 overweight and obese women (BMI = 30.2 ± 3.7 kg/m2; age = 38.3 ± 5.8y, participating in a 16-week University-based weight control program. Body weight and a comprehensive psychometric battery were assessed at baseline and at program's end. Results Weight decreased significantly (-3.6 ± 3.4%, p Conclusion The present models were able to predict 20–30% of variance in short-term weight loss and changes in weight management self-efficacy accounted for a large share of the predictive power. As expected from previous studies, exercise variables were only moderately associated with short-term outcomes; they are expected to play a larger explanatory role in longer-term results.

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

OpenAIRE

Jones, Gary; Macken, Bill

2017-01-01

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

Prediction of interannual climate variations

International Nuclear Information System (INIS)

Shukla, J.

1993-01-01

It has been known for some time that the behavior of the short-term fluctuations of the earth's atmosphere resembles that of a chaotic non-linear dynamical system, and that the day-to-day weather cannot be predicted beyond a few weeks. However, it has also been found that the interactions of the atmosphere with the underlying oceans and the land surfaces can produce fluctuations whose time scales are much longer than the limits of deterministic prediction of weather. It is, therefore, natural to ask whether it is possible that the seasonal and longer time averages of climate fluctuations can be predicted with sufficient skill to be beneficial for social and economic applications, even though the details of day-to-day weather cannot be predicted beyond a few weeks. The main objective of the workshop was to address this question by assessing the current state of knowledge on predictability of seasonal and interannual climate variability and to investigate various possibilities for its prediction. (orig./KW)

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

Directory of Open Access Journals (Sweden)

Jianzhou Wang

2014-01-01

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

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

NARCIS (Netherlands)

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

1998-01-01

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

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

International Nuclear Information System (INIS)

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

2017-01-01

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

Neonatal Pulmonary MRI of Bronchopulmonary Dysplasia Predicts Short-term Clinical Outcomes.

Science.gov (United States)

Higano, Nara S; Spielberg, David R; Fleck, Robert J; Schapiro, Andrew H; Walkup, Laura L; Hahn, Andrew D; Tkach, Jean A; Kingma, Paul S; Merhar, Stephanie L; Fain, Sean B; Woods, Jason C

2018-05-23

Bronchopulmonary dysplasia (BPD) is a serious neonatal pulmonary condition associated with premature birth, but the underlying parenchymal disease and trajectory are poorly characterized. The current NICHD/NHLBI definition of BPD severity is based on degree of prematurity and extent of oxygen requirement. However, no clear link exists between initial diagnosis and clinical outcomes. We hypothesized that magnetic resonance imaging (MRI) of structural parenchymal abnormalities will correlate with NICHD-defined BPD disease severity and predict short-term respiratory outcomes. Forty-two neonates (20 severe BPD, 6 moderate, 7 mild, 9 non-BPD controls; 40±3 weeks post-menstrual age) underwent quiet-breathing structural pulmonary MRI (ultrashort echo-time and gradient echo) in a NICU-sited, neonatal-sized 1.5T scanner, without sedation or respiratory support unless already clinically prescribed. Disease severity was scored independently by two radiologists. Mean scores were compared to clinical severity and short-term respiratory outcomes. Outcomes were predicted using univariate and multivariable models including clinical data and scores. MRI scores significantly correlated with severities and predicted respiratory support at NICU discharge (P<0.0001). In multivariable models, MRI scores were by far the strongest predictor of respiratory support duration over clinical data, including birth weight and gestational age. Notably, NICHD severity level was not predictive of discharge support. Quiet-breathing neonatal pulmonary MRI can independently assess structural abnormalities of BPD, describe disease severity, and predict short-term outcomes more accurately than any individual standard clinical measure. Importantly, this non-ionizing technique can be implemented to phenotype disease and has potential to serially assess efficacy of individualized therapies.

Applicability of short-term accelerated biofouling studies to predict long-term biofouling accumulation in reverse osmosis membrane systems

KAUST Repository

Sanawar, Huma

2018-02-02

Biofouling studies addressing biofouling control are mostly executed in short-term studies. It is unclear whether data collected from these experiments are representative for long-term biofouling as occurring in full-scale membrane systems. This study investigated whether short-term biofouling studies accelerated by biodegradable nutrient dosage to feed water were predictive for long-term biofouling development without nutrient dosage. Since the presence of a feed spacer has an strong effect on the degree of biofouling, this study employed six geometrically different feed spacers. Membrane fouling simulators (MFSs) were operated with the same (i) membrane, (ii) feed flow and (iii) feed water, but with feed spacers varying in geometry. For the short-term experiment, biofilm formation was enhanced by nutrient dosage to the MFS feed water, whereas no nutrient dosage was applied in the long-term experiment. Pressure drop development was monitored to characterize the extent of biofouling, while the accumulated viable biomass content at the end of the experimental run was quantified by adenosine triphosphate (ATP) measurements. Impact of feed spacer geometry on biofouling was compared for the short-term and long-term biofouling study. The results of the study revealed that the feed spacers exhibited the same biofouling behavior for (i) the short-term (9-d) study with nutrient dosage and (ii) the long-term (96-d) study without nutrient dosage. For the six different feed spacers, the accumulated viable biomass content (pg ATP.cm) was roughly the same, but the biofouling impact in terms of pressure drop increase in time was significantly different. The biofouling impact ranking of the six feed spacers was the same for the short-term and long-term biofouling studies. Therefore, it can be concluded that short-term accelerated biofouling studies in MFSs are a representative and suitable approach for the prediction of biofouling in membrane filtration systems after long-term

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

Directory of Open Access Journals (Sweden)

Holger Hoppe

2012-05-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2017-05-17

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

Climate Prediction Center

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Organization Enter Search Term(s): Search Search the CPC Go NCEP Quarterly Newsletter Climate Highlights U.S Climate-Weather El Niño/La Niña MJO Blocking AAO, AO, NAO, PNA Climatology Global Monsoons Expert

A review on the young history of the wind power short-term prediction

DEFF Research Database (Denmark)

Costa, A.; Crespo, A.; Navarro, J.

2008-01-01

This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art oil models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought...... on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly...

Pediatric polytrauma : Short-term and long-term outcomes

NARCIS (Netherlands)

vanderSluis, CK; Kingma, J; Eisma, WH; tenDuis, HJ

Objective: To assess the short-term and long-term outcomes of pediatric polytrauma patients and to analyze the extent to which short-term outcomes can predict long-term outcomes. Materials and Methods: Ail pediatric polytrauma patients (Injury Severity Score of greater than or equal to 16, less than

Prediction of Sea Surface Temperature Using Long Short-Term Memory

Science.gov (United States)

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

2017-10-01

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

Human short-term spatial memory: precision predicts capacity.

Science.gov (United States)

Banta Lavenex, Pamela; Boujon, Valérie; Ndarugendamwo, Angélique; Lavenex, Pierre

2015-03-01

Here, we aimed to determine the capacity of human short-term memory for allocentric spatial information in a real-world setting. Young adults were tested on their ability to learn, on a trial-unique basis, and remember over a 1-min interval the location(s) of 1, 3, 5, or 7 illuminating pads, among 23 pads distributed in a 4m×4m arena surrounded by curtains on three sides. Participants had to walk to and touch the pads with their foot to illuminate the goal locations. In contrast to the predictions from classical slot models of working memory capacity limited to a fixed number of items, i.e., Miller's magical number 7 or Cowan's magical number 4, we found that the number of visited locations to find the goals was consistently about 1.6 times the number of goals, whereas the number of correct choices before erring and the number of errorless trials varied with memory load even when memory load was below the hypothetical memory capacity. In contrast to resource models of visual working memory, we found no evidence that memory resources were evenly distributed among unlimited numbers of items to be remembered. Instead, we found that memory for even one individual location was imprecise, and that memory performance for one location could be used to predict memory performance for multiple locations. Our findings are consistent with a theoretical model suggesting that the precision of the memory for individual locations might determine the capacity of human short-term memory for spatial information. Copyright © 2015 Elsevier Inc. All rights reserved.

Short-Term Forecasting of Electric Energy Generation for a Photovoltaic System

Directory of Open Access Journals (Sweden)

Dinh V.T.

2018-01-01

Full Text Available This article presents a short-term forecast of electric energy output of a photovoltaic (PV system towards Tomsk city, Russia climate variations (module temperature and solar irradiance. The system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University. The obtained results show good agreement between actual data and prediction values.

Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data

Directory of Open Access Journals (Sweden)

Adam Mikus

2018-06-01

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

VAN method of short-term earthquake prediction shows promise

Science.gov (United States)

Uyeda, Seiya

Although optimism prevailed in the 1970s, the present consensus on earthquake prediction appears to be quite pessimistic. However, short-term prediction based on geoelectric potential monitoring has stood the test of time in Greece for more than a decade [VarotsosandKulhanek, 1993] Lighthill, 1996]. The method used is called the VAN method.The geoelectric potential changes constantly due to causes such as magnetotelluric effects, lightning, rainfall, leakage from manmade sources, and electrochemical instabilities of electrodes. All of this noise must be eliminated before preseismic signals are identified, if they exist at all. The VAN group apparently accomplished this task for the first time. They installed multiple short (100-200m) dipoles with different lengths in both north-south and east-west directions and long (1-10 km) dipoles in appropriate orientations at their stations (one of their mega-stations, Ioannina, for example, now has 137 dipoles in operation) and found that practically all of the noise could be eliminated by applying a set of criteria to the data.

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

Czech Academy of Sciences Publication Activity Database

Reissigová, Jindra; Zvolský, M.

2018-01-01

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

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

Science.gov (United States)

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

2018-02-01

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

Development of an integrated method for long-term water quality prediction using seasonal climate forecast

Directory of Open Access Journals (Sweden)

J. Cho

2016-10-01

Full Text Available The APEC Climate Center (APCC produces climate prediction information utilizing a multi-climate model ensemble (MME technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1 the Simple Bias Correction (SBC method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2 the Moving Window Regression (MWR method, which indirectly utilizes dynamic prediction data; (3 the Climate Index Regression (CIR method, which predominantly uses observation-based climate indices; and (4 the Integrated Time Regression (ITR method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

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

NARCIS (Netherlands)

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

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

Four Examples of Short-Term and Imminent Prediction of Earthquakes

Science.gov (United States)

zeng, zuoxun; Liu, Genshen; Wu, Dabin; Sibgatulin, Victor

2014-05-01

We show here 4 examples of short-term and imminent prediction of earthquakes in China last year. They are Nima Earthquake(Ms5.2), Minxian Earthquake(Ms6.6), Nantou Earthquake (Ms6.7) and Dujiangyan Earthquake (Ms4.1) Imminent Prediction of Nima Earthquake(Ms5.2) Based on the comprehensive analysis of the prediction of Victor Sibgatulin using natural electromagnetic pulse anomalies and the prediction of Song Song and Song Kefu using observation of a precursory halo, and an observation for the locations of a degasification of the earth in the Naqu, Tibet by Zeng Zuoxun himself, the first author made a prediction for an earthquake around Ms 6 in 10 days in the area of the degasification point (31.5N, 89.0 E) at 0:54 of May 8th, 2013. He supplied another degasification point (31N, 86E) for the epicenter prediction at 8:34 of the same day. At 18:54:30 of May 15th, 2013, an earthquake of Ms5.2 occurred in the Nima County, Naqu, China. Imminent Prediction of Minxian Earthquake (Ms6.6) At 7:45 of July 22nd, 2013, an earthquake occurred at the border between Minxian and Zhangxian of Dingxi City (34.5N, 104.2E), Gansu province with magnitude of Ms6.6. We review the imminent prediction process and basis for the earthquake using the fingerprint method. 9 channels or 15 channels anomalous components - time curves can be outputted from the SW monitor for earthquake precursors. These components include geomagnetism, geoelectricity, crust stresses, resonance, crust inclination. When we compress the time axis, the outputted curves become different geometric images. The precursor images are different for earthquake in different regions. The alike or similar images correspond to earthquakes in a certain region. According to the 7-year observation of the precursor images and their corresponding earthquake, we usually get the fingerprint 6 days before the corresponding earthquakes. The magnitude prediction needs the comparison between the amplitudes of the fingerpringts from the same

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

International Nuclear Information System (INIS)

Chen, Kuilin; Yu, Jie

2014-01-01

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

Perceived stress and anhedonia predict short-and long-term weight change, respectively, in healthy adults.

Science.gov (United States)

Ibrahim, Mostafa; Thearle, Marie S; Krakoff, Jonathan; Gluck, Marci E

2016-04-01

Perceived stress; emotional eating; anhedonia; depression and dietary restraint, hunger, and disinhibition have been studied as risk factors for obesity. However, the majority of studies have been cross-sectional and the directionality of these relationships remains unclear. In this longitudinal study, we assess their impact on future weight change. Psychological predictors of weight change in short- (6month) and long-term (>1year) periods were studied in 65 lean and obese individuals in two cohorts. Subjects participated in studies of food intake and metabolism that did not include any type of medication or weight loss interventions. They completed psychological questionnaires at baseline and weight change was monitored at follow-up visits. At six months, perceived stress predicted weight gain (r(2)=0.23, P=0.02). There was a significant interaction (r(2)=.38, P=0.009) between perceived stress and positive emotional eating, such that higher scores in both predicted greater weight gain, while those with low stress but high emotional eating scores lost weight. For long-term, higher anhedonia scores predicted weight gain (r(2)=0.24, P=0.04). Depression moderated these effects such that higher scores in both predicted weight gain but higher depression and lower anhedonia scores predicted weight loss. There are different behavioral determinants for short- and long-term weight change. Targeting perceived stress may help with short-term weight loss while depression and anhedonia may be better targets for long-term weight regulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Subtask 2.4 - Integration and Synthesis in Climate Change Predictive Modeling

Energy Technology Data Exchange (ETDEWEB)

Jaroslav Solc

2009-06-01

The Energy & Environmental Research Center (EERC) completed a brief evaluation of the existing status of predictive modeling to assess options for integration of our previous paleohydrologic reconstructions and their synthesis with current global climate scenarios. Results of our research indicate that short-term data series available from modern instrumental records are not sufficient to reconstruct past hydrologic events or predict future ones. On the contrary, reconstruction of paleoclimate phenomena provided credible information on past climate cycles and confirmed their integration in the context of regional climate history is possible. Similarly to ice cores and other paleo proxies, acquired data represent an objective, credible tool for model calibration and validation of currently observed trends. It remains a subject of future research whether further refinement of our results and synthesis with regional and global climate observations could contribute to improvement and credibility of climate predictions on a regional and global scale.

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

Science.gov (United States)

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

2017-05-01

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

Decadal climate predictions improved by ocean ensemble dispersion filtering

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

Short-term climatic fluctuations and the interpretation of recent observations in terms of greenhouse effect

International Nuclear Information System (INIS)

Andre, J.C.; Royer, J.F.

1999-01-01

Simulations of future climate made with coupled general circulation models of the atmosphere and ocean predict that the increase of the concentration of greenhouse gases released in the atmosphere by man's activities will have a large influence on the climate of the next century. The identification of the climatic impact produced by the rapid increase in carbon dioxide concentration in the last decades is made difficult by strong inter-annual climate variability, and requires the application of statistical techniques combining several climatic indicators (method of climatic 'fingerprints') so as to improve the detection of a possible anthropogenic perturbation. In this paper we review the evolution through the last decades of several climate indicators showing global warming, its geographical distribution, sea level, the hydrological cycle and the response of vegetation, and we compare them to the model results predicted in climate scenarios. The coherence between model results and observed climatic trends shows that the additional greenhouse effect is starting to become detectable in recent climatic data. (authors)

Short-Term Behavior of Slag Concretes Exposed to a Real In Situ Mediterranean Climate Environment.

Science.gov (United States)

Ortega, José Marcos; Sánchez, Isidro; Cabeza, Marta; Climent, Miguel Ángel

2017-08-08

At present, one of the most suitable ways to get a more sustainable cement industry is to reduce the CO₂ emissions generated during cement production. In order to reach that goal, the use of ground granulated blast-furnace slag as clinker replacement is becoming increasingly popular. Although the effects of this addition in the properties of cementitious materials are influenced by their hardening conditions, there are not too many experimental studies in which slag concretes have been exposed to real in situ environments. Then, the main objective of this research is to study the short-term effects of exposure to real Mediterranean climate environment of an urban site, where the action of airborne chlorides from sea water and the presence of CO₂ are combined, in the microstructure and service properties of a commercial slag cement concrete, compared to ordinary Portland cement (OPC). The microstructure was studied with mercury intrusion porosimetry. The effective porosity, capillary suction coefficient, chloride migration coefficient, carbonation front depth, and compressive strength were also analyzed. Considering the results obtained, slag concretes exposed to a real in situ Mediterranean climate environment show good service properties in the short-term (180 days), in comparison with OPC.

The contrasting effects of short-term climate change on the early recruitment of tree species.

Science.gov (United States)

Ibáñez, Inés; Katz, Daniel S W; Lee, Benjamin R

2017-07-01

Predictions of plant responses to climate change are frequently based on organisms' presence in warmer locations, which are then assumed to reflect future performance in cooler areas. However, as plant life stages may be affected differently by environmental changes, there is little empirical evidence that this approach provides reliable estimates of short-term responses to global warming. Under this premise, we analyzed 8 years of early recruitment data, seed production and seedling establishment and survival, collected for two tree species at two latitudes. We quantified recruitment to a wide range of environmental conditions, temperature, soil moisture and light, and simulated recruitment under two forecasted climatic scenarios. Annual demographic transitions were affected by the particular conditions taking place during their onset, but the effects of similar environmental shifts differed among the recruitment stages; seed production was higher in warmer years, while seedling establishment and survival peaked during cold years. Within a species, these effects also varied between latitudes; increasing temperatures at the southern location will have stronger detrimental effects on recruitment than similar changes at the northern locations. Our simulations illustrate that warmer temperatures may increase seed production, but they will have a negative effect on establishment and survival. When the three early recruitment processes were simultaneously considered, simulations showed little change in recruitment dynamics at the northern site and a slight decrease at the southern site. It is only when we considered these three stages that we were able to assess likely changes in early recruitment under the predicted conditions.

Expanding research capabilities with sea ice climate records for analysis of long-term climate change and short-term variability

Science.gov (United States)

Scott, D. J.; Meier, W. N.

2008-12-01

Recent sea ice analysis is leading to predictions of a sea ice-free summertime in the Arctic within 20 years, or even sooner. Sea ice topics, such as concentration, extent, motion, and age, are predominately studied using satellite data. At the National Snow and Ice Data Center (NSIDC), passive microwave sea ice data sets provide timely assessments of seasonal-scale variability as well as consistent long-term climate data records. Such data sets are crucial to understanding changes and assessing their impacts. Noticeable impacts of changing sea ice conditions on native cultures and wildlife in the Arctic region are now being documented. With continued deterioration in Arctic sea ice, global economic impacts will be seen as new shipping routes open. NSIDC is at the forefront of making climate data records available to address the changes in sea ice and its global impacts. By focusing on integrated data sets, NSIDC leads the way by broadening the studies of sea ice beyond the traditional cryospheric community.

Decadel climate prediction: challenges and opportunities

International Nuclear Information System (INIS)

Hurrell, J W

2008-01-01

The scientific understanding of climate change is now sufficiently clear to show that climate change from global warming is already upon us, and the rate of change as projected exceeds anything seen in nature in the past 10,000 years. Uncertainties remain, however, especially regarding how climate will change at regional and local scales where the signal of natural variability is large. Addressing many of these uncertainties will require a movement toward high resolution climate system predictions, with a blurring of the distinction between shorter-term predictions and longer-term climate projections. The key is the realization that climate system predictions, regardless of timescale, will require initialization of coupled general circulation models with best estimates of the current observed state of the atmosphere, oceans, cryosphere, and land surface. Formidable challenges exist: for instance, what is the best method of initialization given imperfect observations and systematic errors in models? What effect does initialization have on climate predictions? What predictions should be attempted, and how would they be verified? Despite such challenges, the unrealized predictability that resides in slowly evolving phenomena, such as ocean current systems, is of paramount importance for society to plan and adapt for the next few decades. Moreover, initialized climate predictions will require stronger collaboration with shared knowledge, infrastructure and technical capabilities among those in the weather and climate prediction communities. The potential benefits include improved understanding and predictions on all time scales

Short Term Prediction of PM10 Concentrations Using Seasonal Time Series Analysis

Directory of Open Access Journals (Sweden)

Hamid Hazrul Abdul

2016-01-01

Full Text Available Air pollution modelling is one of an important tool that usually used to make short term and long term prediction. Since air pollution gives a big impact especially to human health, prediction of air pollutants concentration is needed to help the local authorities to give an early warning to people who are in risk of acute and chronic health effects from air pollution. Finding the best time series model would allow prediction to be made accurately. This research was carried out to find the best time series model to predict the PM10 concentrations in Nilai, Negeri Sembilan, Malaysia. By considering two seasons which is wet season (north east monsoon and dry season (south west monsoon, seasonal autoregressive integrated moving average model were used to find the most suitable model to predict the PM10 concentrations in Nilai, Negeri Sembilan by using three error measures. Based on AIC statistics, results show that ARIMA (1, 1, 1 × (1, 0, 012 is the most suitable model to predict PM10 concentrations in Nilai, Negeri Sembilan.

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

Directory of Open Access Journals (Sweden)

Dongxiao Niu

2015-01-01

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

An adaptive short-term prediction scheme for wind energy storage management

International Nuclear Information System (INIS)

Blonbou, Ruddy; Monjoly, Stephanie; Dorville, Jean-Francois

2011-01-01

Research highlights: → We develop a real time algorithm for grid-connected wind energy storage management. → The method aims to guarantee, with ±5% error margin, the power sent to the grid. → Dynamic scheduling of energy storage is based on short-term energy prediction. → Accurate predictions reduce the need in storage capacity. -- Abstract: Efficient forecasting scheme that includes some information on the likelihood of the forecast and based on a better knowledge of the wind variations characteristics along with their influence on power output variation is of key importance for the optimal integration of wind energy in island's power system. In the Guadeloupean archipelago (French West-Indies), with a total wind power capacity of 25 MW; wind energy can represent up to 5% of the instantaneous electricity production. At this level, wind energy contribution can be equivalent to the current network primary control reserve, which causes balancing difficult. The share of wind energy is due to grow even further since the objective is set to reach 118 MW by 2020. It is an absolute evidence for the network operator that due to security concerns of the electrical grid, the share of wind generation should not increase unless solutions are found to solve the prediction problem. The University of French West-Indies and Guyana has developed a short-term wind energy prediction scheme that uses artificial neural networks and adaptive learning procedures based on Bayesian approach and Gaussian approximation. This paper reports the results of the evaluation of the proposed approach; the improvement with respect to the simple persistent prediction model was globally good. A discussion on how such a tool combined with energy storage capacity could help to smooth the wind power variation and improve the wind energy penetration rate into island utility network is also proposed.

Verification of“Trend-Volatility Model”in Short-Term Forecast of Grain Production Potential

Directory of Open Access Journals (Sweden)

MI Chang-hong

2016-02-01

Full Text Available The "trend-volatility model" in short-term forecasting of grain production potential was verified and discussed systematically by using the grain production data from 1949 to 2014, in 16 typical counties and 6 typical districts, and 31 provinces, of China. The results showed as follows:(1 Size of forecast error reflected the precision of short-term production potential, the main reason of large prediction error was a great amount of high yield farmlands were occupied in developed areas and a great increase of vegetable and fruit planted that made grain yield decreased in a short time;(2 The micro-trend amendment method was a necessary part of "trend-volatility model", which could involve the short-term factors such as meteorological factors, science and technology input, social factors and other effects, while macro-trend prediction could not. Therefore, The micro-trend amendment method could improve the forecast precision.(3 In terms of actual situation in recent years in China, the more developed the areas was, the bigger the volatility of short-term production potential was; For the short-term production potential, the stage of increasing-decreasing-recovering also existed in developed areas;(4 In the terms of forecast precision of short-terms production potential, the scale of national was higher than the scale of province, the scale of province was higher than the scale of district, the scale of district was higher than the scale of county. And it was large differences in precision between different provinces, different districts and different counties respectively, which was concerned to the complementarity of domestic climate and the ability of the farmland resistance to natural disasters.

Benefits for wind energy in electricity markets from using short term wind power prediction tools: a simulation study

International Nuclear Information System (INIS)

Usaola, J.; Ravelo, O.; Gonzalez, G.; Soto, F.; Davila, M.C.; Diaz-Guerra, B.

2004-01-01

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)

Short-term Consumer Benefits of Dynamic Pricing

OpenAIRE

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

2011-01-01

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

A review of wave climate and prediction along the Spanish Mediterranean coast

Directory of Open Access Journals (Sweden)

A. Sánchez-Arcilla

2008-11-01

Full Text Available This paper reviews the characterization of wave storms along the Spanish/Catalan Mediterranean coast. It considers the "physical" and "statistical" description of wave parameters and how they are affected by the prevailing meteo patterns and the sharp gradients in orography and bathymetry. The available field data and numerically simulated wave fields are discussed from this perspective. The resulting limits in accuracy and predictability are illustrated with specific examples. This allows deriving some conclusions for both short-term operational predictions and a long-term climatic assessment.

The value of short-term pain relief in predicting the long-term outcome of 'indirect' cervical epidural steroid injections.

Science.gov (United States)

Joswig, Holger; Neff, Armin; Ruppert, Christina; Hildebrandt, Gerhard; Stienen, Martin Nikolaus

2018-05-01

The predictive value of short-term arm pain relief after 'indirect' cervical epidural steroid injection (ESI) for the 1-month treatment response has been previously demonstrated. It remained to be answered whether the long-term response could be estimated by the early post-interventional pain course as well. Prospective observational study, following a cohort of n = 45 patients for a period of 24 months after 'indirect' ESI for radiculopathy secondary to a single-level cervical disk herniation (CDH). Arm and neck pain on the visual analog scale (VAS), health-related quality of life with the Short Form-12 (SF-12), and functional outcome with the Neck Pain and Disability (NPAD) Scale were assessed. Any additional invasive treatment after a single injection (second injection or surgery) defined treatment outcome as 'non-response'. At 24 months, n = 30 (66.7%) patients were responders and n = 15 (33.3%) were non-responders. Non-responders exited the follow-up at 1 month (n = 10), at 3 months (n = 4), and at 6 months (n = 1). No patients were injected again or operated on between the 6- and 24-month follow-up. Patients with favorable treatment response at 24 months had significantly lower VAS arm pain (p  50% short term pain reduction was not a reliable predictor of the 24-month responder status. SF-12 and NPAD scores were better among treatment responders in the long term. Patients who require a second injection or surgery after 'indirect' cervical ESI for a symptomatic CDH do so within the first 6 months. Short-term pain relief cannot reliably predict the long-term outcome.

SHORT-TERM PRECIPITATION OCCURRENCE PREDICTION FOR STRONG CONVECTIVE WEATHER USING FY2-G SATELLITE DATA: A CASE STUDY OF SHENZHEN,SOUTH CHINA

Directory of Open Access Journals (Sweden)

K. Chen

2016-06-01

Full Text Available Short-term precipitation commonly occurs in south part of China, which brings intensive precipitation in local region for very short time. Massive water would cause the intensive flood inside of city when precipitation amount beyond the capacity of city drainage system. Thousands people’s life could be influenced by those short-term disasters and the higher city managements are required to facing these challenges. How to predict the occurrence of heavy precipitation accurately is one of the worthwhile scientific questions in meteorology. According to recent studies, the accuracy of short-term precipitation prediction based on numerical simulation model still remains low reliability, in some area where lack of local observations, the accuracy may be as low as 10%. The methodology for short term precipitation occurrence prediction still remains a challenge. In this paper, a machine learning method based on SVM was presented to predict short-term precipitation occurrence by using FY2-G satellite imagery and ground in situ observation data. The results were validated by traditional TS score which commonly used in evaluation of weather prediction. The results indicate that the proposed algorithm can present overall accuracy up to 90% for one-hour to six-hour forecast. The result implies the prediction accuracy could be improved by using machine learning method combining with satellite image. This prediction model can be further used to evaluated to predicted other characteristics of weather in Shenzhen in future.

Predicting the short-term risk of diabetes in HIV-positive patients

DEFF Research Database (Denmark)

Petoumenos, Kathy; Worm, Signe Westring; Fontas, Eric

2012-01-01

Introduction: HIV-positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM ......). Factors predictive of DM included higher glucose, body mass index (BMI) and triglyceride levels, and older age. Among HIV-related factors, recent CD4 counts of...... and other glucose-associated disorders among HIV-positive patients have been reported to range between 2 and 14%, and in an ageing HIV-positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short-term (six-month) risk of DM in HIV...

Will high-resolution global ocean models benefit coupled predictions on short-range to climate timescales?

Science.gov (United States)

Hewitt, Helene T.; Bell, Michael J.; Chassignet, Eric P.; Czaja, Arnaud; Ferreira, David; Griffies, Stephen M.; Hyder, Pat; McClean, Julie L.; New, Adrian L.; Roberts, Malcolm J.

2017-12-01

As the importance of the ocean in the weather and climate system is increasingly recognised, operational systems are now moving towards coupled prediction not only for seasonal to climate timescales but also for short-range forecasts. A three-way tension exists between the allocation of computing resources to refine model resolution, the expansion of model complexity/capability, and the increase of ensemble size. Here we review evidence for the benefits of increased ocean resolution in global coupled models, where the ocean component explicitly represents transient mesoscale eddies and narrow boundary currents. We consider lessons learned from forced ocean/sea-ice simulations; from studies concerning the SST resolution required to impact atmospheric simulations; and from coupled predictions. Impacts of the mesoscale ocean in western boundary current regions on the large-scale atmospheric state have been identified. Understanding of air-sea feedback in western boundary currents is modifying our view of the dynamics in these key regions. It remains unclear whether variability associated with open ocean mesoscale eddies is equally important to the large-scale atmospheric state. We include a discussion of what processes can presently be parameterised in coupled models with coarse resolution non-eddying ocean models, and where parameterizations may fall short. We discuss the benefits of resolution and identify gaps in the current literature that leave important questions unanswered.

Short-term Forecasting Tools for Agricultural Nutrient Management.

Science.gov (United States)

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

2017-11-01

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

A review on the young history of the wind power short-term prediction

Energy Technology Data Exchange (ETDEWEB)

Costa, Alexandre; Navarro, Jorge [Wind Energy, Division of Renewable Energies, Department of Energy, CIEMAT, Av. Complutense, 22, Ed. 42, 28044 Madrid (Spain); Crespo, Antonio [Laboratorio de Mecanica de Fluidos, Departmento de Ingenieria Energetica y Fluidomecanica, ETSII, Universidad Politecnica de Madrid, C/Jose Gutierrez Abascal, 2-28006 Madrid (Spain); Lizcano, Gil [Oxford University Centre for the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY (United Kingdom); Madsen, Henrik [Informatics and Mathematical Modelling - IMM, Technical University of Denmark, Richard Petersens Plads, Building 321, Office 019, 2800 Kgs. Lyngby (Denmark); Feitosa, Everaldo [Brazilian Wind Energy Centre - CBEE, Centro de Tecnologia e Geociencias, UFPE-50.740-530 Recife, PE (Brazil)

2008-08-15

This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out. (author)

Theta coupling between V4 and prefrontal cortex predicts visual short-term memory performance.

Science.gov (United States)

Liebe, Stefanie; Hoerzer, Gregor M; Logothetis, Nikos K; Rainer, Gregor

2012-01-29

Short-term memory requires communication between multiple brain regions that collectively mediate the encoding and maintenance of sensory information. It has been suggested that oscillatory synchronization underlies intercortical communication. Yet, whether and how distant cortical areas cooperate during visual memory remains elusive. We examined neural interactions between visual area V4 and the lateral prefrontal cortex using simultaneous local field potential (LFP) recordings and single-unit activity (SUA) in monkeys performing a visual short-term memory task. During the memory period, we observed enhanced between-area phase synchronization in theta frequencies (3-9 Hz) of LFPs together with elevated phase locking of SUA to theta oscillations across regions. In addition, we found that the strength of intercortical locking was predictive of the animals' behavioral performance. This suggests that theta-band synchronization coordinates action potential communication between V4 and prefrontal cortex that may contribute to the maintenance of visual short-term memories.

Near-Term Actions to Address Long-Term Climate Risk

Science.gov (United States)

Lempert, R. J.

2014-12-01

Addressing climate change requires effective long-term policy making, which occurs when reflecting on potential events decades or more in the future causes policy makers to choose near-term actions different than those they would otherwise pursue. Contrary to some expectations, policy makers do sometimes make such long-term decisions, but not as commonly and successfully as climate change may require. In recent years however, the new capabilities of analytic decision support tools, combined with improved understanding of cognitive and organizational behaviors, has significantly improved the methods available for organizations to manage longer-term climate risks. In particular, these tools allow decision makers to understand what near-term actions consistently contribute to achieving both short- and long-term societal goals, even in the face of deep uncertainty regarding the long-term future. This talk will describe applications of these approaches for infrastructure, water, and flood risk management planning, as well as studies of how near-term choices about policy architectures can affect long-term greenhouse gas emission reduction pathways.

A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

Science.gov (United States)

Vitousek, Sean; Barnard, Patrick; Limber, Patrick W.; Erikson, Li; Cole, Blake

2017-01-01

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea-level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea-level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea-level rise scenarios of 0.93 to 2.0 m.

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

International Nuclear Information System (INIS)

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

2011-01-01

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

Predicting short-term mortality and long-term survival for hospitalized US patients with alcoholic hepatitis.

Science.gov (United States)

Cuthbert, Jennifer A; Arslanlar, Sami; Yepuri, Jay; Montrose, Marc; Ahn, Chul W; Shah, Jessica P

2014-07-01

No study has evaluated current scoring systems for their accuracy in predicting short and long-term outcome of alcoholic hepatitis in a US population. We reviewed electronic records for patients with alcoholic liver disease (ALD) admitted to Parkland Memorial Hospital between January 2002 and August 2005. Data and outcomes for 148 of 1,761 admissions meeting pre-defined criteria were collected. The discriminant function (DF) was revised (INRdf) to account for changes in prothrombin time reagents that could potentially affect identification of risk using the previous DF threshold of >32. Admission and theoretical peak scores were calculated by use of the Model for End-stage Liver Disease (MELD). Analysis models compared five different scoring systems. INRdf was closely correlated with the old DF (r (2) = 0.95). Multivariate analysis of the data showed that survival for 28 days was significantly associated with a scoring system using a combination of age, bilirubin, coagulation status, and creatinine (p short-term mortality (p 50 % mortality at four weeks and >80 % mortality at six months without specific treatment.

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

African Journals Online (AJOL)

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

Long-term ecophysiological responses to climate change

DEFF Research Database (Denmark)

Boesgaard, Kristine Stove; Ro-Poulsen, Helge

Plant physiology is affected by climate change. Acclimations of photosynthetic processes are induced by short-term changes in climatic conditions. Further acclimation can be caused by longterm adjustments to climate change due to ecosystem-feedbacks. The aim of this PhD was to investigate plant...... term responses of plant physiology to the climate change factors were investigated. In the CLIMAITE-experiment it has been shown that 2 years of treatment altered physiological responses in Deschampsiaand Calluna. In the work of this PhD similar responses were observed after 6 years of treatment...... physiological responses to climate change in a seasonal and long-term perspective. The effects of elevated CO2, passive night time warming and periodic summer drought as single factor and in combination, on plant physiology were investigated in the long-term multifactorial field experiment CLIMAITE in a Danish...

Predicting the short-term risk of diabetes in HIV-positive patients

DEFF Research Database (Denmark)

Petoumenos, Kathy; Worm, Signe W; Fontas, Eric

2012-01-01

HIV-positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM and other...... glucose-associated disorders among HIV-positive patients have been reported to range between 2 and 14%, and in an ageing HIV-positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short-term (six-month) risk of DM in HIV...

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

Energy Technology Data Exchange (ETDEWEB)

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

2011-01-15

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

The state-of-the-art in short-term prediction of wind power. A literature overview

Energy Technology Data Exchange (ETDEWEB)

Giebel, G.; Brownsword, R.; Kariniotakis, G.

2003-08-01

Based on an appropriate questionnaire (WP1.1) and some other works already in progress, this report details the state-of-the-art in short term prediction of wind power, mostly summarising nearly all existing literature on the topic. (au)

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

Science.gov (United States)

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

2016-03-01

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

Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

Energy Technology Data Exchange (ETDEWEB)

Melin, Alexander M. [ORNL; Olama, Mohammed M. [ORNL; Dong, Jin [ORNL; Djouadi, Seddik M. [ORNL; Zhang, Yichen [University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science

2017-09-01

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.

Predicting impacts of climate change on Fasciola hepatica risk.

Science.gov (United States)

Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

2011-01-10

Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

Predicting impacts of climate change on Fasciola hepatica risk.

Directory of Open Access Journals (Sweden)

Naomi J Fox

2011-01-01

Full Text Available Fasciola hepatica (liver fluke is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

Short-term Memory as a Processing Shift

Science.gov (United States)

Lewis-Smith, Marion Quinn

1975-01-01

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

Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-12-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http

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

Directory of Open Access Journals (Sweden)

Rui Xue

2015-01-01

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

Impacts of short-term heatwaves on sun-induced chlorophyll fluorescence(SiF) in temperate tree species

Science.gov (United States)

Wang, F.; Gu, L.; Guha, A.; Han, J.; Warren, J.

2017-12-01

The current projections for global climate change forecast an increase in the intensity and frequency of extreme climatic events, such as droughts and short-term heat waves. Understanding the effects of short-term heat wave on photosynthesis process is of critical importance to predict global impacts of extreme weather event on vegetation. The diurnal and seasonal characteristics of SIF emitted from natural vegetation, e.g., forest and crop, have been studied at the ecosystem-scale, regional-scale and global-scale. However, the detailed response of SIF from different plant species under extremely weather event, especially short-term heat wave, have not been reported. The purpose of this study was to study the response of solar-induced chlorophyll fluorescence, gas exchange and continuous fluorescence at leaf scale for different temperate tree species. The short-term heatwave experiment was conducted using plant growth chamber (CMP6050, Conviron Inc., Canada). We developed an advanced spectral fitting method to obtain the plant SIF in the plant growth chamber. We compared SIF variation among different wavelength and chlorophyll difference among four temperate tree species. The diurnal variation of SIF signals at leaf-scales for temperate tree species are different under heat stress. The SIF response at leaf-scales and their difference for four temperate tree species are different during a cycle of short-term heatwave stress. We infer that SIF be used as a measure of heat tolerance for temperate tree species.

Short-term load forecasting of power system

Science.gov (United States)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

Neural activity in the hippocampus predicts individual visual short-term memory capacity.

Science.gov (United States)

von Allmen, David Yoh; Wurmitzer, Karoline; Martin, Ernst; Klaver, Peter

2013-07-01

Although the hippocampus had been traditionally thought to be exclusively involved in long-term memory, recent studies raised controversial explanations why hippocampal activity emerged during short-term memory tasks. For example, it has been argued that long-term memory processes might contribute to performance within a short-term memory paradigm when memory capacity has been exceeded. It is still unclear, though, whether neural activity in the hippocampus predicts visual short-term memory (VSTM) performance. To investigate this question, we measured BOLD activity in 21 healthy adults (age range 19-27 yr, nine males) while they performed a match-to-sample task requiring processing of object-location associations (delay period  =  900 ms; set size conditions 1, 2, 4, and 6). Based on individual memory capacity (estimated by Cowan's K-formula), two performance groups were formed (high and low performers). Within whole brain analyses, we found a robust main effect of "set size" in the posterior parietal cortex (PPC). In line with a "set size × group" interaction in the hippocampus, a subsequent Finite Impulse Response (FIR) analysis revealed divergent hippocampal activation patterns between performance groups: Low performers (mean capacity  =  3.63) elicited increased neural activity at set size two, followed by a drop in activity at set sizes four and six, whereas high performers (mean capacity  =  5.19) showed an incremental activity increase with larger set size (maximal activation at set size six). Our data demonstrated that performance-related neural activity in the hippocampus emerged below capacity limit. In conclusion, we suggest that hippocampal activity reflected successful processing of object-location associations in VSTM. Neural activity in the PPC might have been involved in attentional updating. Copyright © 2013 Wiley Periodicals, Inc.

The value of perfusion CT in predicting the short-term response to synchronous radiochemotherapy for cervical squamous cancer

International Nuclear Information System (INIS)

Li, Xiang Sheng; Fan, Hong Xia; Zhu, Hong Xian; Song, Yun Long; Zhou, Chun Wu

2012-01-01

To determine the value of the perfusion parameters in predicting short-term tumour response to synchronous radiochemotherapy for cervical squamous carcinoma. Ninety-three patients with cervical squamous carcinoma later than stage IIB were included in this study. Perfusion CT was performed for all these patients who subsequently received the same synchronous radiochemotherapy. The patients were divided into responders and non-responders according to short-term response to treatment. Baseline perfusion parameters of the two groups were compared. The perfusion parameters that might affect treatment effect were analysed by using a multivariate multi-regression analysis. The responders group had higher baseline permeability-surface area product (PS) and blood volume (BV) values than the non-responders group (P 0.05). At multivariate multi-regression analysis, BV, PS and tumour size were significant factors in the prediction of treatment effect. Small tumours usually had high PS and BV values, and thus had a good treatment response. Perfusion CT can provide some helpful information for the prediction of the short-term effect. Synchronous radiochemotherapy may be more effective in cervical squamous carcinoma with higher baseline PS and BV. (orig.)

Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios.

Science.gov (United States)

Muñoz-Mas, R; Lopez-Nicolas, A; Martínez-Capel, F; Pulido-Velazquez, M

2016-02-15

The impact of climate change on the habitat suitability for large brown trout (Salmo trutta L.) was studied in a segment of the Cabriel River (Iberian Peninsula). The future flow and water temperature patterns were simulated at a daily time step with M5 models' trees (NSE of 0.78 and 0.97 respectively) for two short-term scenarios (2011-2040) under the representative concentration pathways (RCP 4.5 and 8.5). An ensemble of five strongly regularized machine learning techniques (generalized additive models, multilayer perceptron ensembles, random forests, support vector machines and fuzzy rule base systems) was used to model the microhabitat suitability (depth, velocity and substrate) during summertime and to evaluate several flows simulated with River2D©. The simulated flow rate and water temperature were combined with the microhabitat assessment to infer bivariate habitat duration curves (BHDCs) under historical conditions and climate change scenarios using either the weighted usable area (WUA) or the Boolean-based suitable area (SA). The forecasts for both scenarios jointly predicted a significant reduction in the flow rate and an increase in water temperature (mean rate of change of ca. -25% and +4% respectively). The five techniques converged on the modelled suitability and habitat preferences; large brown trout selected relatively high flow velocity, large depth and coarse substrate. However, the model developed with support vector machines presented a significantly trimmed output range (max.: 0.38), and thus its predictions were banned from the WUA-based analyses. The BHDCs based on the WUA and the SA broadly matched, indicating an increase in the number of days with less suitable habitat available (WUA and SA) and/or with higher water temperature (trout will endure impoverished environmental conditions ca. 82% of the days). Finally, our results suggested the potential extirpation of the species from the study site during short time spans. Copyright © 2015

The Sources of Life Chances: Does Education, Class Category, Occupation, or Short-Term Earnings Predict 20-Year Long-Term Earnings?

Directory of Open Access Journals (Sweden)

ChangHwan Kim

2018-03-01

Full Text Available In sociological studies of economic stratification and intergenerational mobility, occupation has long been presumed to reflect lifetime earnings better than do short-term earnings. However, few studies have actually tested this critical assumption. In this study, we investigate the cross-sectional determinants of 20-year accumulated earnings using data that match respondents in the Survey of Income and Program Participation to their longitudinal earnings records based on administrative tax information from 1990 to 2009. Fit statistics of regression models are estimated to assess the predictive power of various proxy variables, including occupation, education, and short-term earnings, on cumulative earnings over the 20-year time period. Contrary to the popular assumption in sociology, our results find that cross-sectional earnings have greater predictive power on long-term earnings than occupation-based class classifications, including three-digit detailed occupations for both men and women. The model based on educational attainment, including field of study, has slightly better fit than models based on one-digit occupation or the Erikson, Goldthorpe, and Portocarero class scheme. We discuss the theoretical implications of these findings for the sociology of stratification and intergenerational mobility.

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

Science.gov (United States)

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

2017-12-01

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

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

Directory of Open Access Journals (Sweden)

Qiang Shang

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

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

Science.gov (United States)

Lague, D.; Davy, P.

2008-12-01

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

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

Science.gov (United States)

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

2018-03-01

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

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

Science.gov (United States)

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

2016-03-01

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

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory

Directory of Open Access Journals (Sweden)

Haimin Yang

2017-01-01

Full Text Available Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam, for long short-term memory (LSTM to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.

Science.gov (United States)

Yang, Haimin; Pan, Zhisong; Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

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

Science.gov (United States)

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

2017-07-15

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

Predicting short-term stock fluctuations by using processing fluency

Science.gov (United States)

Alter, Adam L.; Oppenheimer, Daniel M.

2006-01-01

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

Short-Term Predictive Validity of Cluster Analytic and Dimensional Classification of Child Behavioral Adjustment in School

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Kim, Sangwon; Kamphaus, Randy W.; Baker, Jean A.

2006-01-01

A constructive debate over the classification of child psychopathology can be stimulated by investigating the validity of different classification approaches. We examined and compared the short-term predictive validity of cluster analytic and dimensional classifications of child behavioral adjustment in school using the Behavior Assessment System…

Predictive Validity of the Columbia-Suicide Severity Rating Scale for Short-Term Suicidal Behavior

DEFF Research Database (Denmark)

Conway, Paul Maurice; Erlangsen, Annette; Teasdale, Thomas William

2017-01-01

adolescents (90.6% females) who participated at follow-up (85.9%) out of the 99 (49.7%) baseline respondents. All adolescents were recruited from a specialized suicide-prevention clinic in Denmark. Through multivariate logistic regression analyses, we examined whether baseline suicidal behavior predicted......Using the Columbia-Suicide Severity Rating Scale (C-SSRS), we examined the predictive and incremental predictive validity of past-month suicidal behavior and ideation for short-term suicidal behavior among adolescents at high risk of suicide. The study was conducted in 2014 on a sample of 85...... subsequent suicidal behavior (actual attempts and suicidal behavior of any type, including preparatory acts, aborted, interrupted and actual attempts; mean follow-up of 80.8 days, SD = 52.4). Furthermore, we examined whether suicidal ideation severity and intensity incrementally predicted suicidal behavior...

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

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Federico Divina

2018-04-01

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

Hyperdense basilar artery sign diagnoses acute posterior circulation stroke and predicts short-term outcome

Energy Technology Data Exchange (ETDEWEB)

Tan, Xiaoping [Affiliated Hospital of China Medical University at Shenyang, Department of Neurology, Shengjing Hospital, Shenyang (China); Guo, Yang [Shengjing Hospital, Department of Neurology, Shenyang (China)

2010-12-15

It is well established that the hyperdense middle cerebral artery sign is a specific marker for early ischemia in anterior circulation. However, little is known about the hyperdense basilar artery sign (HDBA) in posterior circulation. Our aim was to determine whether the HDBA sign has utility in early diagnosis of acute posterior circulation stroke and prediction of short-term outcome. Three-blinded readers examined unenhanced computed tomography scans for the HDBA sign, and materials were classified into two groups according to this sign. Vascular risk factors, admission and discharge National Institute of Health Stroke Scale (NIHSS) scores, short-term outcome, and radiological findings between the two groups were compared. One hundred and twenty-six cases of acute posterior circulation stroke (PCS) were included in the study. No statistically significant differences were found in risk factors of ischemic stroke, except atrial fibrillation (P = 0.025). Admission and discharge NIHSS scores for the positive HDBA group were significantly higher than scores for the negative HDBA group (P = 0.001, 0.002, respectively). The infarction territory for the positive HDBA group was mainly multi-region in nature (51.6%, P < 0.001), while the negative HDBA group showed mainly middle territory infarction. Significant independent predictors of short-term outcome included the HDBA sign (P < 0.001) and admission NIHSS scores (P < 0.001). Approximately half of the HDBA patients showed multi-region infarction and a serious neurological symptom. Based on our results, this sign might not only be helpful in early diagnosis of acute PCS but also be able to correlate with a poor short-term outcome. (orig.)

Dynamical prediction and pattern mapping in short-term load forecasting

Energy Technology Data Exchange (ETDEWEB)

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

2008-01-15

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

Multi-step prediction for influenza outbreak by an adjusted long short-term memory.

Science.gov (United States)

Zhang, J; Nawata, K

2018-05-01

Influenza results in approximately 3-5 million annual cases of severe illness and 250 000-500 000 deaths. We urgently need an accurate multi-step-ahead time-series forecasting model to help hospitals to perform dynamical assignments of beds to influenza patients for the annually varied influenza season, and aid pharmaceutical companies to formulate a flexible plan of manufacturing vaccine for the yearly different influenza vaccine. In this study, we utilised four different multi-step prediction algorithms in the long short-term memory (LSTM). The result showed that implementing multiple single-output prediction in a six-layer LSTM structure achieved the best accuracy. The mean absolute percentage errors from two- to 13-step-ahead prediction for the US influenza-like illness rates were all LSTM has been applied and refined to perform multi-step-ahead prediction for influenza outbreaks. Hopefully, this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.

Evaluating short-term hydro-meteorological fluxes using GRACE-derived water storage changes

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Eicker, A.; Jensen, L.; Springer, A.; Kusche, J.

2017-12-01

Atmospheric and terrestrial water budgets, which represent important boundary conditions for both climate modeling and hydrological studies, are linked by evapotranspiration (E) and precipitation (P). These fields are provided by numerical weather prediction models and atmospheric reanalyses such as ERA-Interim and MERRA-Land; yet, in particular the quality of E is still not well evaluated. Via the terrestrial water budget equation, water storage changes derived from products of the Gravity Recovery and Climate Experiment (GRACE) mission, combined with runoff (R) data can be used to assess the realism of atmospheric models. In this contribution we will investigate the closure of the water balance for short-term fluxes, i.e. the agreement of GRACE water storage changes with P-E-R flux time series from different (global and regional) atmospheric reanalyses, land surface models, as well as observation-based data sets. Missing river runoff observations will be extrapolated using the calibrated rainfall-runoff model GR2M. We will perform a global analysis and will additionally focus on selected river basins in West Africa. The investigations will be carried out for various temporal scales, focusing on short-term fluxes down to daily variations to be detected in daily GRACE time series.

Markers of preparatory attention predict visual short-term memory performance.

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Murray, Alexandra M; Nobre, Anna C; Stokes, Mark G

2011-05-01

Visual short-term memory (VSTM) is limited in capacity. Therefore, it is important to encode only visual information that is most likely to be relevant to behaviour. Here we asked which aspects of selective biasing of VSTM encoding predict subsequent memory-based performance. We measured EEG during a selective VSTM encoding task, in which we varied parametrically the memory load and the precision of recall required to compare a remembered item to a subsequent probe item. On half the trials, a spatial cue indicated that participants only needed to encode items from one hemifield. We observed a typical sequence of markers of anticipatory spatial attention: early attention directing negativity (EDAN), anterior attention directing negativity (ADAN), late directing attention positivity (LDAP); as well as of VSTM maintenance: contralateral delay activity (CDA). We found that individual differences in preparatory brain activity (EDAN/ADAN) predicted cue-related changes in recall accuracy, indexed by memory-probe discrimination sensitivity (d'). Importantly, our parametric manipulation of memory-probe similarity also allowed us to model the behavioural data for each participant, providing estimates for the quality of the memory representation and the probability that an item could be retrieved. We found that selective encoding primarily increased the probability of accurate memory recall; that ERP markers of preparatory attention predicted the cue-related changes in recall probability. Copyright © 2011. Published by Elsevier Ltd.

Effects of climate change on long-term population growth of pronghorn in an arid environment

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Gedir, Jay V.; Cain, James W.; Harris, Grant; Turnbull, Trey T.

2015-01-01

Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 populations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn populations declined in abundance. Sixteen populations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn populations would experience increased temperatures, while the direction and magnitude of precipitation had high population-specific variation. Models predicted that nine populations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn populations is shifting, making conditions increasingly

Prediction of long-term creep curves

International Nuclear Information System (INIS)

Oikawa, Hiroshi; Maruyama, Kouichi

1992-01-01

This paper aims at discussing how to predict long-term irradiation enhanced creep properties from short-term tests. The predictive method based on the θ concept was examined by using creep data of ferritic steels. The method was successful in predicting creep curves including the tertiary creep stage as well as rupture lifetimes. Some material constants involved in the method are insensitive to the irradiation environment, and their values obtained in thermal creep are applicable to irradiation enhanced creep. The creep mechanisms of most engineering materials definitely change at the athermal yield stress in the non-creep regime. One should be aware that short-term tests must be carried out at stresses lower than the athermal yield stress in order to predict the creep behavior of structural components correctly. (orig.)

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

Science.gov (United States)

Ak, Ronay; Fink, Olga; Zio, Enrico

2016-08-01

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

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

Science.gov (United States)

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

2016-09-01

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

Future climate change is predicted to shift long-term persistence zones in the cold-temperate kelp Laminaria hyperborea.

Science.gov (United States)

Assis, Jorge; Lucas, Ana Vaz; Bárbara, Ignacio; Serrão, Ester Álvares

2016-02-01

Global climate change is shifting species distributions worldwide. At rear edges (warmer, low latitude range margins), the consequences of small variations in environmental conditions can be magnified, producing large negative effects on species ranges. A major outcome of shifts in distributions that only recently received attention is the potential to reduce the levels of intra-specific diversity and consequently the global evolutionary and adaptive capacity of species to face novel disturbances. This is particularly important for low dispersal marine species, such as kelps, that generally retain high and unique genetic diversity at rear ranges resulting from long-term persistence, while ranges shifts during climatic glacial/interglacial cycles. Using ecological niche modelling, we (1) infer the major environmental forces shaping the distribution of a cold-temperate kelp, Laminaria hyperborea (Gunnerus) Foslie, and we (2) predict the effect of past climate changes in shaping regions of long-term persistence (i.e., climatic refugia), where this species might hypothetically harbour higher genetic diversity given the absence of bottlenecks and local extinctions over the long term. We further (3) assessed the consequences of future climate for the fate of L. hyperborea using different scenarios of greenhouse gas emissions (RCP 2.6 and RCP 8.5). Results show NW Iberia, SW Ireland and W English Channel, Faroe Islands and S Iceland, as regions where L. hyperborea may have persisted during past climate extremes until present day. All predictions for the future showed expansions to northern territories coupled with the significant loss of suitable habitats at low latitude range margins, where long-term persistence was inferred (e.g., NW Iberia). This pattern was particularly evident in the most agressive scenario of climate change (RCP 8.5), likely driving major biodiversity loss, changes in ecosystem functioning and the impoverishment of the global gene pool of L

Evolution of extreme temperature events in short term climate projection for Iberian Peninsula.

Science.gov (United States)

Rodriguez, Alfredo; Tarquis, Ana M.; Sanchez, Enrique; Dosio, Alessandro; Ruiz-Ramos, Margarita

2014-05-01

Extreme events of maximum and minimum temperatures are a main hazard for agricultural production in Iberian Peninsula. For this purpose, in this study we analyze projections of their evolution that could be valid for the next decade, represented in this study by the 30-year period 2004-2034 (target period). For this purpose two kinds of data were used in this study: 1) observations from the station network of AEMET (Spanish National Meteorological Agency) for five Spanish locations, and 2) simulated data at a resolution of 50 ×50 km horizontal grid derived from the outputs of twelve Regional Climate Models (RCMs) taken from project ENSEMBLES (van der Linden and Mitchell, 2009), with a bias correction (Dosio and Paruolo, 2011; Dosio et al., 2012) regarding the observational dataset Spain02 (Herrera et al., 2012). To validate the simulated climate, the available period of observations was compared to a baseline period (1964-1994) of simulated climate for all locations. Then, to analyze the changes for the present/very next future, probability of extreme temperature events for 2004-2034 were compared to that of the baseline period. Although only minor changes are expected, small variations in variability may have a significant impact in crop performance. The objective of the work is to evaluate the utility of these short term projections for potential users, as for instance insurance companies. References Dosio A. and Paruolo P., 2011. Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate. Journal of Geophysical Research, VOL. 116,D16106, doi:10.1029/2011JD015934 Dosio A., Paruolo P. and Rojas R., 2012. Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: Analysis of the climate change signal. Journal of Geophysical Research,Volume 117, D17, doi: 0.1029/2012JD017968 Herrera et. al. (2012) Development and Analysis of a 50 year high

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

Directory of Open Access Journals (Sweden)

César Hernández-Hernández

2017-06-01

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

Serial-order short-term memory predicts vocabulary development: evidence from a longitudinal study.

Science.gov (United States)

Leclercq, Anne-Lise; Majerus, Steve

2010-03-01

Serial-order short-term memory (STM), as opposed to item STM, has been shown to be very consistently associated with lexical learning abilities in cross-sectional study designs. This study investigated longitudinal predictions between serial-order STM and vocabulary development. Tasks maximizing the temporary retention of either serial-order or item information were administered to kindergarten children aged 4 and 5. At age 4, age 5, and from age 4 to age 5, serial-order STM capacities, but not item STM capacities, were specifically associated with vocabulary development. Moreover, the increase of serial-order STM capacity from age 4 to age 5 predicted the increase of vocabulary knowledge over the same time period. These results support a theoretical position that assumes an important role for serial-order STM capacities in vocabulary acquisition.

Detecting failure of climate predictions

Science.gov (United States)

Runge, Michael C.; Stroeve, Julienne C.; Barrett, Andrew P.; McDonald-Madden, Eve

2016-01-01

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty1, 2. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies3. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

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

Science.gov (United States)

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

2016-01-01

This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public.

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

Directory of Open Access Journals (Sweden)

Morken Gunnar

2011-03-01

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

Beyond Quarterly Earnings: Preparing the Business Community for Long-term Climate Risks

Science.gov (United States)

Carlson, C.; Goldman, G. T.

2014-12-01

The business community stands to be highly impacted by climate change. In both short and long-term timescales, climate change presents material and financial risks to companies in diverse economic sectors. How the private sector accounts for long-term risks while making short-term decisions about operations is a complex challenge. Companies are accountable to shareholders and must report performance to them on a quarterly basis. At the same time, company investors are exposed to long-term climate-related risks and face losses if companies fail to prepare for climate impacts. The US Securities and Exchange Commission (SEC) obligates publicly traded companies to discuss risks that might materially affect their business and since 2010, the agency recommends that companies consider and discuss any significant risks to their business from climate change. Some companies have complied with this guidance and comprehensively analyze potential climate change impacts, yet others fail to consider climate change at all. Such omissions leave companies without plans for addressing future risks and expose investors and the public to potential catastrophic events from climate change impacts. Climate risk projections can inform companies about the vulnerability of their facilities, supply chains, transportation pathways, and other assets. Such projections can help put climate-related risks in terms of material costs for companies and their investors. Focusing on the vulnerability of coastal facilities, we will use climate change impact projections to demonstrate the economic impacts of climate change faced by the private sector. These risks are then compared to company disclosures to the SEC to assess the degree to which companies have considered their vulnerability to climate change. Finally, we will discuss ways that companies can better assess and manage long-term climate risks.

Withdrawal-Related Changes in Delay Discounting Predict Short-Term Smoking Abstinence.

Science.gov (United States)

Miglin, Rickie; Kable, Joseph W; Bowers, Maureen E; Ashare, Rebecca L

2017-06-01

Impulsive decision making is associated with smoking behavior and reflects preferences for smaller, immediate rewards and intolerance of temporal delays. Nicotine withdrawal may alter impulsive decision making and time perception. However, little is known about whether withdrawal-related changes in decision making and time perception predict smoking relapse. Forty-five smokers (14 female) completed two laboratory sessions, one following 24-hour abstinence and one smoking-as-usual (order counterbalanced; biochemically verified abstinence). During each visit, participants completed measures of time perception, decision making (ie, discount rates), craving, and withdrawal. Following the second laboratory session, subjects underwent a well-validated model of short-term abstinence (quit week) with small monetary incentives for each day of biochemically confirmed abstinence. Smokers significantly overestimated time during abstinence, compared to smoking-as-usual (p = .021), but there were no abstinence effects on discount rates (p = .6). During the quit week, subjects were abstinent for 3.5 days (SD = 2.15) and smoked a total of 12.9 cigarettes (SD = 15.8). Importantly, higher discount rates (ie, preferences for immediate rewards) during abstinence (abstinence minus smoking difference score) predicted greater number of days abstinent (p = .01) and fewer cigarettes smoked during the quit week (p = .02). Withdrawal-related change in time reproduction did not predict relapse (p = .2). These data suggest that individuals who have a greater preference for immediate rewards during abstinence (vs. smoking-as-usual) may be more successful at maintaining short-term abstinence when provided with frequent (eg, daily) versus less frequent incentive schedules (eg, 1 month). Abstinence-induced changes in decision making may be important for identifying smokers who may benefit from interventions that incentivize abstinence such as contingency management (CM). The present results

Dispositional optimism as predictor of outcome in short- and long-term psychotherapy.

Science.gov (United States)

Heinonen, Erkki; Heiskanen, Tiia; Lindfors, Olavi; Härkäpää, Kristiina; Knekt, Paul

2017-09-01

Dispositional optimism predicts various beneficial outcomes in somatic health and treatment, but has been little studied in psychotherapy. This study investigated whether an optimistic disposition differentially predicts patients' ability to benefit from short-term versus long-term psychotherapy. A total of 326 adult outpatients with mood and/or anxiety disorder were randomized into short-term (solution-focused or short-term psychodynamic) or long-term psychodynamic therapy and followed up for 3 years. Dispositional optimism was assessed by patients at baseline with the self-rated Life Orientation Test (LOT) questionnaire. Outcome was assessed at baseline and seven times during the follow-up, in terms of depressive (BDI, HDRS), anxiety (SCL-90-ANX, HARS), and general psychiatric symptoms (SCL-90-GSI), all seven follow-up points including patients' self-reports and three including interview-based measures. Lower dispositional optimism predicted faster symptom reduction in short-term than in long-term psychotherapy. Higher optimism predicted equally rapid and eventually greater benefits in long-term, as compared to short-term, psychotherapy. Weaker optimism appeared to predict sustenance of problems early in long-term therapy. Stronger optimism seems to best facilitate engaging in and benefiting from a long-term therapy process. Closer research might clarify the psychological processes responsible for these effects and help fine-tune both briefer and longer interventions to optimize treatment effectiveness for particular patients and their psychological qualities. Weaker dispositional optimism does not appear to inhibit brief therapy from effecting symptomatic recovery. Patients with weaker optimism do not seem to gain added benefits from long-term therapy, but instead may be susceptible to prolonged psychiatric symptoms in the early stages of long-term therapy. © 2016 The British Psychological Society.

Air quality: how to assess air quality management policies on a short and on a long term? The integration of the climate factor

International Nuclear Information System (INIS)

Prevot, Aurelie

2014-01-01

This document presents the activities and works performed by the INERIS Institute in the development of tools for the assessment of air quality management policies including the climate factor. This comprises the development of simulations within the frame of the SALUT'AIR project, and also within the frame of the reviewing of the European policy on air quality (directives 2008/50/CE on ambient air quality and 2001/81/CE on national limits of emissions of some pollutants). The CHIMERE model of chemistry and transport is one of these tools. Simulations are performed to analyse the impact of scenarios of air quality management on a short term, in terms of pollutant emissions, pollutant concentration, and particle concentrations. The integration of a climate factor is justified by the existence of interactions between climate and air quality

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

Science.gov (United States)

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

2012-01-01

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

Improving creativity performance by short-term meditation

Science.gov (United States)

2014-01-01

Background One form of meditation intervention, the integrative body-mind training (IBMT) has been shown to improve attention, reduce stress and change self-reports of mood. In this paper we examine whether short-term IBMT can improve performance related to creativity and determine the role that mood may play in such improvement. Methods Forty Chinese undergraduates were randomly assigned to short-term IBMT group or a relaxation training (RT) control group. Mood and creativity performance were assessed by the Positive and Negative Affect Schedule (PANAS) and Torrance Tests of Creative Thinking (TTCT) questionnaire respectively. Results As predicted, the results indicated that short-term (30 min per day for 7 days) IBMT improved creativity performance on the divergent thinking task, and yielded better emotional regulation than RT. In addition, cross-lagged analysis indicated that both positive and negative affect may influence creativity in IBMT group (not RT group). Conclusions Our results suggested that emotion-related creativity-promoting mechanism may be attributed to short-term meditation. PMID:24645871

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

Science.gov (United States)

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

2017-03-01

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

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

International Nuclear Information System (INIS)

Zheng, Xiujuan; Fang, Huajing

2015-01-01

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

Short-term changes in arterial inflammation predict long-term changes in atherosclerosis progression

Energy Technology Data Exchange (ETDEWEB)

Joseph, Philip [Massachusetts General Hospital and Harvard Medical School, Cardiology Division and Cardiac MR PET CT Program, Boston, MA (United States); McMaster University, Population Health Research Institute, Department of Medicine, and Department of Radiology, Hamilton, ON (Canada); Ishai, Amorina; Tawakol, Ahmed [Massachusetts General Hospital and Harvard Medical School, Cardiology Division and Cardiac MR PET CT Program, Boston, MA (United States); Mani, Venkatesh [Icahn School of Medicine at Mount Sinai School of Medicine, Translational and Molecular Imaging Institute and Department of Radiology, New York, NY (United States); Kallend, David [The Medicines Company, Parsippany, NJ (United States); Rudd, James H.F. [University of Cambridge, Division of Cardiovascular Medicine, Cambridge (United Kingdom); Fayad, Zahi A. [Icahn School of Medicine at Mount Sinai School of Medicine, Translational and Molecular Imaging Institute and Department of Radiology, New York, NY (United States); Icahn School of Medicine at Mount Sinai School of Medicine, Hess CSM Building Floor TMII, Rm S1-104, Translational and Molecular Imaging Institute and Department of Radiology, New York, NY (United States)

2017-01-15

It remains unclear whether changes in arterial wall inflammation are associated with subsequent changes in the rate of structural progression of atherosclerosis. In this sub-study of the dal-PLAQUE clinical trial, multi-modal imaging was performed using 18-fludeoxyglucose (FDG) positron emission tomography (PET, at 0 and 6 months) and magnetic resonance imaging (MRI, at 0 and 24 months). The primary objective was to determine whether increasing FDG uptake at 6 months predicted atherosclerosis progression on MRI at 2 years. Arterial inflammation was measured by the carotid FDG target-to-background ratio (TBR), and atherosclerotic plaque progression was defined as the percentage change in carotid mean wall area (MWA) and mean wall thickness (MWT) on MRI between baseline and 24 months. A total of 42 participants were included in this sub-study. The mean age of the population was 62.5 years, and 12 (28.6 %) were women. In participants with (vs. without) any increase in arterial inflammation over 6 months, the long-term changes in both MWT (% change MWT: 17.49 % vs. 1.74 %, p = 0.038) and MWA (% change MWA: 25.50 % vs. 3.59 %, p = 0.027) were significantly greater. Results remained significant after adjusting for clinical and biochemical covariates. Individuals with no increase in arterial inflammation over 6 months had no significant structural progression of atherosclerosis over 24 months as measured by MWT (p = 0.616) or MWA (p = 0.373). Short-term changes in arterial inflammation are associated with long-term structural atherosclerosis progression. These data support the concept that therapies that reduce arterial inflammation may attenuate or halt progression of atherosclerosis. (orig.)

Short-term changes in arterial inflammation predict long-term changes in atherosclerosis progression

International Nuclear Information System (INIS)

Joseph, Philip; Ishai, Amorina; Tawakol, Ahmed; Mani, Venkatesh; Kallend, David; Rudd, James H.F.; Fayad, Zahi A.

2017-01-01

It remains unclear whether changes in arterial wall inflammation are associated with subsequent changes in the rate of structural progression of atherosclerosis. In this sub-study of the dal-PLAQUE clinical trial, multi-modal imaging was performed using 18-fludeoxyglucose (FDG) positron emission tomography (PET, at 0 and 6 months) and magnetic resonance imaging (MRI, at 0 and 24 months). The primary objective was to determine whether increasing FDG uptake at 6 months predicted atherosclerosis progression on MRI at 2 years. Arterial inflammation was measured by the carotid FDG target-to-background ratio (TBR), and atherosclerotic plaque progression was defined as the percentage change in carotid mean wall area (MWA) and mean wall thickness (MWT) on MRI between baseline and 24 months. A total of 42 participants were included in this sub-study. The mean age of the population was 62.5 years, and 12 (28.6 %) were women. In participants with (vs. without) any increase in arterial inflammation over 6 months, the long-term changes in both MWT (% change MWT: 17.49 % vs. 1.74 %, p = 0.038) and MWA (% change MWA: 25.50 % vs. 3.59 %, p = 0.027) were significantly greater. Results remained significant after adjusting for clinical and biochemical covariates. Individuals with no increase in arterial inflammation over 6 months had no significant structural progression of atherosclerosis over 24 months as measured by MWT (p = 0.616) or MWA (p = 0.373). Short-term changes in arterial inflammation are associated with long-term structural atherosclerosis progression. These data support the concept that therapies that reduce arterial inflammation may attenuate or halt progression of atherosclerosis. (orig.)

Short-term Automated Quantification of Radiologic Changes in the Characterization of Idiopathic Pulmonary Fibrosis Versus Nonspecific Interstitial Pneumonia and Prediction of Long-term Survival.

Science.gov (United States)

De Giacomi, Federica; Raghunath, Sushravya; Karwoski, Ronald; Bartholmai, Brian J; Moua, Teng

2018-03-01

Fibrotic interstitial lung diseases presenting with nonspecific and overlapping radiologic findings may be difficult to diagnose without surgical biopsy. We hypothesized that baseline quantifiable radiologic features and their short-term interval change may be predictive of underlying histologic diagnosis as well as long-term survival in idiopathic pulmonary fibrosis (IPF) presenting without honeycombing versus nonspecific interstitial pneumonia (NSIP). Forty biopsy-confirmed IPF and 20 biopsy-confirmed NSIP patients with available high-resolution chest computed tomography 4 to 24 months apart were studied. CALIPER software was used for the automated characterization and quantification of radiologic findings. IPF subjects were older (66 vs. 48; P<0.0001) with lower diffusion capacity for carbon monoxide and higher volumes of baseline reticulation (193 vs. 83 mL; P<0.0001). Over the interval period, compared with NSIP, IPF patients experienced greater functional decline (forced vital capacity, -6.3% vs. -1.7%; P=0.02) and radiologic progression, as noted by greater increase in reticulation volume (24 vs. 1.74 mL; P=0.048), and decrease in normal (-220 vs. -37.7 mL; P=0.045) and total lung volumes (-198 vs. 58.1 mL; P=0.03). Older age, male gender, higher reticulation volumes at baseline, and greater interval decrease in normal lung volumes were predictive of IPF. Both baseline and short-term changes in quantitative radiologic findings were predictive of mortality. Baseline quantitative radiologic findings and assessment of short-term disease progression may help characterize underlying IPF versus NSIP in those with difficult to differentiate clinicoradiologic presentations. Our study supports the possible utility of assessing serial quantifiable high-resolution chest computed tomographic findings for disease differentiation in these 2 entities.

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

Science.gov (United States)

ElSaid, AbdElRahman Ahmed

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

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

Directory of Open Access Journals (Sweden)

Jinxing Shen

2013-01-01

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

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

Science.gov (United States)

Gerber, Brian D.; Kendall, William L.; Hooten, Mevin B.; Dubovsky, James A.; Drewien, Roderick C.

2015-01-01

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (time-scales and spring/summer weather affects recruitment.Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression.Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect.Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond

Climate Prediction Center - Outreach: 41st Annual Climate Diagnostics &

Science.gov (United States)

home page National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Annual Climate Diagnostics & Prediction Workshop NOAA's 41st Climate Diagnostics and Prediction Climate Diagnostics Prediction Workshop (CDPW) news, visit the CDPW list server Abstract Submission Has

Relationship between short and long term radon measurements

International Nuclear Information System (INIS)

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

2000-01-01

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

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

International Nuclear Information System (INIS)

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

2010-01-01

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

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

OpenAIRE

Stewart, Ian; Arendt, Dustin; Bell, Eric; Volkova, Svitlana

2017-01-01

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. ...

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

Science.gov (United States)

Pan, Zhiyuan; Liu, Li

2018-02-01

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

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

Science.gov (United States)

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

2014-05-01

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

Audit of long-term and short-term liabilities

Directory of Open Access Journals (Sweden)

Korinko M.D.

2017-03-01

Full Text Available The article determines the importance of long-term and short-term liabilities for the management of financial and material resources of an enterprise. It reviews the aim, objects and information generators for realization of audit of short-term and long-term obligations. The organizing and methodical providing of audit of long-term and short-term liabilities of an enterprise are generalized. The authors distinguish the stages of realization of audit of long-term and short-term liabilities, the aim of audit on each of the presented stages, and recommend methodical techniques. It is fixed that it is necessary to conduct the estimation of the systems of internal control and record-keeping of an enterprise by implementation of public accountant procedures for determination of volume and maintenance of selection realization. After estimating the indicated systems, a public accountant determines the methodology for realization of public accountant verification of long-term and short-term liabilities. The analytical procedures that public accountants are expedient to use for realization of audit of short-term and long-term obligations are determined. The authors suggest the classification of the educed defects on the results of the conducted public accountant verification of short-term and long-term obligations.

A score to predict short-term risk of COPD exacerbations (SCOPEX

Directory of Open Access Journals (Sweden)

Make BJ

2015-01-01

properties of predictive variables. Results: The best predictors of an exacerbation in the next 6 months were more COPD maintenance medications prior to the trial, higher mean daily reliever use, more exacerbations during the previous year, lower forced expiratory volume in 1 second/forced vital capacity ratio, and female sex. Using these risk variables, we developed a score to predict short-term (6-month risk of COPD exacerbations (SCOPEX. Budesonide/formoterol reduced future exacerbation risk more than formoterol or as-needed short-acting ß2-agonist (salbutamol. Conclusion: SCOPEX incorporates easily identifiable patient characteristics and can be readily applied in clinical practice to target therapy to reduce COPD exacerbations in patients at the highest risk. Keywords: chronic obstructive pulmonary disease, exacerbation, model, predictor, inhaled corticosteroids, bronchodilators 

State-of-the-Art Climate Predictions for Energy Climate Services

Science.gov (United States)

Torralba-Fernandez, Veronica; Davis, Melanie; Doblas-Reyes, Francisco J.; Gonzalez-Reviriego, Nube

2015-04-01

Climate predictions tailored to the energy sector represent the cutting edge in climate sciences to forecast wind power generation. At seasonal time scales, current energy practices use a deterministic approach based on retrospective climatology, but climate predictions have recently been shown to provide additional value. For this reason, probabilistic climate predictions of near surface winds can allow end users to take calculated, precautionary action with a potential cost savings to their operations. As every variable predicted in a coupled model forecast system, the prediction of wind speed is affected by biases. To overcome this, two different techniques for the post-processing of ensemble forecasts are considered: a simple bias correction and a calibration method. The former is based on the assumption that the reference and predicted distributions are well approximated by a normal distribution. The latter is a calibration technique which inflates the model variance, and the inflation of the ensemble is required in order to obtain a reliable outcome. Both methods use the "one-year out" cross-validated mode, and they provide corrected forecasts with improved statistical properties. The impact of these bias corrections on the quality of the ECMWF S4 predictions of near surface wind speed during winter is explored. To offer a comprehensive picture of the post-processing effect on the forecast quality of the system, it is necessary to use several scoring measures: rank histograms, reliability diagrams and skill maps. These tools are essential to assess different aspects of the forecasts, and to observe changes in their properties when the two methods are applied. This study reveals that the different techniques to correct the predictions produce a statistically consistent ensemble. However, the operations performed on the forecasts decrease their skill which correspond to an increase in the uncertainty. Therefore, even though the bias correction is fundamental

Climate Prediction - NOAA's National Weather Service

Science.gov (United States)

Statistical Models... MOS Prod GFS-LAMP Prod Climate Past Weather Predictions Weather Safety Weather Radio National Weather Service on FaceBook NWS on Facebook NWS Director Home > Climate > Predictions Climate Prediction Long range forecasts across the U.S. Climate Prediction Web Sites Climate Prediction

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

Science.gov (United States)

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

2014-01-01

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

Assessment of Short Term Flood Operation Strategies Using Numerical Weather Prediction Data in YUVACΙK DAM Reservoir, Turkey

Science.gov (United States)

Uysal, G.; Yavuz, O.; Sensoy, A.; Sorman, A.; Akgun, T.; Gezgin, T.

2011-12-01

Yuvacik Dam Reservoir Basin, located in the Marmara region of Turkey with 248 km2 drainage area, has steep topography, mild and rainy climate thus induces high flood potential with fast flow response, especially to early spring and fall precipitation events. Moreover, the basin provides considerable snowmelt contribution to the streamflow during melt season since the elevation ranges between 80 - 1548 m. The long term strategies are based on supplying annual demand of 142 hm3 water despite a relatively small reservoir capacity of 51 hm3. This situation makes short term release decisions as the challenging task regarding the constrained downstream safe channel capacity especially in times of floods. Providing the demand of 1.5 million populated city of Kocaeli is the highest priority issue in terms of reservoir management but risk optimization is also required due to flood regulation. Although, the spillway capacity is 1560 m3/s, the maximum amount of water to be released is set as 100 m3/s by the regional water authority taking into consideration the downstream channel capacity which passes through industrial region of the city. The reservoir is a controlled one and it is possible to hold back the 15 hm3 additional water by keeping the gates closed. Flood regulation is set to achieve the maximum possible flood attenuation by using the full flood-control zone capacity in the reservoir before making releases in excess of the downstream safe-channel capacity. However, the operators still need to exceed flood regulation zones to take precautions for drought summer periods in order to supply water without any shortage that increases the risk in times of flood. Regarding to this circumstances, a hydrological model integrated reservoir modeling system, is applied to account for the physical behavior of the system. Hence, this reservoir modeling is carried out to analyze both previous decisions and also the future scenarios as a decision support tool for operators. In the

Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

Energy Technology Data Exchange (ETDEWEB)

Prasanna, Prateek; Patel, Jay; Madabhushi, Anant; Tiwari, Pallavi [Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH (United States); Partovi, Sasan [University Hospitals Case Medical Center, Case Western Reserve School of Medicine, Cleveland, OH (United States)

2017-10-15

Despite 90 % of glioblastoma (GBM) recurrences occurring in the peritumoral brain zone (PBZ), its contribution in patient survival is poorly understood. The current study leverages computerized texture (i.e. radiomic) analysis to evaluate the efficacy of PBZ features from pre-operative MRI in predicting long- (>18 months) versus short-term (<7 months) survival in GBM. Sixty-five patient examinations (29 short-term, 36 long-term) with gadolinium-contrast T{sub 1w}, FLAIR and T{sub 2w} sequences from the Cancer Imaging Archive were employed. An expert manually segmented each study as: enhancing lesion, PBZ and tumour necrosis. 402 radiomic features (capturing co-occurrence, grey-level dependence and directional gradients) were obtained for each region. Evaluation was performed using threefold cross-validation, such that a subset of studies was used to select the most predictive features, and the remaining subset was used to evaluate their efficacy in predicting survival. A subset of ten radiomic 'peritumoral' MRI features, suggestive of intensity heterogeneity and textural patterns, was found to be predictive of survival (p = 1.47 x 10{sup -5}) as compared to features from enhancing tumour, necrotic regions and known clinical factors. Our preliminary analysis suggests that radiomic features from the PBZ on routine pre-operative MRI may be predictive of long- versus short-term survival in GBM. (orig.)

Assessment of short- and long-term memory in trends of major climatic variables over Iran: 1966-2015

Science.gov (United States)

Mianabadi, Ameneh; Shirazi, Pooya; Ghahraman, Bijan; Coenders-Gerrits, A. M. J.; Alizadeh, Amin; Davary, Kamran

2018-02-01

In arid and semi-arid regions, water scarcity is the crucial issue for crop production. Identifying the spatial and temporal trends in aridity, especially during the crop-growing season, is important for farmers to manage their agricultural practices. This will become especially relevant when considering climate change projections. To reliably determine the actual trends, the influence of short- and long-term memory should be removed from the trend analysis. The objective of this study is to investigate the effect of short- and long-term memory on estimates of trends in two aridity indicators—the inverted De Martonne (ϕ IDM ) and Budyko (ϕ B ) indices. The analysis is done using precipitation and temperature data over Iran for a 50-year period (1966-2015) at three temporal scales: annual, wheat-growing season (October-June), and maize-growing season (May-November). For this purpose, the original and the modified Mann-Kendall tests (i.e., modified by three methods of trend free pre-whitening (TFPT), effective sample size (ESS), and long-term persistence (LTP)) are used to investigate the temporal trends in aridity indices, precipitation, and temperature by taking into account the effect of short- and long-term memory. Precipitation and temperature data were provided by the Islamic Republic of Iran Meteorological Organization (IRIMO). The temporal trend analysis showed that aridity increased from 1966 to 2015 at the annual and wheat-growing season scales, which is due to a decreasing trend in precipitation and an increasing trend in mean temperature at these two timescales. The trend in aridity indices was decreasing in the maize-growing season, since precipitation has an increasing trend for most parts of Iran in that season. The increasing trend in aridity indices is significant in Western Iran, which can be related to the significantly more negative trend in precipitation in the West. This increasing trend in aridity could result in an increasing crop water

Backcasting long-term climate data: evaluation of hypothesis

Science.gov (United States)

Saghafian, Bahram; Aghbalaghi, Sara Ghasemi; Nasseri, Mohsen

2018-05-01

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies.

Ocean eddies and climate predictability.

Science.gov (United States)

Kirtman, Ben P; Perlin, Natalie; Siqueira, Leo

2017-12-01

A suite of coupled climate model simulations and experiments are used to examine how resolved mesoscale ocean features affect aspects of climate variability, air-sea interactions, and predictability. In combination with control simulations, experiments with the interactive ensemble coupling strategy are used to further amplify the role of the oceanic mesoscale field and the associated air-sea feedbacks and predictability. The basic intent of the interactive ensemble coupling strategy is to reduce the atmospheric noise at the air-sea interface, allowing an assessment of how noise affects the variability, and in this case, it is also used to diagnose predictability from the perspective of signal-to-noise ratios. The climate variability is assessed from the perspective of sea surface temperature (SST) variance ratios, and it is shown that, unsurprisingly, mesoscale variability significantly increases SST variance. Perhaps surprising is the fact that the presence of mesoscale ocean features even further enhances the SST variance in the interactive ensemble simulation beyond what would be expected from simple linear arguments. Changes in the air-sea coupling between simulations are assessed using pointwise convective rainfall-SST and convective rainfall-SST tendency correlations and again emphasize how the oceanic mesoscale alters the local association between convective rainfall and SST. Understanding the possible relationships between the SST-forced signal and the weather noise is critically important in climate predictability. We use the interactive ensemble simulations to diagnose this relationship, and we find that the presence of mesoscale ocean features significantly enhances this link particularly in ocean eddy rich regions. Finally, we use signal-to-noise ratios to show that the ocean mesoscale activity increases model estimated predictability in terms of convective precipitation and atmospheric upper tropospheric circulation.

Short-term forecasting model for aggregated regional hydropower generation

International Nuclear Information System (INIS)

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

2014-01-01

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

Using the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey to Predict the Occurrence of Short-Term Coronary Heart Disease Events in Women.

Science.gov (United States)

McSweeney, Jean C; Cleves, Mario A; Fischer, Ellen P; Pettey, Christina M; Beasley, Brittany

Few instruments capture symptoms that predict cardiac events in the short-term. This study examines the ability of the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey to predict acute cardiac events within 3 months of administration and to identify the prodromal symptoms most associated with short-term risk in women without known coronary heart disease. The McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey was administered to 1,097 women referred to a cardiologist for initial coronary heart disease evaluation. Logistic regression models were used to examine prodromal symptoms individually and in combination to identify the subset of symptoms most predictive of an event within 3 months. Fifty-one women had an early cardiac event. In bivariate analyses, 4 of 30 prodromal symptoms were significantly associated with event occurrence within 90 days. In adjusted analyses, women reporting arm pain or discomfort and unusual fatigue were more likely (OR, 4.67; 95% CI, 2.08-10.48) to have a cardiac event than women reporting neither. The McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey may assist in predicting short-term coronary heart disease events in women without known coronary heart disease. Copyright © 2017 Jacobs Institute of Women's Health. All rights reserved.

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

International Nuclear Information System (INIS)

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

2016-01-01

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

Value of five-stage prognostic system in predicting short-term outcome of patients with liver cirrhosis

Directory of Open Access Journals (Sweden)

TIAN Yan

2015-03-01

Full Text Available ObjectiveTo evaluate the clinical value of five-stage prognostic system in predicting the short-term outcome of patients with liver cirrhosis, and to compare it with the Child-Turcotte-Pugh (CTP and Model of End-Stage Liver Disease (MELD scores. MethodsTwo hundred and one hospitalized patients with liver cirrhosis in the Department of Gastroenterology in the First Affiliated Hospital of Anhui Medical University from January 2011 to January 2014 were enrolled in the study and followed up for at least six months. Patients were classified accorded to the five-stage prognostic system, and the mortality rate in each stage was measured. The receiver operating characteristic (ROC curve and the area under the ROC curve (AUC were used to assess the accuracy of the five-stage prognostic system in predicting the short-term death risk of cirrhotic patients, which was then compared with the CTP and MELD scores. Categorical data were analyzed by chi-square test. Comparison of AUC was made by normal distribution Z test. Spearman′s correlation analysis was used to investigate the correlation of the five-stage prognostic system with the CTP and MELD scores. ResultsThe study used the admission time as the starting point and the death of patients or study termination time as the endpoint. Among the 201 patients, 50 (24.9% died within six months. Based on the five-stage prognostic system, the mortality rates for stages 1 to 5 were 0(0/11, 0（0/18, 4.2%（2/48, 16.3% (7/43, and 50.6%（41/81, respectively. In patients with decompensated cirrhosis (stages 3, 4, and 5, the mortality increased with stage, and the differences in mortality between patients in stages 3 and 4, 3 and 5, and 4 and 5 were all significant (χ2=3.89, 35.33, and 13.96, respectively; P=0.049, 0.000, and 0.049, respectively. The AUC for the five-stage prognostic system, five-stage prognostic system combined with CTP and MELD score, and CTP score were 0820, 0.915, 0.888, and 0

Qualitative similarities in the visual short-term memory of pigeons and people

OpenAIRE

Gibson, Brett; Wasserman, Edward; Luck, Steven J.

2011-01-01

Visual short-term memory plays a key role in guiding behavior, and individual differences in visual short-term memory capacity are strongly predictive of higher cognitive abilities. To provide a broader evolutionary context for understanding this memory system, we directly compared the behavior of pigeons and humans on a change detection task. Although pigeons had a lower storage capacity and a higher lapse rate than humans, both species stored multiple items in short-term memory and conforme...

Qualitative similarities in the visual short-term memory of pigeons and people.

Science.gov (United States)

Gibson, Brett; Wasserman, Edward; Luck, Steven J

2011-10-01

Visual short-term memory plays a key role in guiding behavior, and individual differences in visual short-term memory capacity are strongly predictive of higher cognitive abilities. To provide a broader evolutionary context for understanding this memory system, we directly compared the behavior of pigeons and humans on a change detection task. Although pigeons had a lower storage capacity and a higher lapse rate than humans, both species stored multiple items in short-term memory and conformed to the same basic performance model. Thus, despite their very different evolutionary histories and neural architectures, pigeons and humans have functionally similar visual short-term memory systems, suggesting that the functional properties of visual short-term memory are subject to similar selective pressures across these distant species.

Climate Prediction Center - Monitoring & Data: La Niña Seasonal Maps and

Science.gov (United States)

page National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center ±a Case Selection Contact Richard Tinker [rtinker@ncep.noaa.gov], Climate Prediction Center significant climate signals: The La Niña episode, and long-term trends in average temperature and total

On the short-term predictability of fully digital chaotic oscillators for pseudo-random number generation

KAUST Repository

Radwan, Ahmed Gomaa

2014-06-18

This paper presents a digital implementation of a 3rd order chaotic system using the Euler approximation. Short-term predictability is studied in relation to system precision, Euler step size and attractor size and optimal parameters for maximum performance are derived. Defective bits from the native chaotic output are neglected and the remaining pass the NIST SP. 800-22 tests without post-processing. The resulting optimized pseudorandom number generator has throughput up to 17.60 Gbits/s for a 64-bit design experimentally verified on a Xilinx Virtex 4 FPGA with logic utilization less than 1.85%.

On the short-term predictability of fully digital chaotic oscillators for pseudo-random number generation

KAUST Repository

Radwan, Ahmed Gomaa; Mansingka, Abhinav S.; Salama, Khaled N.; Zidan, Mohammed A.

2014-01-01

This paper presents a digital implementation of a 3rd order chaotic system using the Euler approximation. Short-term predictability is studied in relation to system precision, Euler step size and attractor size and optimal parameters for maximum performance are derived. Defective bits from the native chaotic output are neglected and the remaining pass the NIST SP. 800-22 tests without post-processing. The resulting optimized pseudorandom number generator has throughput up to 17.60 Gbits/s for a 64-bit design experimentally verified on a Xilinx Virtex 4 FPGA with logic utilization less than 1.85%.

The PER (Preoperative Esophagectomy Risk) Score: A Simple Risk Score to Predict Short-Term and Long-Term Outcome in Patients with Surgically Treated Esophageal Cancer.

Science.gov (United States)

Reeh, Matthias; Metze, Johannes; Uzunoglu, Faik G; Nentwich, Michael; Ghadban, Tarik; Wellner, Ullrich; Bockhorn, Maximilian; Kluge, Stefan; Izbicki, Jakob R; Vashist, Yogesh K

2016-02-01

Esophageal resection in patients with esophageal cancer (EC) is still associated with high mortality and morbidity rates. We aimed to develop a simple preoperative risk score for the prediction of short-term and long-term outcomes for patients with EC treated by esophageal resection. In total, 498 patients suffering from esophageal carcinoma, who underwent esophageal resection, were included in this retrospective cohort study. Three preoperative esophagectomy risk (PER) groups were defined based on preoperative functional evaluation of different organ systems by validated tools (revised cardiac risk index, model for end-stage liver disease score, and pulmonary function test). Clinicopathological parameters, morbidity, and mortality as well as disease-free survival (DFS) and overall survival (OS) were correlated to the PER score. The PER score significantly predicted the short-term outcome of patients with EC who underwent esophageal resection. PER 2 and PER 3 patients had at least double the risk of morbidity and mortality compared to PER 1 patients. Furthermore, a higher PER score was associated with shorter DFS (P PER score was identified as an independent predictor of tumor recurrence (hazard ratio [HR] 2.1; P PER score allows preoperative objective allocation of patients with EC into different risk categories for morbidity, mortality, and long-term outcomes. Thus, multicenter studies are needed for independent validation of the PER score.

The importance of short-term off-target effects in estimating the long-term renal and cardiovascular protection of angiotensin receptor blockers

DEFF Research Database (Denmark)

Smink, P A; Miao, Y; Eijkemans, M J C

2014-01-01

Angiotensin receptor blockers (ARBs) have multiple effects that may contribute to their efficacy on renal/cardiovascular outcomes. We developed and validated a risk score that incorporated short-term changes in multiple risk markers to predict the ARB effect on renal/cardiovascular outcomes.......98), in addition to being markedly more accurate than predicted RRRs based on changes in single markers. The score was validated in an independent ARB trial. Predictions of long-term renal/cardiovascular ARB effects are more accurate when considering short-term changes in multiple risk markers, challenging the use...

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

International Nuclear Information System (INIS)

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

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Maura Murru

2010-11-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2012-07-01

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

Brain oscillatory substrates of visual short-term memory capacity.

Science.gov (United States)

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

2009-11-17

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

Climate Prediction Center - Outlooks

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Web resources and services. HOME > Outreach > Publications > Climate Diagnostics Bulletin Climate Diagnostics Bulletin - Tropics Climate Diagnostics Bulletin - Forecast Climate Diagnostics

Short-term wind power prediction

DEFF Research Database (Denmark)

Joensen, Alfred K.

2003-01-01

, and to implement these models and methods in an on-line software application. The economical value of having predictions available is also briefly considered. The summary report outlines the background and motivation for developing wind power prediction models. The meteorological theory which is relevant......The present thesis consists of 10 research papers published during the period 1997-2002 together with a summary report. The objective of the work described in the thesis is to develop models and methods for calculation of high accuracy predictions of wind power generated electricity...

Sweden's Future Climate in the short- and medium-term perspective. Basis for development of climate adaptation tools; Sveriges framtida klimat paa kort och medellaang sikt. Underlag foer utveckling av verktyg foer klimatanpassning

Energy Technology Data Exchange (ETDEWEB)

Carlsen, Henrik; Parmhed, Oskar

2008-12-15

This report studies two questions - how the actual amount of greenhouse gas emissions has changed over time in comparison with previous assumptions and what will happen to Sweden's climate from short and medium term perspectives given the future development of the climate in general. The content of this report shall be used as the basis for continued work in the development of climate adaptation tools that can be used primarily in Sweden. The first section of this report presents the development of actual greenhouse gas emissions in recent years. Eight years have passed since IPCC published its emissions scenarios (SRES) and for this reason a comparison between the emissions levels from the report and the experimental data from observations made in recent years is well due. Measurements show that current levels of emissions of carbon dioxide from fossil fuels exceed the average of the SRES families as a whole. Among other implications, this means that emission levels are significantly higher than the levels assumed in the climate scenarios of the Swedish Commission on Climate and Vulnerability (Klimat- och saarbarhetsutredningen). There is little support from the experimental data to indicate that this rate of increase will subside. In addition, studies are indicating that the increased emissions are not solely attributable to a rise in population levels and increased wealth (GNP/capita). Another aspect is that the carbon intensity, measured as the amount of carbon dioxide per unit production price, has been declining since 2000. This is entirely contrary to the assumptions made in all of the emissions scenarios in SRES that carbon dioxide efficiency would continue to increase. The report therefore concludes that work with climate adaptation in Sweden must take into consideration to a greater extent than is currently done today the consequences of emissions levels that are in line with or exceed the highest SRES levels. The second section of this report takes

Weather uncertainty versus climate change uncertainty in a short television weather broadcast

Science.gov (United States)

Witte, J.; Ward, B.; Maibach, E.

2011-12-01

For TV meteorologists talking about uncertainty in a two-minute forecast can be a real challenge. It can quickly open the way to viewer confusion. TV meteorologists understand the uncertainties of short term weather models and have different methods to convey the degrees of confidence to the viewing public. Visual examples are seen in the 7-day forecasts and the hurricane track forecasts. But does the public really understand a 60 percent chance of rain or the hurricane cone? Communication of climate model uncertainty is even more daunting. The viewing public can quickly switch to denial of solid science. A short review of the latest national survey of TV meteorologists by George Mason University and lessons learned from a series of climate change workshops with TV broadcasters provide valuable insights into effectively using visualizations and invoking multimedia-learning theories in weather forecasts to improve public understanding of climate change.

Individual stress vulnerability is predicted by short-term memory and AMPA receptor subunit ratio in the hippocampus.

Science.gov (United States)

Schmidt, Mathias V; Trümbach, Dietrich; Weber, Peter; Wagner, Klaus; Scharf, Sebastian H; Liebl, Claudia; Datson, Nicole; Namendorf, Christian; Gerlach, Tamara; Kühne, Claudia; Uhr, Manfred; Deussing, Jan M; Wurst, Wolfgang; Binder, Elisabeth B; Holsboer, Florian; Müller, Marianne B

2010-12-15

Increased vulnerability to aversive experiences is one of the main risk factors for stress-related psychiatric disorders as major depression. However, the molecular bases of vulnerability, on the one hand, and stress resilience, on the other hand, are still not understood. Increasing clinical and preclinical evidence suggests a central involvement of the glutamatergic system in the pathogenesis of major depression. Using a mouse paradigm, modeling increased stress vulnerability and depression-like symptoms in a genetically diverse outbred strain, and we tested the hypothesis that differences in AMPA receptor function may be linked to individual variations in stress vulnerability. Vulnerable and resilient animals differed significantly in their dorsal hippocampal AMPA receptor expression and AMPA receptor binding. Treatment with an AMPA receptor potentiator during the stress exposure prevented the lasting effects of chronic social stress exposure on physiological, neuroendocrine, and behavioral parameters. In addition, spatial short-term memory, an AMPA receptor-dependent behavior, was found to be predictive of individual stress vulnerability and response to AMPA potentiator treatment. Finally, we provide evidence that genetic variations in the AMPA receptor subunit GluR1 are linked to the vulnerable phenotype. Therefore, we propose genetic variations in the AMPA receptor system to shape individual stress vulnerability. Those individual differences can be predicted by the assessment of short-term memory, thereby opening up the possibility for a specific treatment by enhancing AMPA receptor function.

Limited influence of climate change mitigation on short-term glacier mass loss

Science.gov (United States)

Marzeion, Ben; Kaser, Georg; Maussion, Fabien; Champollion, Nicolas

2018-04-01

Glacier mass loss is a key contributor to sea-level change1,2, slope instability in high-mountain regions3,4 and the changing seasonality and volume of river flow5-7. Understanding the causes, mechanisms and time scales of glacier change is therefore paramount to identifying successful strategies for mitigation and adaptation. Here, we use temperature and precipitation fields from the Coupled Model Intercomparison Project Phase 5 output to force a glacier evolution model, quantifying mass responses to future climatic change. We find that contemporary glacier mass is in disequilibrium with the current climate, and 36 ± 8% mass loss is already committed in response to past greenhouse gas emissions. Consequently, mitigating future emissions will have only very limited influence on glacier mass change in the twenty-first century. No significant differences between 1.5 and 2 K warming scenarios are detectable in the sea-level contribution of glaciers accumulated within the twenty-first century. In the long-term, however, mitigation will exert strong control, suggesting that ambitious measures are necessary for the long-term preservation of glaciers.

Climate Prediction Center - Seasonal Outlook

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Forecast Discussion PROGNOSTIC DISCUSSION FOR MONTHLY OUTLOOK NWS CLIMATE PREDICTION CENTER COLLEGE PARK MD INFLUENCE ON THE MONTHLY-AVERAGED CLIMATE. OUR MID-MONTH ASSESSMENT OF LOW-FREQUENCY CLIMATE VARIABILITY IS

V4 activity predicts the strength of visual short-term memory representations

NARCIS (Netherlands)

Sligte, I.G.; Scholte, H.S.; Lamme, V.A.F.

2009-01-01

Recent studies have shown the existence of a form of visual memory that lies intermediate of iconic memory and visual short-term memory (VSTM), in terms of both capacity (up to 15 items) and the duration of the memory trace (up to 4 s). Because new visual objects readily overwrite this intermediate

Do climate model predictions agree with long-term precipitation trends in the arid southwestern United States?

Science.gov (United States)

Elias, E.; Rango, A.; James, D.; Maxwell, C.; Anderson, J.; Abatzoglou, J. T.

2016-12-01

Researchers evaluating climate projections across southwestern North America observed a decreasing precipitation trend. Aridification was most pronounced in the cold (non-monsoonal) season, whereas downward trends in precipitation were smaller in the warm (monsoonal) season. In this region, based upon a multimodel mean of 20 Coupled Model Intercomparison Project 5 models using a business-as-usual (Representative Concentration Pathway 8.5) trajectory, midcentury precipitation is projected to increase slightly during the monsoonal time period (July-September; 6%) and decrease slightly during the remainder of the year (October-June; -4%). We use observed long-term (1915-2015) monthly precipitation records from 16 weather stations to investigate how well measured trends corroborate climate model predictions during the monsoonal and non-monsoonal timeframe. Running trend analysis using the Mann-Kendall test for 15 to 101 year moving windows reveals that half the stations showed significant (p≤0.1), albeit small, increasing trends based on the longest term record. Trends based on shorter-term records reveal a period of significant precipitation decline at all stations representing the 1950s drought. Trends from 1930 to 2015 reveal significant annual, monsoonal and non-monsoonal increases in precipitation (Fig 1). The 1960 to 2015 time window shows no significant precipitation trends. The more recent time window (1980 to 2015) shows a slight, but not significant, increase in monsoonal precipitation and a larger, significant decline in non-monsoonal precipitation. GCM precipitation projections are consistent with more recent trends for the region. Running trends from the most recent time window (mid-1990s to 2015) at all stations show increasing monsoonal precipitation and decreasing Oct-Jun precipitation, with significant trends at 6 of 16 stations. Running trend analysis revealed that the long-term trends were not persistent throughout the series length, but depended

Utilization of Short-Simulations for Tuning High-Resolution Climate Model

Science.gov (United States)

Lin, W.; Xie, S.; Ma, P. L.; Rasch, P. J.; Qian, Y.; Wan, H.; Ma, H. Y.; Klein, S. A.

2016-12-01

Many physical parameterizations in atmospheric models are sensitive to resolution. Tuning the models that involve a multitude of parameters at high resolution is computationally expensive, particularly when relying primarily on multi-year simulations. This work describes a complementary set of strategies for tuning high-resolution atmospheric models, using ensembles of short simulations to reduce the computational cost and elapsed time. Specifically, we utilize the hindcast approach developed through the DOE Cloud Associated Parameterization Testbed (CAPT) project for high-resolution model tuning, which is guided by a combination of short (tests have been found to be effective in numerous previous studies in identifying model biases due to parameterized fast physics, and we demonstrate that it is also useful for tuning. After the most egregious errors are addressed through an initial "rough" tuning phase, longer simulations are performed to "hone in" on model features that evolve over longer timescales. We explore these strategies to tune the DOE ACME (Accelerated Climate Modeling for Energy) model. For the ACME model at 0.25° resolution, it is confirmed that, given the same parameters, major biases in global mean statistics and many spatial features are consistent between Atmospheric Model Intercomparison Project (AMIP)-type simulations and CAPT-type hindcasts, with just a small number of short-term simulations for the latter over the corresponding season. The use of CAPT hindcasts to find parameter choice for the reduction of large model biases dramatically improves the turnaround time for the tuning at high resolution. Improvement seen in CAPT hindcasts generally translates to improved AMIP-type simulations. An iterative CAPT-AMIP tuning approach is therefore adopted during each major tuning cycle, with the former to survey the likely responses and narrow the parameter space, and the latter to verify the results in climate context along with assessment in

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

Science.gov (United States)

Jarrold, C; Baddeley, A D

2001-10-01

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

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

Science.gov (United States)

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

2017-04-01

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

Flirting with disaster: short-term mating orientation and hostile sexism predict different types of sexual harassment.

Science.gov (United States)

Diehl, Charlotte; Rees, Jonas; Bohner, Gerd

2012-01-01

We combine evolutionary and sociocultural accounts of sexual harassment, proposing that sexuality-related and hostility-related motives lead to different types of harassment. Specifically, men's short-term mating orientation (STMO) was hypothesized to predict only unwanted sexual attention but not gender harassment, whereas men's hostile sexism (HS) was hypothesized to predict both unwanted sexual attention and gender harassment. As part of an alleged computer-chat task, 100 male students could send sexualized personal remarks (representing unwanted sexual attention), sexist jokes (representing gender harassment), or nonharassing material to an attractive female target. Independently, participants' STMO, HS, and sexual harassment myth acceptance (SHMA) were assessed. Correlational and path analyses revealed that STMO specifically predicted unwanted sexual attention, whereas HS predicted both unwanted sexual attention and gender harassment. Furthermore, SHMA fully mediated the effect of HS on gender harassment, but did not mediate effects of STMO or HS on unwanted sexual attention. Results are discussed in relation to motivational explanations for sexual harassment and antiharassment interventions. © 2012 Wiley Periodicals, Inc.

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

International Nuclear Information System (INIS)

Lihn, M.L.

1989-03-01

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

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

Science.gov (United States)

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

2018-05-17

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

Climate Prediction Center - The ENSO Cycle

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Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Web resources and services. HOME > El Niño/La Niña > The ENSO Cycle ENSO Cycle Banner Climate for Weather and Climate Prediction Climate Prediction Center 5830 University Research Court College

V4 activity predicts the strength of visual short-term memory representations.

Science.gov (United States)

Sligte, Ilja G; Scholte, H Steven; Lamme, Victor A F

2009-06-10

Recent studies have shown the existence of a form of visual memory that lies intermediate of iconic memory and visual short-term memory (VSTM), in terms of both capacity (up to 15 items) and the duration of the memory trace (up to 4 s). Because new visual objects readily overwrite this intermediate visual store, we believe that it reflects a weak form of VSTM with high capacity that exists alongside a strong but capacity-limited form of VSTM. In the present study, we isolated brain activity related to weak and strong VSTM representations using functional magnetic resonance imaging. We found that activity in visual cortical area V4 predicted the strength of VSTM representations; activity was low when there was no VSTM, medium when there was a weak VSTM representation regardless of whether this weak representation was available for report or not, and high when there was a strong VSTM representation. Altogether, this study suggests that the high capacity yet weak VSTM store is represented in visual parts of the brain. Allegedly, only some of these VSTM traces are amplified by parietal and frontal regions and as a consequence reside in traditional or strong VSTM. The additional weak VSTM representations remain available for conscious access and report when attention is redirected to them yet are overwritten as soon as new visual stimuli hit the eyes.

Association between Early Attention-Deficit/Hyperactivity Symptoms and Current Verbal and Visuo-Spatial Short-Term Memory

Science.gov (United States)

Gau, Susan Shur-Fen; Chiang, Huey-Ling

2013-01-01

Deficits in short-term memory are common in adolescents with attention-deficit/hyperactivity disorder (ADHD), but their current ADHD symptoms cannot well predict their short-term performance. Taking a developmental perspective, we wanted to clarify the association between ADHD symptoms at early childhood and short-term memory in late childhood and…

Heartbeat of the Southern Oscillation explains ENSO climatic resonances

Science.gov (United States)

Bruun, John T.; Allen, J. Icarus; Smyth, Timothy J.

2017-08-01

The El Niño-Southern Oscillation (ENSO) nonlinear oscillator phenomenon has a far reaching influence on the climate and human activities. The up to 10 year quasi-period cycle of the El Niño and subsequent La Niña is known to be dominated in the tropics by nonlinear physical interaction of wind with the equatorial waveguide in the Pacific. Long-term cyclic phenomena do not feature in the current theory of the ENSO process. We update the theory by assessing low (>10 years) and high (features. The observational data sets of the Southern Oscillation Index (SOI), North Pacific Index Anomaly, and ENSO Sea Surface Temperature Anomaly, as well as a theoretical model all confirm the existence of long-term and short-term climatic cycles of the ENSO process with resonance frequencies of {2.5, 3.8, 5, 12-14, 61-75, 180} years. This fundamental result shows long-term and short-term signal coupling with mode locking across the dominant ENSO dynamics. These dominant oscillation frequency dynamics, defined as ENSO frequency states, contain a stable attractor with three frequencies in resonance allowing us to coin the term Heartbeat of the Southern Oscillation due to its characteristic shape. We predict future ENSO states based on a stable hysteresis scenario of short-term and long-term ENSO oscillations over the next century.Plain Language SummaryThe Pacific El Niño-Southern Oscillation (ENSO) nonlinear oscillator phenomenon has a far reaching influence on the climate and our human activities. This work can help predict both long-term and short-term future ENSO events and to assess the risk of future climate hysteresis changes: is the elastic band that regulates the ENSO climate breaking? We update the current theory of the ENSO process with a sophisticated analysis approach (Dominant Frequency State Analysis) to include long-term oscillations (up to 200 years) as well as tropical and extratropical interaction dynamics. The analysis uses instrumental and paleoproxy data

Error analysis of short term wind power prediction models

International Nuclear Information System (INIS)

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

2011-01-01

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

Error analysis of short term wind power prediction models

Energy Technology Data Exchange (ETDEWEB)

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

2011-04-15

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

Locked into Copenhagen pledges - Implications of short-term emission targets for the cost and feasibility of long-term climate goals

NARCIS (Netherlands)

Riahi, Keywan; Kriegler, Elmar; Johnson, Nils; Bertram, Christoph; den Elzen, Michel; Eom, Jiyong; Schaeffer, Michiel; Edmonds, Jae; Isaac, Morna; Krey, Volker; Longden, Thomas; Luderer, Gunnar; Méjean, Aurélie; McCollum, David L.; Mima, Silvana; Turton, Hal; van Vuuren, Detlef P.; Wada, Kenichi; Bosetti, Valentina; Capros, Pantelis; Criqui, Patrick; Hamdi-Cherif, Meriem; Kainuma, Mikiko; Edenhofer, Ottmar

2015-01-01

This paper provides an overview of the AMPERE modeling comparison project with focus on the implications of near-term policies for the costs and attainability of long-term climate objectives. Nine modeling teams participated in the project to explore the consequences of global emissions following

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

Science.gov (United States)

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

2017-02-01

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

The suitability of short-term measurements of radon in the built environment

International Nuclear Information System (INIS)

Denman, A.R.; Groves-Kirkby, C.J.; Phillips, P.S.; Crockett, R.G.M.; Woolridge, A.C.

2008-01-01

Although domestic and workplace radon concentration levels often show marked diurnal/short-term variation, overall health risk is determined by the long-term average level, and many national protocols advocate the use of long exposure periods, usually three months, to assess long-term risk. Simple passive measurement techniques, e.g. track-etch, activated charcoal and electret, can, however, provide reasonably accurate determinations with exposures as short as one week, and there is pressure from users and stake holders for assessments within this time period. We report evaluation of the effectiveness of one-week, one-month and three-month exposures over a period of one year in a designated Radon Affected Area in the United Kingdom (UK). Although short-term exposures did not compromise measurement accuracy, short-term radon variability rendered one-week measurements less reliable in predicting annual average radon levels via the conventional methodology. Analysis permitted estimation of the maximum and minimum short-term measured domestic radon concentrations at which there was 95% probability of the predicted annual average being below or above the UK Action Level of 200 Bq·m -3 respectively. Between these limits, the short-term result is equivocal, requiring repetition, and the 'equivocal range' for one-week measurements is significantly wider than for three-month exposures. In any geographical area, domestic radon concentrations are distributed log normally, with many properties having low average levels; a small number exhibit excessive levels, and this distribution must be considered when defining exposures for a radon measurement programme. In low-radon areas, where 1% of houses might exceed the Action Level, a one-week assessment will find that fewer outcomes are equivocal. For high-radon areas, with 20% or more houses over the Action Level, more than 50% of one-week outcomes will be equivocal, requiring repeats. The results of this work will be presented

Ain't no mountain high enough? Setting high weight loss goals predict effort and short-term weight loss.

Science.gov (United States)

De Vet, Emely; Nelissen, Rob M A; Zeelenberg, Marcel; De Ridder, Denise T D

2013-05-01

Although psychological theories outline that it might be beneficial to set more challenging goals, people attempting to lose weight are generally recommended to set modest weight loss goals. The present study explores whether the amount of weight loss individuals strive for is associated with more positive psychological and behavioral outcomes. Hereto, 447 overweight and obese participants trying to lose weight completed two questionnaires with a 2-month interval. Many participants set goals that could be considered unrealistically high. However, higher weight loss goals did not predict dissatisfaction but predicted more effort in the weight loss attempt, as well as more self-reported short-term weight loss when baseline commitment and motivation were controlled for.

Quality of rearing practices as predictor of short-term outcome in adolescent anorexia nervosa.

Science.gov (United States)

Castro, J; Toro, J; Cruz, M

2000-01-01

Studies of family relationships in anorexia nervosa have produced conflicting results. Some authors claim that family factors are related to short-term outcomes. Perceived rearing practices, as measured by the EMBU (Egna Minnen Betraffande Uppfostran: 'My memories of Upbringing') were examined in a sample (N = 158) of adolescents with anorexia nervosa and compared with the perceptions of adolescents (N = 159) from the general population. A further comparison was made between the groups of patients with good and bad short-term outcomes. Logistic regression analysis was performed to evaluate the predictive value of different variables on short-term outcome. Overall, small differences were observed in the perceptions of rearing practices as expressed by the controls and the anorexic patients. Patients with bad short-term outcome perceived more rejection and control-overprotection from both parents than those with good outcome. In the logistic regression analysis only Rejection from father and the EAT (Eating Attitudes Test) total score gave independent prediction of treatment response. Taken as a whole, these results do not support the idea of altered rearing practices in anorexic patients, at least in young patients with a short evolution of the disease. Perceived rearing practices, especially 'rejection', appear to have an appreciable effect on the short-term outcome.

Characterization of the Dynamics of Climate Systems and Identification of Missing Mechanisms Impacting the Long Term Predictive Capabilities of Global Climate Models Utilizing Dynamical Systems Approaches to the Analysis of Observed and Modeled Climate

Energy Technology Data Exchange (ETDEWEB)

Bhatt, Uma S. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Atmospheric Sciences; Wackerbauer, Renate [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Physics; Polyakov, Igor V. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Atmospheric Sciences; Newman, David E. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Physics; Sanchez, Raul E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Fusion Energy Division; Univ. Carlos III de Madrid (Spain)

2015-11-13

The goal of this research was to apply fractional and non-linear analysis techniques in order to develop a more complete characterization of climate change and variability for the oceanic, sea ice and atmospheric components of the Earth System. This research applied two measures of dynamical characteristics of time series, the R/S method of calculating the Hurst exponent and Renyi entropy, to observational and modeled climate data in order to evaluate how well climate models capture the long-term dynamics evident in observations. Fractional diffusion analysis was applied to ARGO ocean buoy data to quantify ocean transport. Self organized maps were applied to North Pacific sea level pressure and analyzed in ways to improve seasonal predictability for Alaska fire weather. This body of research shows that these methods can be used to evaluate climate models and shed light on climate mechanisms (i.e., understanding why something happens). With further research, these methods show promise for improving seasonal to longer time scale forecasts of climate.

Climate Neutral Campus Key Terms and Definitions | Climate Neutral Research

Science.gov (United States)

Campuses | NREL Neutral Campus Key Terms and Definitions Climate Neutral Campus Key Terms and Definitions The term climate neutral evolved along with net zero and a number of other "green" and accuracy in these areas lets research campuses know exactly how close they are to climate

Auditory-Cortex Short-Term Plasticity Induced by Selective Attention

Science.gov (United States)

Jääskeläinen, Iiro P.; Ahveninen, Jyrki

2014-01-01

The ability to concentrate on relevant sounds in the acoustic environment is crucial for everyday function and communication. Converging lines of evidence suggests that transient functional changes in auditory-cortex neurons, “short-term plasticity”, might explain this fundamental function. Under conditions of strongly focused attention, enhanced processing of attended sounds can take place at very early latencies (~50 ms from sound onset) in primary auditory cortex and possibly even at earlier latencies in subcortical structures. More robust selective-attention short-term plasticity is manifested as modulation of responses peaking at ~100 ms from sound onset in functionally specialized nonprimary auditory-cortical areas by way of stimulus-specific reshaping of neuronal receptive fields that supports filtering of selectively attended sound features from task-irrelevant ones. Such effects have been shown to take effect in ~seconds following shifting of attentional focus. There are findings suggesting that the reshaping of neuronal receptive fields is even stronger at longer auditory-cortex response latencies (~300 ms from sound onset). These longer-latency short-term plasticity effects seem to build up more gradually, within tens of seconds after shifting the focus of attention. Importantly, some of the auditory-cortical short-term plasticity effects observed during selective attention predict enhancements in behaviorally measured sound discrimination performance. PMID:24551458

Decadal climate prediction (project GCEP).

Science.gov (United States)

Haines, Keith; Hermanson, Leon; Liu, Chunlei; Putt, Debbie; Sutton, Rowan; Iwi, Alan; Smith, Doug

2009-03-13

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

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

Directory of Open Access Journals (Sweden)

Jaime Lloret

2013-08-01

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

The CCPP-ARM Parameterization Testbed (CAPT): Where Climate Simulation Meets Weather Prediction

Energy Technology Data Exchange (ETDEWEB)

Phillips, T J; Potter, G L; Williamson, D L; Cederwall, R T; Boyle, J S; Fiorino, M; Hnilo, J J; Olson, J G; Xie, S; Yio, J J

2003-11-21

To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands, in particular, that the GCM parameterizations of unresolved processes should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provied that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by realistically initialized climate GCM, and the application of six-hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be similarly tested. In order to further this method for evaluating and analyzing parameterizations in climate GCMs, the USDOE is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM. Numerical weather prediction methods show promise for improving parameterizations in climate GCMs.

Long-term climate monitoring by the global climate observing system

International Nuclear Information System (INIS)

Karl, T.R.

1995-12-01

Is the climate warming? Is the hydrologic cycle changing? Is the atmospheric/oceanic circulation changing? Is the climate becoming more variable or extreme? Is radiative forcing of the climate changing? are complex questions not only from the standpoint of a multi-variate problem, but because of the various aspects of spatial and temporal sampling that must be considered on a global scale. The development of a Global Climate Observing System (GCOS) offers the opportunity for scientists to do something about existing observing deficiencies in light of the importance of documenting long-term climate changes that may already be affected by anthropogenic changes of atmospheric composition and land use as well as other naturally occurring changes. As an important step toward improving the present inadequacies, a workshop was held to help define the long-term monitoring requirements minimally needed to address the five questions posed above, with special emphasis on detecting anthropogenic climate change and its potential impact on managed and unmanaged systems The workshop focussed on three broad areas related to long-term climate monitoring: (a) the scientific rationale for the long-term climate products (including their accuracy, resolution, and homogeneity) required from our observing systems as related to climate monitoring and climate change detection and attribution; (b) the status of long-term climate products and the observing systems from which these data are derived; and (c) implementation strategies necessary to fulfill item (a) in light of existing systems. Item (c) was treated more in terms of feasibility rather than as a specific implementation plan. figs., tabs., refs

Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

Science.gov (United States)

Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

2017-12-01

The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

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

International Nuclear Information System (INIS)

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

2002-01-01

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

Climate Prediction Center - Site Index

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Means Bulletins Annual Winter Stratospheric Ozone Climate Diagnostics Bulletin (Most Recent) Climate (Hazards Outlook) Climate Assessment: Dec. 1999-Feb. 2000 (Seasonal) Climate Assessment: Mar-May 2000

The patient safety climate in healthcare organizations (PSCHO) survey: Short-form development.

Science.gov (United States)

Benzer, Justin K; Meterko, Mark; Singer, Sara J

2017-08-01

Measures of safety climate are increasingly used to guide safety improvement initiatives. However, cost and respondent burden may limit the use of safety climate surveys. The purpose of this study was to develop a 15- to 20-item safety climate survey based on the Patient Safety Climate in Healthcare Organizations survey, a well-validated 38-item measure of safety climate. The Patient Safety Climate in Healthcare Organizations was administered to all senior managers, all physicians, and a 10% random sample of all other hospital personnel in 69 private sector hospitals and 30 Veterans Health Administration hospitals. Both samples were randomly divided into a derivation sample to identify a short-form subset and a confirmation sample to assess the psychometric properties of the proposed short form. The short form consists of 15 items represented 3 overarching domains in the long-form scale-organization, work unit, and interpersonal. The proposed short form efficiently captures 3 important sources of variance in safety climate: organizational, work-unit, and interpersonal. The short-form development process was a practical method that can be applied to other safety climate surveys. This safety climate short form may increase response rates in studies that involve busy clinicians or repeated measures. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions.

Science.gov (United States)

Fox, Naomi J; Marion, Glenn; Davidson, Ross S; White, Piran C L; Hutchings, Michael R

2012-03-06

Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed.

Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions

Directory of Open Access Journals (Sweden)

Ross S. Davidson

2012-03-01

Full Text Available Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed.

Predicting Employment Outcomes for Consumers in Community College Short-Term Training Programs

Science.gov (United States)

Flannery, K. Brigid; Benz, Michael R.; Yovanoff, Paul; Kato, Mary McGrath; Lindstrom, Lauren

2011-01-01

Postsecondary education has been linked to improved access to employment opportunities for individuals with and without disabilities. The purpose of this study was to determine factors associated with increased employment outcomes for Vocational Rehabilitation consumers enrolled in community college short term occupational skill training programs.…

Self-concept and quality of object relations as predictors of outcome in short- and long-term psychotherapy.

Science.gov (United States)

Lindfors, Olavi; Knekt, Paul; Heinonen, Erkki; Virtala, Esa

2014-01-01

Quality of object relations and self-concept reflect clinically relevant aspects of personality functioning, but their prediction as suitability factors for psychotherapies of different lengths has not been compared. This study compared their prediction on psychiatric symptoms and work ability in short- and long-term psychotherapy. Altogether 326 patients, 20-46 years of age, with mood and/or anxiety disorder, were randomized to short-term (solution-focused or short-term psychodynamic) psychotherapy and long-term psychodynamic psychotherapy. The Quality of Object Relations Scale (QORS) and the Structural Analysis of Social Behavior (SASB) self-concept questionnaire were measured at baseline, and their prediction on outcome during the 3-year follow-up was assessed by the Symptom Check List Global Severity Index and the Anxiety Scale, the Beck Depression Inventory and by the Work Ability Index, Social Adjustment Scale work subscale and the Perceived Psychological Functioning scale. Negative self-concept strongly and self-controlling characteristics modestly predicted better 3-year outcomes in long-term therapy, after faster early gains in short-term therapy. Patients with a more positive or self-emancipating self-concept, or more mature object relations, experienced more extensive benefits after long-term psychotherapy. The importance of length vs. long-term therapy technique on the differences found is not known. Patients with mild to moderate personality pathology, indicated by poor self-concept, seem to benefit more from long-term than short-term psychotherapy, in reducing risk of depression. Long-term therapy may also be indicated for patients with relatively good psychological functioning. More research is needed on the relative importance of these characteristics in comparison with other patient-related factors. © 2013 Published by Elsevier B.V.

Development of a Climate Prediction Market

Science.gov (United States)

Roulston, M. S.

2017-12-01

Winton, a global investment firm, is planning to establish a prediction market for climate. This prediction market will allow participants to place bets on global climate up to several decades in the future. Winton is pursuing this endeavour as part of its philanthropy that funds scientific research and the communication of scientific ideas. The Winton Climate Prediction Market will be based in the U.K. It will be structured as an online gambling site subject to the regulation of the Gambling Commission. Unlike existing betting sites, the Climate Prediction Market will be subsidized: a central market maker will inject money into the market. This is in contrast to traditional bookmakers or betting exchanges who set odds in their favour or charge commissions to make a profit. The philosophy of a subsidized prediction market is that the party seeking information should fund the market, rather than the participants who provide the information. The initial market will allow bets to be placed on the atmospheric concentration of carbon dioxide and the global mean temperature anomaly. It will thus produce implied forecasts of carbon dioxide concentration as well as global temperatures. If the initial market is successful, additional markets could be added which target other climate variables, such as regional temperatures or sea-level rise. These markets could be sponsored by organizations that are interested in predictions of the specific climate variables. An online platform for the Climate Prediction Market has been developed and has been tested internally at Winton.

The effect of permafrost thaw on short- and long-term carbon accumulation in permafrost mires

Science.gov (United States)

Olid, Carolina; Klaminder, Jonatan; Monteux, Sylvain; Johansson, Margareta; Dorrepaal, Ellen

2017-04-01

Permafrost stores twice as much carbon (C) as is currently present in the atmosphere. During recent years, warmer temperatures in the Arctic has caused rapid thawing of permafrost, which have dramatically altered permafrost C storage by increasing both microbial decomposition and plant productivity. Although current research focuses on the effects of climate change on these two processes, there are still no scientific consensus about the magnitude or even the direction of future C feedbacks from permafrost ecosystems. Field manipulation experiments have been widely used during the last decade to improve our knowledge about the net effects of permafrost thaw in the permafrost C storage. However, due to the slow response (decades) of permafrost ecosystems to environmental changes and the short-time nature of these experiments (usually shorter than 5-9 years), there are still concerns when attempting to extrapolate the results to predict long term effects. In addition, measurements are mostly taken exclusively during the summer season, without taking into account inter-annual variability in C fluxes and underestimating microbial activity throughout the cold season. The need to develop a comprehensive understanding of C fluxes over the entire year and at long temporal scales sets the basis of this study. This study aims to quantify the effects of permafrost thawing in permafrost C fluxes using a 12 years permafrost thaw experiment in northern Sweden. Our aims were to quantify the effect of permafrost thaw in both decomposition and primary production in active layer and newly thawed permafrost, and its implications for the C balance. Based on previous observations, we hypothesized that 1) soil decomposition rates were higher in manipulated thaw plots. However, 2) the observed increase in nutrients availability and the higher presence of vascular plants after thawing stimulate primary production, which compensates to some extent the increased C losses by respiration. To

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

Science.gov (United States)

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

2014-05-01

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

Toward seamless weather-climate and environmental prediction

Science.gov (United States)

Brunet, Gilbert

2016-04-01

Over the last decade or so, predicting the weather, climate and atmospheric composition has emerged as one of the most important areas of scientific endeavor. This is partly because the remarkable increase in skill of current weather forecasts has made society more and more dependent on them day to day for a whole range of decision making. And it is partly because climate change is now widely accepted and the realization is growing rapidly that it will affect every person in the world profoundly, either directly or indirectly. One of the important endeavors of our societies is to remain at the cutting-edge of modelling and predicting the evolution of the fully coupled environmental system: atmosphere (weather and composition), oceans, land surface (physical and biological), and cryosphere. This effort will provide an increasingly accurate and reliable service across all the socio-economic sectors that are vulnerable to the effects of adverse weather and climatic conditions, whether now or in the future. This emerging challenge was at the center of the World Weather Open Science Conference (Montreal, 2014).The outcomes of the conference are described in the World Meteorological Organization (WMO) book: Seamless Prediction of the Earth System: from Minutes to Months, (G. Brunet, S. Jones, P. Ruti Eds., WMO-No. 1156, 2015). It is freely available on line at the WMO website. We will discuss some of the outcomes of the conference for the WMO World Weather Research Programme (WWRP) and Global Atmospheric Watch (GAW) long term goals and provide examples of seamless modelling and prediction across a range of timescales at convective and sub-kilometer scales for regional coupled forecasting applications at Environment and Climate Change Canada (ECCC).

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

Science.gov (United States)

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

2014-01-01

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

Statistical short-term earthquake prediction.

Science.gov (United States)

Kagan, Y Y; Knopoff, L

1987-06-19

A statistical procedure, derived from a theoretical model of fracture growth, is used to identify a foreshock sequence while it is in progress. As a predictor, the procedure reduces the average uncertainty in the rate of occurrence for a future strong earthquake by a factor of more than 1000 when compared with the Poisson rate of occurrence. About one-third of all main shocks with local magnitude greater than or equal to 4.0 in central California can be predicted in this way, starting from a 7-year database that has a lower magnitude cut off of 1.5. The time scale of such predictions is of the order of a few hours to a few days for foreshocks in the magnitude range from 2.0 to 5.0.

How Emotional Pictures Influence Visuospatial Binding in Short-Term Memory in Ageing and Alzheimer's Disease?

Science.gov (United States)

Borg, Celine; Leroy, Nicolas; Favre, Emilie; Laurent, Bernard; Thomas-Anterion, Catherine

2011-01-01

The present study examines the prediction that emotion can facilitate short-term memory. Nevertheless, emotion also recruits attention to process information, thereby disrupting short-term memory when tasks involve high attentional resources. In this way, we aimed to determine whether there is a differential influence of emotional information on…

Readiness for change and short-term outcomes of female adolescents in residential treatment for anorexia nervosa.

Science.gov (United States)

McHugh, Matthew D

2007-11-01

To determine if readiness for change (RFC) at admission predicted length of stay (LOS) and short-term outcomes among female adolescents in residential treatment for anorexia nervosa (AN). Using a prospective cohort design to collect data from participants (N = 65) at admission and discharge, Kaplan-Meier survival analysis and Cox regression tested whether RFC on admission predicted time in LOS to a favorable short-term outcome--a composite endpoint based on minimum criteria for weight gain, drive for thinness, depression, anxiety, and health-related quality of life (HRQOL). Participants with low RFC had a mean survival time to a favorable short-term outcome of 59.4 days compared to 34.1 days for those with high RFC (log rank = 8.44, df = 1, p = .003). The probability of a favorable short-term outcome was 5.30 times greater for participants with high RFC. Readiness for change is a useful predictor of a favorable short-term outcome and should be considered in the assessment profile of patients with AN. (c) 2007 by Wiley Periodicals, Inc.

The Impact of Ocean Observations in Seasonal Climate Prediction

Science.gov (United States)

Rienecker, Michele; Keppenne, Christian; Kovach, Robin; Marshak, Jelena

2010-01-01

The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. This presentation will address the impact these different observations have had on seasonal climate predictions with the GMAO's coupled model.

Climate Prediction Center - Atlantic Hurricane Outlook

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News ; Seasonal Climate Summary Archive The 2018 Atlantic hurricane season outlook is an official product of the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC). The outlook is

Long-term climatic change and sustainable ground water resources management

International Nuclear Information System (INIS)

Loaiciga, Hugo A

2009-01-01

Atmospheric concentrations of greenhouse gases (GHGs), prominently carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), and halocarbons, have risen from fossil-fuel combustion, deforestation, agriculture, and industry. There is currently heated national and international debate about the consequences of such increasing concentrations of GHGs on the Earth's climate, and, ultimately, on life and society in the world as we know it. This paper reviews (i) long-term patterns of climate change, secular climatic variability, and predicted population growth and their relation to water resources management, and, specifically, to ground water resources management, (ii) means available for mitigating and adapting to trends of climatic change and climatic variability and their impacts on ground water resources. Long-term (that is, over hundreds of millions of years), global-scale, climatic fluctuations are compared with more recent (in the Holocene) patterns of the global and regional climates to shed light on the meaning of rising mean surface temperature over the last century or so, especially in regions whose historical hydroclimatic records exhibit large inter-annual variability. One example of regional ground water resources response to global warming and population growth is presented.

Climate Prediction Center - Outlooks: CFS Forecast of Seasonal Climate

Science.gov (United States)

National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. CFS Seasonal Climate Forecasts CFS Forecast of Seasonal Climate discontinued after October 2012. This page displays seasonal climate anomalies from the NCEP coupled forecast

A fuzzy inference model for short-term load forecasting

International Nuclear Information System (INIS)

Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad

2009-01-01

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

Seasonal climate prediction for North Eurasia

International Nuclear Information System (INIS)

Kryjov, Vladimir N

2012-01-01

An overview of the current status of the operational seasonal climate prediction for North Eurasia is presented. It is shown that the performance of existing climate models is rather poor in seasonal prediction for North Eurasia. Multi-model ensemble forecasts are more reliable than single-model ones; however, for North Eurasia they tend to be close to climatological ones. Application of downscaling methods may improve predictions for some locations (or regions). However, general improvement of the reliability of seasonal forecasts for North Eurasia requires improvement of the climate prediction models. (letter)

Short- and Long-Term Feedbacks on Vegetation Water Use: Unifying Evidence from Observations and Modeling

Science.gov (United States)

Mackay, D. S.

2001-05-01

Recent efforts to measure and model the interacting influences of climate, soil, and vegetation on soil water and nutrient dynamics have identified numerous important feedbacks that produce nonlinear responses. In particular, plant physiological factors that control rates of transpiration respond to soil water deficits and vapor pressure deficits (VPD) in the short-term, and to climate, nutrient cycling and disturbance in the long-term. The starting point of this presentation is the observation that in many systems, in particular forest ecosystems, conservative water use emerges as a result of short-term closure of stomata in response to high evaporative demand, and long-term vegetative canopy development under nutrient limiting conditions. Evidence for important short-term controls is presented from sap flux measurements of stand transpiration, remote sensing, and modeling of transpiration through a combination of physically-based modeling and Monte Carlo analysis. A common result is a strong association between stomatal conductance (gs) and the negative evaporative gain (∂ gs/∂ VPD) associated with the sensitivity of stomatal closure to rates of water loss. The importance of this association from the standpoint of modeling transpiration depends on the degree of canopy-atmosphere coupling. This suggests possible simplifications to future canopy component models for use in watershed and larger-scale hydrologic models for short-term processes. However, further results are presented from theoretical modeling, which suggest that feedbacks between hydrology and vegetation in current long-term (inter-annual to century) models may be too simple, as they do not capture the spatially variable nature of slow nutrient cycling in response to soil water dynamics and site history. Memory effects in the soil nutrient pools can leave lasting effects on more rapid processes associated with soil, vegetation, atmosphere coupling.

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-07-17

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

Accordion complication grading predicts short-term outcome after right colectomy.

Science.gov (United States)

Klos, Coen L; Safar, Bashar; Hunt, Steven R; Wise, Paul E; Birnbaum, Elisa H; Mutch, Matthew G; Fleshman, James W; Dharmarajan, Sekhar

2014-08-01

The Accordion severity grading system is a novel system to score the severity of postoperative complications in a standardized fashion. This study aims to demonstrate the validity of the Accordion system in colorectal surgery by correlating severity grades with short-term outcomes after right colectomy for colon cancer. This is a retrospective cohort review of patients who underwent right colectomy for cancer between January 1, 2002, and January 31, 2007, at a single tertiary care referral center. Complications were categorized according to the Accordion severity grading system: grades 1 (mild), 2 (moderate), 3-5 (severe), and 6 (death). Outcome measures were hospital stay, 30-d readmission rate and 1-y survival. Correlation between Accordion grades and outcome measures is reflected by Spearman rho (ρ). One-year survival was obtained per Kaplan-Meier method and compared by logrank test for trend. Significance was set at P ≤ 0.05. Overall, 235 patients underwent right colectomy for cancer of which 122 (51.9%) had complications. In total, 52 (43%) had an Accordion grade 1 complication; 44 (36%) grade 2; four (3%) grade 3; 11 (9%) grade 4; seven (6%) grade 5; and four (3%) grade 6. There was significant correlation between Accordion grades and hospital stay (ρ = 0.495, P trend in 1-y survival as complication severity by Accordion grade increased (P = 0.02). The Accordion grading system is a useful tool to estimate short-term outcomes after right colectomy for cancer. High-grade Accordion complications are associated with longer hospital stay and increased risk of readmission and mortality. Published by Elsevier Inc.

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

Science.gov (United States)

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

2018-01-01

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

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

Science.gov (United States)

Cowan, Nelson

2008-01-01

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

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

Science.gov (United States)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

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

Comparison of short-term rainfall forecasts for modelbased flow prediction in urban drainage systems

DEFF Research Database (Denmark)

Thorndahl, Søren; Ahm, Malte; Nielsen, Jesper Ellerbek

2013-01-01

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

Short-term variations of Icelandic ice cap mass inferred from cGPS coordinate time series

DEFF Research Database (Denmark)

Compton, Kathleen; Bennett, Richard A.; Hreinsdóttir, Sigrún

2017-01-01

As the global climate changes, understanding short-term variations in water storage is increasingly important. Continuously operating Global Positioning System (cGPS) stations in Iceland record annual periodic motion—the elastic response to winter accumulation and spring melt seasons—with peak-to...

Development of Short-term Molecular Thresholds to Predict Long-term Mouse Liver Tumor Outcomes: Phthalate Case Study

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Short-term molecular profiles are a central component of strategies to model health effects of environmental chemicals. In this study, a 7 day mouse assay was used to evaluate transcriptomic and proliferative responses in the liver for a hepatocarcinogenic phthalate, di (2-ethylh...

Facilitative Effects of Forgetting from Short-Term Memory on Growth of Long-Term Memory in Retardates

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Sperber, Richard D.

1976-01-01

Competing explanations of the beneficial effect of spacing in retardate discrimination learning were tested. Results are inconsistent with consolidation and rehearsal theories but support the prediction of the Geber, Greenfield, and House spacing model that forgetting from short-term memory facilities retardate learning. (Author/SB)

Climate prediction and predictability

Science.gov (United States)

Allen, Myles

2010-05-01

Climate prediction is generally accepted to be one of the grand challenges of the Geophysical Sciences. What is less widely acknowledged is that fundamental issues have yet to be resolved concerning the nature of the challenge, even after decades of research in this area. How do we verify or falsify a probabilistic forecast of a singular event such as anthropogenic warming over the 21st century? How do we determine the information content of a climate forecast? What does it mean for a modelling system to be "good enough" to forecast a particular variable? How will we know when models and forecasting systems are "good enough" to provide detailed forecasts of weather at specific locations or, for example, the risks associated with global geo-engineering schemes. This talk will provide an overview of these questions in the light of recent developments in multi-decade climate forecasting, drawing on concepts from information theory, machine learning and statistics. I will draw extensively but not exclusively from the experience of the climateprediction.net project, running multiple versions of climate models on personal computers.

Ensemble-based Regional Climate Prediction: Political Impacts

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Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

2008-12-01

Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

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

International Nuclear Information System (INIS)

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

2016-01-01

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

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

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

2017-07-01

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

Attentional Demands Predict Short-Term Memory Load Response in Posterior Parietal Cortex

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Magen, Hagit; Emmanouil, Tatiana-Aloi; McMains, Stephanie A.; Kastner, Sabine; Treisman, Anne

2009-01-01

Limits to the capacity of visual short-term memory (VSTM) indicate a maximum storage of only 3 or 4 items. Recently, it has been suggested that activity in a specific part of the brain, the posterior parietal cortex (PPC), is correlated with behavioral estimates of VSTM capacity and might reflect a capacity-limited store. In three experiments that…

The Value of Median Nerve Sonography as a Predictor for Short- and Long-Term Clinical Outcomes in Patients with Carpal Tunnel Syndrome: A Prospective Long-Term Follow-Up Study.

Directory of Open Access Journals (Sweden)

Alexander Marschall

Full Text Available To investigate the prognostic value of B-mode and Power Doppler (PD ultrasound of the median nerve for the short- and long-term clinical outcomes of patients with carpal tunnel syndrome (CTS.Prospective study of 135 patients with suspected CTS seen 3 times: at baseline, then at short-term (3 months and long-term (15-36 months follow-up. At baseline, the cross-sectional area (CSA of the median nerve was measured with ultrasound at 4 levels on the forearm and wrist. PD signals were graded semi-quantitatively (0-3. Clinical outcomes were evaluated at each visit with the Boston Questionnaire (BQ and the DASH Questionnaire, as well as visual analogue scales for the patient's assessment of pain (painVAS and physician's global assessment (physVAS. The predictive values of baseline CSA and PD for clinical outcomes were determined with multivariate logistic regression models.Short-term and long-term follow-up data were available for 111 (82.2% and 105 (77.8% patients, respectively. There was a final diagnosis of CTS in 84 patients (125 wrists. Regression analysis revealed that the CSA, measured at the carpal tunnel inlet, predicted short-term clinical improvement according to BQ in CTS patients undergoing carpal tunnel surgery (OR 1.8, p = 0.05, but not in patients treated conservatively. Neither CSA nor PD assessments predicted short-term improvement of painVAS, physVAS or DASH, nor was any of the ultrasound parameters useful for the prediction of long-term clinical outcomes.Ultrasound assessment of the median nerve at the carpal tunnel inlet may predict short-term clinical improvement in CTS patients undergoing carpal tunnel release, but long-term outcomes are unrelated to ultrasound findings.

The Demonstration of Short-Term Consolidation.

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Jolicoeur, Pierre; Dell'Acqua, Roberto

1998-01-01

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

Short-Term Expectation Formation Versus Long-Term Equilibrium Conditions: The Danish Housing Market

Directory of Open Access Journals (Sweden)

Andreas Hetland

2017-09-01

Full Text Available The primary contribution of this paper is to establish that the long-swings behavior observed in the market price of Danish housing since the 1970s can be understood by studying the interplay between short-term expectation formation and long-run equilibrium conditions. We introduce an asset market model for housing based on uncertainty rather than risk, which under mild assumptions allows for other forms of forecasting behavior than rational expectations. We test the theory via an I(2 cointegrated VAR model and find that the long-run equilibrium for the housing price corresponds closely to the predictions from the theoretical framework. Additionally, we corroborate previous findings that housing markets are well characterized by short-term momentum forecasting behavior. Our conclusions have wider relevance, since housing prices play a role in the wider Danish economy, and other developed economies, through wealth effects.

Climate Prediction Center - monthly Outlook

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Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Outlooks monthly Climate Outlooks Banner OFFICIAL Forecasts June 2018 [UPDATED MONTHLY FORECASTS SERVICE ) Canonical Correlation Analysis ECCA - Ensemble Canonical Correlation Analysis Optimal Climate Normals

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

DEFF Research Database (Denmark)

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

2016-01-01

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

Clinical Significance of the Prognostic Nutritional Index for Predicting Short- and Long-Term Surgical Outcomes After Gastrectomy: A Retrospective Analysis of 7781 Gastric Cancer Patients.

Science.gov (United States)

Lee, Jee Youn; Kim, Hyoung-Il; Kim, You-Na; Hong, Jung Hwa; Alshomimi, Saeed; An, Ji Yeong; Cheong, Jae-Ho; Hyung, Woo Jin; Noh, Sung Hoon; Kim, Choong-Bai

2016-05-01

To evaluate the predictive and prognostic significance of the prognostic nutritional index (PNI) in a large cohort of gastric cancer patients who underwent gastrectomy.Assessing a patient's immune and nutritional status, PNI has been reported as a predictive marker for surgical outcomes in various types of cancer.We retrospectively reviewed data from a prospectively maintained database of 7781 gastric cancer patients who underwent gastrectomy from January 2001 to December 2010 at a single center. From this data, we analyzed clinicopathologic characteristics, PNI, and short- and long-term surgical outcomes for each patient. We used the PNI value for the 10th percentile (46.70) of the study cohort as a cut-off for dividing patients into low and high PNI groups.Regarding short-term outcomes, multivariate analysis showed a low PNI (odds ratio [OR] = 1.505, 95% CI = 1.212-1.869, P cancer recurrence.

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

OpenAIRE

Cowan, Nelson

2008-01-01

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

The roles of self-efficacy and motivation in the prediction of short- and long-term adherence to exercise among patients with coronary heart disease.

Science.gov (United States)

Slovinec D'Angelo, Monika E; Pelletier, Luc G; Reid, Robert D; Huta, Veronika

2014-11-01

Poor adherence to regular exercise is a documented challenge among people with heart disease. Identifying key determinants of exercise adherence and distinguishing between the processes driving short- and long-term adherence to regular exercise is a valuable endeavor. The purpose of the present study was to test a model of exercise behavior change, which incorporates motivational orientations and self-efficacy for exercise behavior, in the prediction of short- and long-term exercise adherence. Male and female patients (N = 801) hospitalized for coronary heart disease were recruited from 3 tertiary care cardiac centers and followed for a period of 1 year after hospital discharge. A prospective, longitudinal design was used to examine the roles of motivation and self-efficacy (measured at recruitment and at 2 and 6 months after discharge) in the prediction of exercise behavior at 6 and 12 months. Baseline measures of exercise and clinical and demographic covariates were included in the analyses. Structural equation modeling showed that both autonomous motivation and self-efficacy were important determinants of short-term (6-month) exercise behavior regulation, but that only autonomous motivation remained a significant predictor of long-term (12-month) exercise behavior. Self-efficacy partially mediated the relationship between motivation for exercise and 6-month exercise behavior. This research confirmed the roles of autonomous motivation and self-efficacy in the health behavior change process and emphasized the key function of autonomous motivation in exercise maintenance. Theoretical and cardiac rehabilitation program applications of this research are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Climate Prediction Center - Monitoring and Data - Regional Climate Maps:

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National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. HOME > Monitoring and Data > U.S. Climate Data > ; Precipitation & Temperature > Regional Climate Maps: USA Menu Weekly 1-Month 3-Month 12-Month Weekly

Self-reported immature defense style as a predictor of outcome in short-term and long-term psychotherapy.

Science.gov (United States)

Laaksonen, Maarit A; Sirkiä, Carlos; Knekt, Paul; Lindfors, Olavi

2014-07-01

Identification of pretreatment patient characteristics predictive of psychotherapy outcome could help to guide treatment choices. This study evaluates patients' initial level of immature defense style as a predictor of the outcome of short-term versus long-term psychotherapy. In the Helsinki Psychotherapy Study, 326 adult outpatients with mood or anxiety disorder were randomized to individual short-term (psychodynamic or solution-focused) or long-term (psychodynamic) psychotherapy. Their defense style was assessed at baseline using the 88-item Defense Style Questionnaire and classified as low or high around the median value of the respective score. Both specific (Beck Depression Inventory [BDI], Hamilton Depression Rating Scale [HDRS], Symptom Check List Anxiety Scale [SCL-90-Anx], Hamilton Anxiety Rating Scale [HARS]) and global (Symptom Check List Global Severity Index [SCL-90-GSI], Global Assessment of Functioning Scale [GAF]) psychiatric symptoms were measured at baseline and 3-7 times during a 3-year follow-up. Patients with high use of immature defense style experienced greater symptom reduction in long-term than in short-term psychotherapy by the end of the 3-year follow-up (50% vs. 34%). Patients with low use of immature defense style experienced faster symptom reduction in short-term than in long-term psychotherapy during the first year of follow-up (34% vs. 19%). Knowledge of patients' initial level of immature defense style may potentially be utilized in tailoring treatments. Further research on defense styles as outcome predictors in psychotherapies of different types is needed.

Weather, not climate, defines distributions of vagile bird species.

Directory of Open Access Journals (Sweden)

April E Reside

Full Text Available BACKGROUND: Accurate predictions of species distributions are essential for climate change impact assessments. However the standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. The benefit of using temporally explicit weather and distribution data has not been assessed. We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species. METHODOLOGY: We tested our hypothesis by generating distribution models for 157 bird species found in Australian tropical savannas (ATS using modelling algorithm Maxent. The variable weather of the ATS supports a bird assemblage with variable movement patterns and a high incidence of nomadism. We developed "weather" models by relating climatic variables (mean temperature, rainfall, rainfall seasonality and temperature seasonality from the three month, six month and one year period preceding each bird record over a 58 year period (1950-2008. These weather models were compared against models built using long-term (30 year averages of the same climatic variables. CONCLUSIONS: Weather models consistently achieved higher model scores than climate models, particularly for wide-ranging, nomadic and desert species. Climate models predicted larger range areas for species, whereas weather models quantified fluctuations in habitat suitability across months, seasons and years. Models based on long-term climate averages over-estimate availability of suitable habitat and species' climatic tolerances, masking species potential vulnerability to climate change. Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions.

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

Energy Technology Data Exchange (ETDEWEB)

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

2017-08-23

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

Prosodic Similarity Effects in Short-Term Memory in Developmental Dyslexia.

Science.gov (United States)

Goswami, Usha; Barnes, Lisa; Mead, Natasha; Power, Alan James; Leong, Victoria

2016-11-01

Children with developmental dyslexia are characterized by phonological difficulties across languages. Classically, this 'phonological deficit' in dyslexia has been investigated with tasks using single-syllable words. Recently, however, several studies have demonstrated difficulties in prosodic awareness in dyslexia. Potential prosodic effects in short-term memory have not yet been investigated. Here we create a new instrument based on three-syllable words that vary in stress patterns, to investigate whether prosodic similarity (the same prosodic pattern of stressed and unstressed syllables) exerts systematic effects on short-term memory. We study participants with dyslexia and age-matched and younger reading-level-matched typically developing controls. We find that all participants, including dyslexic participants, show prosodic similarity effects in short-term memory. All participants exhibited better retention of words that differed in prosodic structure, although participants with dyslexia recalled fewer words accurately overall compared to age-matched controls. Individual differences in prosodic memory were predicted by earlier vocabulary abilities, by earlier sensitivity to syllable stress and by earlier phonological awareness. To our knowledge, this is the first demonstration of prosodic similarity effects in short-term memory. The implications of a prosodic similarity effect for theories of lexical representation and of dyslexia are discussed. © 2016 The Authors. Dyslexia published by John Wiley & Sons Ltd. © 2016 The Authors. Dyslexia published by John Wiley & Sons Ltd.

Short-term memory across eye blinks.

Science.gov (United States)

Irwin, David E

2014-01-01

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

Short-term memory and dual task performance

Science.gov (United States)

Regan, J. E.

1982-01-01

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

Association between early attention-deficit/hyperactivity symptoms and current verbal and visuo-spatial short-term memory.

Science.gov (United States)

Gau, Susan Shur-Fen; Chiang, Huey-Ling

2013-01-01

Deficits in short-term memory are common in adolescents with attention-deficit/hyperactivity disorder (ADHD), but their current ADHD symptoms cannot well predict their short-term performance. Taking a developmental perspective, we wanted to clarify the association between ADHD symptoms at early childhood and short-term memory in late childhood and adolescence. The participants included 401 patients with a clinical diagnosis of DSM-IV ADHD, 213 siblings, and 176 unaffected controls aged 8-17 years (mean age, 12.02 ± 2.24). All participants and their mothers were interviewed using the Chinese Kiddie Epidemiologic version of the Schedule for Affective Disorders and Schizophrenia to obtain information about ADHD symptoms and other psychiatric disorders retrospectively, at an earlier age first, then currently. The participants were assessed with the Wechsler Intelligence Scale for Children--3rd edition, including Digit Span, and the Spatial working memory task of the Cambridge Neuropsychological Test Automated Battery. Multi-level regression models were used for data analysis. Although crude analyses revealed that inattention, hyperactivity, and impulsivity symptoms significantly predicted deficits in short-term memory, only inattention symptoms had significant effects (all pshort-term memory at the current assessment. Therefore, our findings suggest that earlier inattention symptoms are associated with impaired verbal and visuo-spatial short-term memory at a later development stage. Impaired short-term memory in adolescence can be detected earlier by screening for the severity of inattention in childhood. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

International Nuclear Information System (INIS)

Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

2016-01-01

Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

Climate Prediction Center - Outlooks: Current UV Index Forecast Map

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Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Service NOAA Center for Weather and Climate Prediction Climate Prediction Center 5830 University Research Court College Park, Maryland 20740 Page Author: Climate Prediction Center Internet Team Disclaimer

Short-term LNG-markets

International Nuclear Information System (INIS)

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

2005-01-01

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

Climate Prediction Center - Monitoring & Data Index

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Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Oceanic & Atmospheric Monitoring and Data Monitoring Weather & Climate in Realtime Climate Diagnostics Bulletin Preliminary Climate Diagnostics Bulletin Figures Monthly Atmospheric & Sea Surface

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

DEFF Research Database (Denmark)

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

2016-01-01

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

Short-Term Memory as an Additional Predictor of School Achievement for Immigrant Children?

Science.gov (United States)

te Nijenhuis, Jan; Resing, Wilma; Tolboom, Elsbeth; Bleichrodt, Nico

2004-01-01

The predictive validity and utility of assessment procedures can be increased by adding predictors to the prediction supplied by general ability tests. Of Jensen's early work comes the suggestion of focusing on the cognitive ability short-term memory (STM), especially for low-"g" Black children. Meta-analysis convincingly shows high…

COLLABORATIVE RESEARCH: TOWARDS ADVANCED UNDERSTANDING AND PREDICTIVE CAPABILITY OF CLIMATE CHANGE IN THE ARCTIC USING A HIGH-RESOLUTION REGIONAL ARCTIC CLIMATE SYSTEM MODEL

Energy Technology Data Exchange (ETDEWEB)

Gutowski, William J.

2013-02-07

The motivation for this project was to advance the science of climate change and prediction in the Arctic region. Its primary goals were to (i) develop a state-of-the-art Regional Arctic Climate system Model (RACM) including high-resolution atmosphere, land, ocean, sea ice and land hydrology components and (ii) to perform extended numerical experiments using high performance computers to minimize uncertainties and fundamentally improve current predictions of climate change in the northern polar regions. These goals were realized first through evaluation studies of climate system components via one-way coupling experiments. Simulations were then used to examine the effects of advancements in climate component systems on their representation of main physics, time-mean fields and to understand variability signals at scales over many years. As such this research directly addressed some of the major science objectives of the BER Climate Change Research Division (CCRD) regarding the advancement of long-term climate prediction.

Malnutrition: a highly predictive risk factor of short-term mortality in elderly presenting to the emergency department.

Science.gov (United States)

Gentile, S; Lacroix, O; Durand, A C; Cretel, E; Alazia, M; Sambuc, R; Bonin-Guillaume, S

2013-04-01

To identify independent risk factors of mortality among elderly patients in the 3 months after their visit (T3) to an emergency department (ED). Prospective cohort study. University hospital ED in an urban setting in France. One hundred seventy-three patients aged 75 and older were admitted to the ED over two weeks (18.7% of the 924 ED visits). Of these, 164 patients (94.8%) were included in our study, and 157 (95.7%) of them were followed three months after their ED visit. During the inclusion period (T0), a standardized questionnaire was used to collect data on socio-demographic and environmental characteristics, ED visit circumstances, medical conditions and geriatric assessment including functional and nutritional status. Three months after the ED visits (T3), patients or their caregivers were interviewed to collect data on vital status, and ED return or hospitalization. Among the 157 patients followed at T3, 14.6% had died, 19.9% had repeated ED visits, and 63.1% had been hospitalized. The two independent predictive factors for mortality within the 3 months after ED visit were: malnutrition screened by the Mini Nutritional Assessment short-form (MNA-SF) (OR=20.2; 95% CI: 5.74-71.35; pMalnutrition is the strongest independent risk factor predicting short-term mortality in elderly patients visiting the ED, and it was easily detected by MNA-SF and supported from the ED visit.

From probabilistic forecasts to statistical scenarios of short-term wind power production

DEFF Research Database (Denmark)

Pinson, Pierre; Papaefthymiou, George; Klockl, Bernd

2009-01-01

on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time-dependent and multistage decision-making problems, e.g. optimal operation of combined wind-storage systems or multiple-market trading with different gate closures......Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform....... This issue is addressed here by describing a method that permits the generation of statistical scenarios of short-term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion...

Short-term incentive schemes for hospital managers

Directory of Open Access Journals (Sweden)

Lucas Malambe

2013-10-01

Full Text Available Orientation: Short-term incentives, considered to be an extrinsic motivation, are commonly used to motivate performance. This study explored hospital managers’ perceptions of short term incentives in maximising performance and retention. Research purpose: The study explored the experiences, views and perceptions of private hospital managers in South Africa regarding the use of short-term incentives to maximise performance and retention, as well as the applicability of the findings to public hospitals. Motivation for the study: Whilst there is an established link between performance reward schemes and organisational performance, there is little understanding of the effects of short term incentives on the performance and retention of hospital managers within the South African context. Research design, approach, and method: The study used a qualitative research design: interviews were conducted with a purposive sample of 19 hospital managers, and a thematic content analysis was performed. Main findings: Short-term incentives may not be the primary motivator for hospital managers, but they do play a critical role in sustaining motivation. Participants indicated that these schemes could also be applicable to public hospitals. Practical/managerial implications: Hospital managers are inclined to be more motivated by intrinsic than extrinsic factors. However, hospital managers (as middle managers also seem to be motivated by short-term incentives. A combination of intrinsic and extrinsic motivators should thus be used to maximise performance and retention. Contribution/value-add: Whilst the study sought to explore hospital managers’ perceptions of short-term incentives, it also found that an adequate balance between internal and external motivators is key to implementing an effective short-term incentive scheme.

Short-Term Intercultural Psychotherapy: Ethnographic Inquiry

Science.gov (United States)

Seeley, Karen M.

2004-01-01

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

Why do short term workers have high mortality?

DEFF Research Database (Denmark)

Kolstad, Henrik; Olsen, Jørn

1999-01-01

or violence, the rate ratios for short term employment were 2.30 (95% Cl 1.74-3.06) and 1.86 (95% Cl 1.35-2.56), respectively. An unhealthy lifestyle may also be a determinant of short term employment. While it is possible in principle to adjust for lifestyle factors if proper data are collected, the health......Increased mortality is often reported among workers in short term employment. This may indicate either a health-related selection process or the presence of different lifestyle or social conditions among short term workers. The authors studied these two aspects of short term employment among 16...

Predictive Validity of the Columbia-Suicide Severity Rating Scale for Short-Term Suicidal Behavior: A Danish Study of Adolescents at a High Risk of Suicide.

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Conway, Paul Maurice; Erlangsen, Annette; Teasdale, Thomas William; Jakobsen, Ida Skytte; Larsen, Kim Juul

2017-07-03

Using the Columbia-Suicide Severity Rating Scale (C-SSRS), we examined the predictive and incremental predictive validity of past-month suicidal behavior and ideation for short-term suicidal behavior among adolescents at high risk of suicide. The study was conducted in 2014 on a sample of 85 adolescents (90.6% females) who participated at follow-up (85.9%) out of the 99 (49.7%) baseline respondents. All adolescents were recruited from a specialized suicide-prevention clinic in Denmark. Through multivariate logistic regression analyses, we examined whether baseline suicidal behavior predicted subsequent suicidal behavior (actual attempts and suicidal behavior of any type, including preparatory acts, aborted, interrupted and actual attempts; mean follow-up of 80.8 days, SD = 52.4). Furthermore, we examined whether suicidal ideation severity and intensity incrementally predicted suicidal behavior at follow-up over and above suicidal behavior at baseline. Actual suicide attempts at baseline strongly predicted suicide attempts at follow-up. Baseline suicidal ideation severity and intensity did not significantly predict future actual attempts over and above baseline attempts. The suicidal ideation intensity items deterrents and duration were significant predictors of subsequent actual attempts after adjustment for baseline suicide attempts and suicidal behavior of any type, respectively. Suicidal ideation severity and intensity, and the intensity items frequency, duration and deterrents, all significantly predicted any type of suicidal behavior at follow-up, also after adjusting for baseline suicidal behavior. The present study points to an incremental predictive validity of the C-SSRS suicidal ideation scales for short-term suicidal behavior of any type among high-risk adolescents.

The USGS plan for short-term prediction of the anticipated Parkfield earthquake

Science.gov (United States)

Bakun, W.H.

1988-01-01

Aside from the goal of better understanding the Parkfield earthquake cycle, it is the intention of the U.S Geological Survey to attempt to issue a warning shortly before the anticipated earthquake. Although short-term earthquake warnings are not yet generally feasible, the wealth of information available for the previous significant Parkfield earthquakes suggests that if the next earthquake follows the pattern of "characteristic" Parkfield shocks, such a warning might be possible. Focusing on earthquake precursors reported for the previous  "characteristic" shocks, particulary the 1934 and 1966 events, the USGS developed a plan* in late 1985 on which to base earthquake warnings for Parkfield and has assisted State, county, and local officials in the Parkfield area to prepare a coordinated, reasonable response to a warning, should one be issued. 

What is the Climate?

Science.gov (United States)

Lovejoy, S.; Schertzer, D.

2009-04-01

Although meteorology and climatology are increasingly considered to be distinct sciences, the actual boundary between "climate scales" and "meteorological scales" is not clear and there are no universally accepted definitions. It is still hard to improve upon the old adage "the climate is what you expect, the weather is what you get". For example [Committee on Radiative Forcing Effects on Climate, 2005], the National Academy of Science essentially accepts this ("Climate is conventionally defined as the long-term statistics of the weather") proposing only to expand the definition of climate to encompass the oceanic and terrestrial spheres as well as chemical components of the atmosphere. However the weather itself has nontrivial statistics so that the key weather/climate distinction depend on apparently subjective distinction between long-term and short-term statistics. In this talk, we give an objective basis to the weather/climate distinction with the help of an anisotropic space-time turbulence theory and both lidar satellite radiances, in situ spectra and numerical models of the atmosphere and reanalyses. We show that the latter accurately follow the predictions of multiplicative cascade models up to about 7-10 days. This marks the beginning of a weather/climate transition region which extends up to the cascade outer scale of about 20-30 days (depending somewhat on the atmospheric field), after which the climate regime begins. We bolster this interpretation by empirically constructing space-time (Stommel) diagrammes; we obtain near linear relations between time and (horizontal) space and theoretically predicted power law relations between the vertical and time up until the end of the weather regime (~10000 km in the horizontal, ~10 km in the vertical, ~10 days in time). We discuss the implications for weather, climate and climate trends.

Use of short-term test systems for the prediction of the hazard represented by potential chemical carcinogens

Energy Technology Data Exchange (ETDEWEB)

Glass, L.R.; Jones, T.D.; Easterly, C.E.; Walsh, P.J.

1990-10-01

It has been hypothesized that results from short-term bioassays will ultimately provide information that will be useful for human health hazard assessment. Historically, the validity of the short-term tests has been assessed using the framework of the epidemiologic/medical screens. In this context, the results of the carcinogen (long-term) bioassay is generally used as the standard. However, this approach is widely recognized as being biased and, because it employs qualitative data, cannot be used to assist in isolating those compounds which may represent a more significant toxicologic hazard than others. In contrast, the goal of this research is to address the problem of evaluating the utility of the short-term tests for hazard assessment using an alternative method of investigation. Chemicals were selected mostly from the list of carcinogens published by the International Agency for Research on Carcinogens (IARC); a few other chemicals commonly recognized as hazardous were included. Tumorigenicity and mutagenicity data on 52 chemicals were obtained from the Registry of Toxic Effects of Chemical Substances (RTECS) and were analyzed using a relative potency approach. The data were evaluated in a format which allowed for a comparison of the ranking of the mutagenic relative potencies of the compounds (as estimated using short-term data) vs. the ranking of the tumorigenic relative potencies (as estimated from the chronic bioassays). Although this was a preliminary investigation, it offers evidence that the short-term tests systems may be of utility in ranking the hazards represented by chemicals which may contribute to increased carcinogenesis in humans as a result of occupational or environmental exposures. 177 refs., 8 tabs.

Use of short-term test systems for the prediction of the hazard represented by potential chemical carcinogens

International Nuclear Information System (INIS)

Glass, L.R.; Jones, T.D.; Easterly, C.E.; Walsh, P.J.

1990-10-01

It has been hypothesized that results from short-term bioassays will ultimately provide information that will be useful for human health hazard assessment. Historically, the validity of the short-term tests has been assessed using the framework of the epidemiologic/medical screens. In this context, the results of the carcinogen (long-term) bioassay is generally used as the standard. However, this approach is widely recognized as being biased and, because it employs qualitative data, cannot be used to assist in isolating those compounds which may represent a more significant toxicologic hazard than others. In contrast, the goal of this research is to address the problem of evaluating the utility of the short-term tests for hazard assessment using an alternative method of investigation. Chemicals were selected mostly from the list of carcinogens published by the International Agency for Research on Carcinogens (IARC); a few other chemicals commonly recognized as hazardous were included. Tumorigenicity and mutagenicity data on 52 chemicals were obtained from the Registry of Toxic Effects of Chemical Substances (RTECS) and were analyzed using a relative potency approach. The data were evaluated in a format which allowed for a comparison of the ranking of the mutagenic relative potencies of the compounds (as estimated using short-term data) vs. the ranking of the tumorigenic relative potencies (as estimated from the chronic bioassays). Although this was a preliminary investigation, it offers evidence that the short-term tests systems may be of utility in ranking the hazards represented by chemicals which may contribute to increased carcinogenesis in humans as a result of occupational or environmental exposures. 177 refs., 8 tabs

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

DEFF Research Database (Denmark)

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

2012-01-01

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

Short-term seaward fish migration in the Messolonghi Etoliko lagoons (Western Greek coast) in relation to climatic variables and the lunar cycle

Science.gov (United States)

Katselis, George; Koukou, Katerina; Dimitriou, Evagelos; Koutsikopoulos, Constantin

2007-07-01

In the present study we analysed the daily seaward migratory behaviour of four dominant euryhaline fish species (Mugilidae: Liza saliens, Liza aurata, Mugil cephalus and Sparidae: Sparus aurata) in the Messolonghi Etoliko lagoon system (Western Greek coast) based on the daily landings' time series of barrier traps and assessed the relationship between their migratory behaviour and various climatic variables (air temperature and atmospheric pressure) and the lunar cycle. A 2-year time series of daily fish landings (1993 and 1994), a long time series of daily air temperature and daily temperature range (1991 1998) as well as a 4-year time series of the daily atmospheric pressure (1994 1997) and daily pressure range were used. Harmonic models (HM) consisting of annual and lunar cycle harmonic components explained most (R2 > 0.80) of the mean daily species landings and temperature variations, while a rather low part of the variation (0.18 0.6) to the daily fluctuations of the climatic variables at seasonal, mid and short-term scales.

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

Science.gov (United States)

Binamé, Florence; Poncelet, Martine

2016-01-01

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

Climate Prediction Center - Expert Assessments Index

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Web resources and services. HOME > Monitoring and Data > Global Climate Data & Maps > ; Global Regional Climate Maps Regional Climate Maps Banner The Monthly regional analyses products are

Saving and Re-building Lives: Determinants of Short-term and Long-term Disaster Relief

Directory of Open Access Journals (Sweden)

Geethanjali SELVARETNAM

2014-11-01

Full Text Available We analyse both theoretically and empirically, the factors that influence the amount of humanitarian aid received by countries which are struck by natural disasters, particularly distinguishing between immediate disaster relief and long term humanitarian aid. The theoretical model is able to make predictions as well as explain some of the peculiarities in the empirical results. We show that both short and long term humanitarian aid increases with number of people killed, financial loss and level of corruption, while GDP per capita had no effect. More populated countries receive more humanitarian aid. Earthquake, tsunami and drought attract more aid.

Short-term mechanisms influencing volumetric brain dynamics

Directory of Open Access Journals (Sweden)

Nikki Dieleman

2017-01-01

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

Climate Prediction Center - Monitoring & Data: Seasonal ENSO Impacts on

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page National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center , state and local government Web resources and services. HOME > Monitoring and Data > U.S. Climate and Climate Prediction Climate Prediction Center 5830 University Research Court College Park, Maryland

A Standardized Evaluation System for Decadal Climate Prediction

Science.gov (United States)

Kadow, C.; Cubasch, U.

2012-12-01

The evaluation of decadal prediction systems is a scientific challenge as well as a technical challenge in the climate research. The major project MiKlip (www.fona-miklip.de) for medium-term climate prediction funded by the Federal Ministry of Education and Research in Germany (BMBF) has the aim to create a model system that can provide reliable decadal forecasts on climate and weather. The model system to be developed will be novel in several aspects, with great challenges for the methodology development. This concerns especially the determination of the initial conditions, the inclusion into the model of processes relevant to decadal predictions, the increase of the spatial resolution through regionalisation, the improvement or adjustment of statistical post-processing, and finally the synthesis and validation of the entire model system. Therefore, a standardized evaluation system will be part of the MiKlip system to validate it - developed by the project 'Integrated data and evaluation system for decadal scale prediction' (INTEGRATION). The presentation gives an overview of the different linkages of such a project, shows the different development stages and gives an outlook for users and possible end users in climate service. The technical interface combines all projects inside of MiKlip and invites them to participate in a common evaluation system. The system design and the validation strategy from a standalone tool in the beginning to a user friendly web based system using GRID technologies to an integrated part of the operational MiKlip system for industry and society will give the opportunity to enhance the MiKlip strategy. First results of different possibilities of such a system will be shown to present the scientific background through Taylor diagrams, ensemble skill scores and e.g. climatological means to show the usability and possibilities of MiKlip and the INTEGRATION project.

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

Science.gov (United States)

Raine, Adrian; And Others

1991-01-01

Children with speech disorders had lower short-term memory capacity and smaller word length effect than control children. Children with speech disorders also had reduced speech-motor activity during rehearsal. Results suggest that speech rate may be a causal determinant of verbal short-term memory capacity. (BC)

In Their Own Words: Assessing Global Citizenship in a Short-Term Study-Abroad Program in Bangladesh

Science.gov (United States)

Gambino, Giacomo; Hashim, S. Mohsin

2016-01-01

The article examines whether short-term study-abroad (STSA) experiences can cultivate the cultural understandings and ethical commitments entailed by a cosmopolitan civic education. We examine students' critical reflections on their participation in a two-week study-abroad program titled "Climate Change and Sustainable Development in…

Are abrupt climate changes predictable?

Science.gov (United States)

Ditlevsen, Peter

2013-04-01

It is taken for granted that the limited predictability in the initial value problem, the weather prediction, and the predictability of the statistics are two distinct problems. Lorenz (1975) dubbed this predictability of the first and the second kind respectively. Predictability of the first kind in a chaotic dynamical system is limited due to the well-known critical dependence on initial conditions. Predictability of the second kind is possible in an ergodic system, where either the dynamics is known and the phase space attractor can be characterized by simulation or the system can be observed for such long times that the statistics can be obtained from temporal averaging, assuming that the attractor does not change in time. For the climate system the distinction between predictability of the first and the second kind is fuzzy. This difficulty in distinction between predictability of the first and of the second kind is related to the lack of scale separation between fast and slow components of the climate system. The non-linear nature of the problem furthermore opens the possibility of multiple attractors, or multiple quasi-steady states. As the ice-core records show, the climate has been jumping between different quasi-stationary climates, stadials and interstadials through the Dansgaard-Oechger events. Such a jump happens very fast when a critical tipping point has been reached. The question is: Can such a tipping point be predicted? This is a new kind of predictability: the third kind. If the tipping point is reached through a bifurcation, where the stability of the system is governed by some control parameter, changing in a predictable way to a critical value, the tipping is predictable. If the sudden jump occurs because internal chaotic fluctuations, noise, push the system across a barrier, the tipping is as unpredictable as the triggering noise. In order to hint at an answer to this question, a careful analysis of the high temporal resolution NGRIP isotope

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

Energy Technology Data Exchange (ETDEWEB)

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

2012-07-01

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

Short Review on Predicting Fouling in RO Desalination

Directory of Open Access Journals (Sweden)

Alejandro Ruiz-García

2017-10-01

Full Text Available Reverse Osmosis (RO membrane fouling is one of the main challenges that membrane manufactures, the scientific community and industry professionals have to deal with. The consequences of this inevitable phenomenon have a negative effect on the performance of the desalination system. Predicting fouling in RO systems is key to evaluating the long-term operating conditions and costs. Much research has been done on fouling indices, methods, techniques and prediction models to estimate the influence of fouling on the performance of RO systems. This paper offers a short review evaluating the state of industry knowledge in the development of fouling indices and models in membrane systems for desalination in terms of use and applicability. Despite major efforts in this field, there are gaps in terms of effective methods and models for the estimation of fouling in full-scale RO desalination plants. In existing models applied to full-scale RO desalination plants, neither the spacer geometry of membranes, nor the efficiency and frequency of chemical cleanings are considered.

Climate Prediction Center - Forecasts & Outlook Maps, Graphs and Tables

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News list below The Climate Prediction Center (CPC) is responsible for issuing seasonal climate outlook maps , and National Centers for Environmental Prediction). These weather and climate products comprise the

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

Directory of Open Access Journals (Sweden)

Kristina M Visscher

2007-03-01

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

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

Science.gov (United States)

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

2007-03-01

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

Characterizing short-term stability for Boolean networks over any distribution of transfer functions

International Nuclear Information System (INIS)

Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; Mayo, Jackson R.; Armstrong, Robert C.

2016-01-01

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.

The Mind and Brain of Short-Term Memory

OpenAIRE

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

2008-01-01

The past 10 years have brought near-revolutionary changes in psychological theories about short-term memory, with similarly great advances in the neurosciences. Here, we critically examine the major psychological theories (the “mind”) of short-term memory and how they relate to evidence about underlying brain mechanisms. We focus on three features that must be addressed by any satisfactory theory of short-term memory. First, we examine the evidence for the architecture of short-term memory, w...

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

Science.gov (United States)

Belmore, Suzanne C; Shevell, Steven K

2011-02-09

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

Projected Applications of a "Weather in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

Science.gov (United States)

Jedlovec, Gary J.; Molthan, Andrew; Zavodsky, Bradley T.; Case, Jonathan L.; LaFontaine, Frank J.; Srikishen, Jayanthi

2010-01-01

The NASA Short-term Prediction Research and Transition Center (SPoRT)'s new "Weather in a Box" resources will provide weather research and forecast modeling capabilities for real-time application. Model output will provide additional forecast guidance and research into the impacts of new NASA satellite data sets and software capabilities. By combining several research tools and satellite products, SPoRT can generate model guidance that is strongly influenced by unique NASA contributions.

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

Directory of Open Access Journals (Sweden)

T. Penttilä

2008-03-01

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

Generation of statistical scenarios of short-term wind power production

DEFF Research Database (Denmark)

Pinson, Pierre; Papaefthymiou, George; Klockl, Bernd

2007-01-01

Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform...... on the development of the forecast uncertainty through forecast series. This issue is addressed here by describing a method that permits to generate statistical scenarios of wind generation that accounts for the interdependence structure of prediction errors, in plus of respecting predictive distributions of wind...

Predicting short and long-term exercise intentions and behaviour in patients with coronary artery disease: A test of protection motivation theory.

Science.gov (United States)

Tulloch, Heather; Reida, Robert; D'Angeloa, Monika Slovinec; Plotnikoff, Ronald C; Morrina, Louise; Beatona, Louise; Papadakisa, Sophia; Pipe, Andrew

2009-03-01

The purpose of this study was to examine the utility of protection motivation theory (PMT) in the prediction of exercise intentions and behaviour in the year following hospitalisation for coronary artery disease (CAD). Patients with documented CAD (n = 787), recruited at hospital discharge, completed questionnaires measuring PMT's threat (i.e. perceived severity and vulnerability) and coping (i.e. self-efficacy, response efficacy) appraisal constructs at baseline, 2 and 6 months, and exercise behaviour at baseline, 6 and 12 months post-hospitalisation. Structural equation modelling showed that the PMT model of exercise at 6 months had a good fit with the empirical data. Self-efficacy, response efficacy, and perceived severity predicted exercise intentions, which, in turn predicted exercise behaviour. Overall, the PMT variables accounted for a moderate amount of variance in exercise intentions (23%) and behaviour (20%). In contrast, the PMT model was not reliable for predicting exercise behaviour at 12 months post-hospitalisation. The data provided support for PMT applied to short-term, but not long-term, exercise behaviour among patients with CAD. Health education should concentrate on providing positive coping messages to enhance patients' confidence regarding exercise and their belief that exercise provides health benefits, as well as realistic information about disease severity.

Long-term monitoring of an amphibian community after a climate change- and infectious disease-driven species extirpation.

Science.gov (United States)

Bosch, Jaime; Fernández-Beaskoetxea, Saioa; Garner, Trenton W J; Carrascal, Luis María

2018-02-15

Infectious disease and climate change are considered major threats to biodiversity and act as drivers behind the global amphibian decline. This is, to a large extent, based on short-term studies that are designed to detect the immediate and strongest biodiversity responses to a threatening process. What few long-term studies are available, although typically focused on single species, report outcomes that often diverge significantly from the short-term species responses. Here, we report the results of an 18-year survey of an amphibian community exposed to both climate warming and the emergence of lethal chytridiomycosis. Our study shows that the impacts of infectious disease are ongoing but restricted to two out of nine species that form the community, despite the fact all species can become infected with the fungus. Climate warming appears to be affecting four out of the nine species, but the response of three of these is an increase in abundance. Our study supports a decreasing role of infectious disease on the community, and an increasing and currently positive effect of climate warming. We caution that if the warming trends continue, the net positive effect will turn negative as amphibian breeding habitat becomes unavailable as water bodies dry, a pattern that already may be underway. © 2018 John Wiley & Sons Ltd.

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

Energy Technology Data Exchange (ETDEWEB)

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

2017-07-26

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

Applications of NASA and NOAA Satellite Observations by NASA's Short-term Prediction Research and Transition (SPoRT) Center in Response to Natural Disasters

Science.gov (United States)

Molthan, Andrew L.; Burks, Jason E.; McGrath, Kevin M.; Jedlovec, Gary J.

2012-01-01

NASA s Short-term Prediction Research and Transition (SPoRT) Center supports the transition of unique NASA and NOAA research activities to the operational weather forecasting community. SPoRT emphasizes real-time analysis and prediction out to 48 hours. SPoRT partners with NOAA s National Weather Service (NWS) Weather Forecast Offices (WFOs) and National Centers to improve current products, demonstrate future satellite capabilities and explore new data assimilation techniques. Recently, the SPoRT Center has been involved in several activities related to disaster response, in collaboration with NOAA s National Weather Service, NASA s Applied Sciences Disasters Program, and other partners.

Climate-vegetation-soil interactions and long-term hydrologic partitioning: signatures of catchment co-evolution

Directory of Open Access Journals (Sweden)

P. A. Troch

2013-06-01

Full Text Available Budyko (1974 postulated that long-term catchment water balance is controlled to first order by the available water and energy. This leads to the interesting question of how do landscape characteristics (soils, geology, vegetation and climate properties (precipitation, potential evaporation, number of wet and dry days interact at the catchment scale to produce such a simple and predictable outcome of hydrological partitioning? Here we use a physically-based hydrologic model separately parameterized in 12 US catchments across a climate gradient to decouple the impact of climate and landscape properties to gain insight into the role of climate-vegetation-soil interactions in long-term hydrologic partitioning. The 12 catchment models (with different paramterizations are subjected to the 12 different climate forcings, resulting in 144 10 yr model simulations. The results are analyzed per catchment (one catchment model subjected to 12 climates and per climate (one climate filtered by 12 different model parameterization, and compared to water balance predictions based on Budyko's hypothesis (E/P = ϕ (Ep/P; E: evaporation, P: precipitation, Ep: potential evaporation. We find significant anti-correlation between average deviations of the evaporation index (E/P computed per catchment vs. per climate, compared to that predicted by Budyko. Catchments that on average produce more E/P have developed in climates that on average produce less E/P, when compared to Budyko's prediction. Water and energy seasonality could not explain these observations, confirming previous results reported by Potter et al. (2005. Next, we analyze which model (i.e., landscape filter characteristics explain the catchment's tendency to produce more or less E/P. We find that the time scale that controls subsurface storage release explains the observed trend. This time scale combines several geomorphologic and hydraulic soil properties. Catchments with relatively longer

Short term memory in echo state networks

OpenAIRE

Jaeger, H.

2001-01-01

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

Simulating infectious disease risk based on climatic drivers: from numerical weather prediction to long term climate change scenario

Science.gov (United States)

Caminade, C.; Ndione, J. A.; Diallo, M.; MacLeod, D.; Faye, O.; Ba, Y.; Dia, I.; Medlock, J. M.; Leach, S.; McIntyre, K. M.; Baylis, M.; Morse, A. P.

2012-04-01

Climate variability is an important component in determining the incidence of a number of diseases with significant health and socioeconomic impacts. In particular, vector born diseases are the most likely to be affected by climate; directly via the development rates and survival of both the pathogen and the vector, and indirectly through changes in the surrounding environmental conditions. Disease risk models of various complexities using different streams of climate forecasts as inputs have been developed within the QWeCI EU and ENHanCE ERA-NET project frameworks. This work will present two application examples, one for Africa and one for Europe. First, we focus on Rift Valley fever over sub-Saharan Africa, a zoonosis that affects domestic animals and humans by causing an acute fever. We show that the Rift Valley fever outbreak that occurred in late 2010 in the northern Sahelian region of Mauritania might have been anticipated ten days in advance using the GFS numerical weather prediction system. Then, an ensemble of regional climate projections is employed to model the climatic suitability of the Asian tiger mosquito for the future over Europe. The Asian tiger mosquito is an invasive species originally from Asia which is able to transmit West Nile and Chikungunya Fever among others. This species has spread worldwide during the last decades, mainly through the shipments of goods from Asia. Different disease models are employed and inter-compared to achieve such a task. Results show that the climatic conditions over southern England, central Western Europe and the Balkans might become more suitable for the mosquito (including the proviso that the mosquito has already been introduced) to establish itself in the future.

Predicting climate-driven regime shifts versus rebound potential in coral reefs.

Science.gov (United States)

Graham, Nicholas A J; Jennings, Simon; MacNeil, M Aaron; Mouillot, David; Wilson, Shaun K

2015-02-05

Climate-induced coral bleaching is among the greatest current threats to coral reefs, causing widespread loss of live coral cover. Conditions under which reefs bounce back from bleaching events or shift from coral to algal dominance are unknown, making it difficult to predict and plan for differing reef responses under climate change. Here we document and predict long-term reef responses to a major climate-induced coral bleaching event that caused unprecedented region-wide mortality of Indo-Pacific corals. Following loss of >90% live coral cover, 12 of 21 reefs recovered towards pre-disturbance live coral states, while nine reefs underwent regime shifts to fleshy macroalgae. Functional diversity of associated reef fish communities shifted substantially following bleaching, returning towards pre-disturbance structure on recovering reefs, while becoming progressively altered on regime shifting reefs. We identified threshold values for a range of factors that accurately predicted ecosystem response to the bleaching event. Recovery was favoured when reefs were structurally complex and in deeper water, when density of juvenile corals and herbivorous fishes was relatively high and when nutrient loads were low. Whether reefs were inside no-take marine reserves had no bearing on ecosystem trajectory. Although conditions governing regime shift or recovery dynamics were diverse, pre-disturbance quantification of simple factors such as structural complexity and water depth accurately predicted ecosystem trajectories. These findings foreshadow the likely divergent but predictable outcomes for reef ecosystems in response to climate change, thus guiding improved management and adaptation.

Long-Term Phenological Shifts in Raptor Migration and Climate

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Jaffré, Mikaël; Beaugrand, Grégory; Goberville, Éric; Jiguet, Frédéric; Kjellén, Nils; Troost, Gerard; Dubois, Philippe J.; Leprêtre, Alain; Luczak, Christophe

2013-01-01

Climate change is having a discernible effect on many biological and ecological processes. Among observed changes, modifications in bird phenology have been widely documented. However, most studies have interpreted phenological shifts as gradual biological adjustments in response to the alteration of the thermal regime. Here we analysed a long-term dataset (1980-2010) of short-distance migratory raptors in five European regions. We revealed that the responses of these birds to climate-induced changes in autumn temperatures are abrupt and synchronous at a continental scale. We found that when the temperatures increased, birds delayed their mean passage date of autumn migration. Such delay, in addition to an earlier spring migration, suggests that a significant warming may induce an extension of the breeding-area residence time of migratory raptors, which may eventually lead to residency. PMID:24223888

Long-term phenological shifts in raptor migration and climate.

Directory of Open Access Journals (Sweden)

Mikaël Jaffré

Full Text Available Climate change is having a discernible effect on many biological and ecological processes. Among observed changes, modifications in bird phenology have been widely documented. However, most studies have interpreted phenological shifts as gradual biological adjustments in response to the alteration of the thermal regime. Here we analysed a long-term dataset (1980-2010 of short-distance migratory raptors in five European regions. We revealed that the responses of these birds to climate-induced changes in autumn temperatures are abrupt and synchronous at a continental scale. We found that when the temperatures increased, birds delayed their mean passage date of autumn migration. Such delay, in addition to an earlier spring migration, suggests that a significant warming may induce an extension of the breeding-area residence time of migratory raptors, which may eventually lead to residency.

Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk

Science.gov (United States)

Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Patel, Bhavika; Heidari, Morteza; Liu, Hong; Zheng, Bin

2018-05-01

This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied ‘as is’ to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUC  =  0.65  ±  0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (p  breast cancer risk.

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

OpenAIRE

Singh, Akash

2017-01-01

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

Short-term prediction of local wind conditions

DEFF Research Database (Denmark)

Landberg, L.

2001-01-01

This paper will describe a system which predicts the expected power output of a number of wind farms. The system is automatic and operates on-line. The paper will quantify the accuracy of the predictions and will also give examples of the performance for specific storm events. An actual...

Short term and medium term power distribution load forecasting by neural networks

International Nuclear Information System (INIS)

Yalcinoz, T.; Eminoglu, U.

2005-01-01

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

Development of Short-term Molecular Thresholds to Predict Long-term Mouse Liver Tumor Outcomes: Phthalate Case StudyTo be

Science.gov (United States)

Molecular Thresholds for Early Key Events in Liver Tumorgensis: PhthalateCase StudyTriangleShort-term changes in molecular profiles are a central component of strategies to model health effects of environmental chemicals such as phthalates, for which there is widespread human exp...

The prediction of the level of personality organization on reduction of psychiatric symptoms and improvement of work ability in short- versus long-term psychotherapies during a 5-year follow-up.

Science.gov (United States)

Knekt, Paul; Lindfors, Olavi; Keinänen, Matti; Heinonen, Erkki; Virtala, Esa; Härkänen, Tommi

2017-09-01

How level of personality organization (LPO) predicts psychiatric symptoms and work ability in short- versus long-term psychotherapies is poorly known. We investigated the importance of the LPO on the benefits of short-term versus long-term psychotherapies. A cohort study based on 326 outpatients with mood or anxiety disorder was allocated to long-term (LPP) and short-term (SPP) psychodynamic psychotherapy, and solution-focused therapy (SFT). The LPO was assessed by interview at baseline and categorized into neuroses and higher level borderline. Outcome was assessed at baseline and 4-9 times during a 5-year follow-up, using self-report and interview-based measures of symptoms and work ability. For patients receiving SPP, improvement in work ability, symptom reduction, and the remission rate were more considerable in patients with neuroses than in higher level borderline patients, whereas LPP or SFT showed no notable differences in effectiveness in the two LPO groups. In patients with neuroses, improvement was more considerable in the short-term therapy groups during the first year of follow-up, and in higher level borderline patients LPP was more effective after 3 years of follow-up. The remission rate, defined as both symptom reduction and lack of auxiliary treatment, was higher in LPP than in SPP for both the LPO groups considered. In neuroses, short-term psychotherapy was associated with a more rapid reduction of symptoms and increase in work ability, whereas LPP was more effective for longer follow-ups in both LPO groups. Further large-scale studies are needed. Level of personality organization is relevant for selection between short- and long-term psychotherapies. Short-term therapy gives faster benefits for neurotic patients but not for patients with higher level borderline personality organization. Sustained remission from symptoms is more probable after long-term than short-term therapy. © 2016 The British Psychological Society.

{sup 18}F-alfatide PET/CT may predict short-term outcome of concurrent chemoradiotherapy in patients with advanced non-small cell lung cancer

Energy Technology Data Exchange (ETDEWEB)

Luan, Xiaohui [Shandong Cancer Hospital affiliated to Shandong University, Department of Radiation Oncology, Jinan, Shandong (China); University of Jinan-Shandong Academy of Medical Sciences, School of Medicine and Life Sciences, Jinan (China); Huang, Yong; Sun, Xiaorong; Ma, Li; Teng, Xuepeng; Lu, Hong [Shandong Cancer Hospital affiliated to Shandong University, Department of Radiology, Jinan, Shandong (China); Gao, Song [Jining Infectious Diseases Hospital, Department of Oncology, Jining, Shandong (China); Wang, Suzhen; Yu, Jinming; Yuan, Shuanghu [Shandong Cancer Hospital affiliated to Shandong University, Department of Radiation Oncology, Jinan, Shandong (China)

2016-12-15

The study aims to investigate the role of {sup 18}F-alfatide positron emission tomography/computed tomography (PET/CT) in predicting the short-term outcome of concurrent chemoradiotherapy (CCRT) in patients with advanced non-small cell lung cancer (NSCLC). Eighteen patients with advanced NSCLC had undergone {sup 18}F-alfatide PET/CT scans before CCRT and PET/CT parameters including maximum and mean standard uptake values (SUV{sub max}/SUV{sub mean}), peak standard uptake values (SUV{sub peak}) and tumor volume (TV{sub PET} and TV{sub CT}) were obtained. The SUV{sub max} of tumor and normal tissues (lung, blood pool and muscle) were measured, and their ratios were denoted as T/NT (T/NT{sub lung}, T/NT{sub blood} and T/NT{sub muscle}). Statistical methods included the Two-example t test, Wilcoxon rank-sum test, Receiver-operating characteristic (ROC) curve analysis and logistic regression analyses. We found that SUV{sub max}, SUV{sub peak}, T/NT{sub lung}, T/NT{sub blood} and T/NT{sub muscle} were higher in non-responders than in responders (P = 0.0024, P = 0.016, P < 0.001, P = 0.003, P = 0.004). According to ROC curve analysis, the thresholds of SUV{sub max}, SUV{sub peak}, T/NT{sub lung}, T/NT{sub blood} and T/NT{sub muscle} were 5.65, 4.46, 7.11, 5.41, and 11.75, respectively. The five parameters had high sensitivity, specificity and accuracy in distinguishing non-responders and responders. Multivariate logistic regression analyses showed that T/NT{sub lung} was an independent predictor of the short-term outcome of CCRT in patients with advanced NSCLC (P = 0.032). {sup 18}F-alfatide PET/CT may be useful in predicting the short-term outcome of CCRT in patients with advanced NSCLC. (orig.)

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

Science.gov (United States)

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

2013-07-01

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

Climate Prediction Center - Monitoring and Data

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Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News monthly data, time series, and maps for various climate parameters, such as precipitation, temperature Oscillations (ENSO) and other climate patterns such as the North Atlantic and Pacific Decadal Oscillations, and

Short Lived Climate Pollutants cause a Long Lived Effect on Sea-level Rise: Analyzing climate metrics for sea-level rise

Science.gov (United States)

Sterner, E.; Johansson, D. J.

2013-12-01

Climate change depends on the increase of several different atmospheric pollutants. While long term global warming will be determined mainly by carbon dioxide, warming in the next few decades will depend to a large extent on short lived climate pollutants (SLCP). Reducing emissions of SLCPs could contribute to lower the global mean surface temperature by 0.5 °C already by 2050 (Shindell et al. 2012). Furthermore, the warming effect of one of the most potent SLCPs, black carbon (BC), may have been underestimated in the past. Bond et al. (2013) presents a new best estimate of the total BC radiative forcing (RF) of 1.1 W/m2 (90 % uncertainty bounds of 0.17 to 2.1 W/m2) since the beginning of the industrial era. BC is however never emitted alone and cooling aerosols from the same sources offset a majority of this RF. In the wake of calls for mitigation of SLCPs it is important to study other aspects of the climate effect of SLCPs. One key impact of climate change is sea-level rise (SLR). In a recent study, the effect of SLCP mitigation scenarios on SLR is examined. Hu et al (2013) find a substantial effect on SLR from mitigating SLCPs sharply, reducing SLR by 22-42% by 2100. We choose a different approach focusing on emission pulses and analyse a metric based on sea level rise so as to further enlighten the SLR consequences of SLCPs. We want in particular to understand the time dynamics of SLR impacts caused by SLCPs compared to other greenhouse gases. The most commonly used physical based metrics are GWP and GTP. We propose and evaluate an additional metric: The global sea-level rise potential (GSP). The GSP is defined as the sea level rise after a time horizon caused by an emissions pulse of a forcer to the sea level rise after a time horizon caused by an emissions pulse of a CO2. GSP is evaluated and compared to GWP and GTP using a set of climate forcers chosen to cover the whole scale of atmospheric perturbation life times (BC, CH4, N2O, CO2 and SF6). The study

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

Science.gov (United States)

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

2017-02-01

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

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

Science.gov (United States)

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

2014-11-01

Experiments were conducted to test a modern exemplar-familiarity model on its ability to account for both short-term and long-term probe recognition within the same memory-search paradigm. Also, making connections to the literature on attention and visual search, the model was used to interpret differences in probe-recognition performance across diverse conditions that manipulated relations between targets and foils across trials. Subjects saw lists of from 1 to 16 items followed by a single item recognition probe. In a varied-mapping condition, targets and foils could switch roles across trials; in a consistent-mapping condition, targets and foils never switched roles; and in an all-new condition, on each trial a completely new set of items formed the memory set. In the varied-mapping and all-new conditions, mean correct response times (RTs) and error proportions were curvilinear increasing functions of memory set size, with the RT results closely resembling ones from hybrid visual-memory search experiments reported by Wolfe (2012). In the consistent-mapping condition, new-probe RTs were invariant with set size, whereas old-probe RTs increased slightly with increasing study-test lag. With appropriate choice of psychologically interpretable free parameters, the model accounted well for the complete set of results. The work provides support for the hypothesis that a common set of processes involving exemplar-based familiarity may govern long-term and short-term probe recognition across wide varieties of memory- search conditions. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Climate Prediction Center - Monitoring and Data Index

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Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News ; Atmospheric Monitoring and Data Monitoring Weather & Climate in Realtime Climate Diagnostics Bulletin Preliminary Climate Diagnostics Bulletin Figures Monthly Atmospheric & Sea Surface Temperature Indices

Long-term climate policy implications of phasing out fossil fuel subsidies

International Nuclear Information System (INIS)

Schwanitz, Valeria Jana; Piontek, Franziska; Bertram, Christoph; Luderer, Gunnar

2014-01-01

It is often argued that fossil fuel subsidies hamper the transition towards a sustainable energy supply as they incentivize wasteful consumption. We assess implications of a subsidy phase-out for the mitigation of climate change and the low-carbon transformation of the energy system, using the global energy–economy model REMIND. We compare our results with those obtained by the International Energy Agency (based on the World Energy Model) and by the Organization for Economic Co-Operation and Development (OECD-Model ENV-Linkages), providing the long-term perspective of an intertemporal optimization model. The results are analyzed in the two dimensions of subsidy phase-out and climate policy scenarios. We confirm short-term benefits of phasing-out fossil fuel subsidies as found in prior studies. However, these benefits are only sustained to a small extent in the long term, if dedicated climate policies are weak or nonexistent. Most remarkably we find that a removal of fossil fuel subsidies, if not complemented by other policies, can slow down a global transition towards a renewable based energy system. The reason is that world market prices for fossil fuels may drop due to a removal of subsidies. Thus, low carbon alternatives would encounter comparative disadvantages. - Highlights: • We assess implications of phasing out fossil fuel subsidies on the mitigation of climate change. • The removal of subsidies leads to a net-reduction in the use of energy. • Emission reductions contribute little to stabilize greenhouse gases at 450 ppm if not combined with climate policies. • Low carbon alternatives may encounter comparative disadvantages due to relative price changes at world markets

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

Directory of Open Access Journals (Sweden)

Qiang Shang

2016-08-01

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

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

Science.gov (United States)

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

2018-06-01

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

Aberrant GSTP1 promoter methylation predicts short-term prognosis in acute-on-chronic hepatitis B liver failure.

Science.gov (United States)

Gao, S; Sun, F-K; Fan, Y-C; Shi, C-H; Zhang, Z-H; Wang, L-Y; Wang, K

2015-08-01

Glutathione-S-transferase P1 (GSTP1) methylation has been demonstrated to be associated with oxidative stress induced liver damage in acute-on-chronic hepatitis B liver failure (ACHBLF). To evaluate the methylation level of GSTP1 promoter in acute-on-chronic hepatitis B liver failure and determine its predictive value for prognosis. One hundred and five patients with acute-on-chronic hepatitis B liver failure, 86 with chronic hepatitis B (CHB) and 30 healthy controls (HC) were retrospectively enrolled. GSTP1 methylation level in peripheral mononuclear cells (PBMC) was detected by MethyLight. Clinical and laboratory parameters were obtained. GSTP1 methylation levels were significantly higher in patients with acute-on-chronic hepatitis B liver failure (median 16.84%, interquartile range 1.83-59.05%) than those with CHB (median 1.25%, interquartile range 0.48-2.47%; P chronic hepatitis B liver failure group, nonsurvivors showed significantly higher GSTP1 methylation levels (P chronic hepatitis B liver failure, GSTP1 methylation showed significantly better predictive value than MELD score [area under the receiver operating characteristic curve (AUC) 0.89 vs. 0.72, P chronic hepatitis B liver failure and shows high predictive value for short-term mortality. It might serve as a potential prognostic marker for acute-on-chronic hepatitis B liver failure. © 2015 John Wiley & Sons Ltd.

Short-term variations of Icelandic ice cap mass inferred from cGPS coordinate time series

DEFF Research Database (Denmark)

Compton, Kathleen; Bennett, Richard A.; Hreinsdóttir, Sigrún

2017-01-01

As the global climate changes, understanding short-term variations in water storage is increasingly important. Continuously operating Global Positioning System (cGPS) stations in Iceland record annual periodic motion—the elastic response to winter accumulation and spring melt seasons—with peak-to...... insulation in response to tephra deposition following volcanic eruptions, processes that are not resolved with once or twice-yearly stake measurements....

Short-term predictions in forex trading

Science.gov (United States)

Muriel, A.

2004-12-01

Using a kinetic equation that is used to model turbulence (Physica A, 1985-1988, Physica D, 2001-2003), we redefine variables to model the time evolution of the foreign exchange rates of three major currencies. We display live and predicted data for one period of trading in October, 2003.

Short-term wind power forecasting: probabilistic and space-time aspects

DEFF Research Database (Denmark)

Tastu, Julija

work deals with the proposal and evaluation of new mathematical models and forecasting methods for short-term wind power forecasting, accounting for space-time dynamics based on geographically distributed information. Different forms of power predictions are considered, starting from traditional point...... into the corresponding models are analysed. As a final step, emphasis is placed on generating space-time trajectories: this calls for the prediction of joint multivariate predictive densities describing wind power generation at a number of distributed locations and for a number of successive lead times. In addition......Optimal integration of wind energy into power systems calls for high quality wind power predictions. State-of-the-art forecasting systems typically provide forecasts for every location individually, without taking into account information coming from the neighbouring territories. It is however...

Short-term memories with a stochastic perturbation

International Nuclear Information System (INIS)

Pontes, Jose C.A. de; Batista, Antonio M.; Viana, Ricardo L.; Lopes, Sergio R.

2005-01-01

We investigate short-term memories in linear and weakly nonlinear coupled map lattices with a periodic external input. We use locally coupled maps to present numerical results about short-term memory formation adding a stochastic perturbation in the maps and in the external input

Prediction of Short- and Medium-term Efficacy of Biosimilar Infliximab Therapy. Do Trough Levels and Antidrug Antibody Levels or Clinical And Biochemical Markers Play the More Important Role?

Science.gov (United States)

Gonczi, Lorant; Vegh, Zsuzsanna; Golovics, Petra Anna; Rutka, Mariann; Gecse, Krisztina Barbara; Bor, Renata; Farkas, Klaudia; Szamosi, Tamás; Bene, László; Gasztonyi, Beáta; Kristóf, Tünde; Lakatos, László; Miheller, Pál; Palatka, Károly; Papp, Mária; Patai, Árpád; Salamon, Ágnes; Tóth, Gábor Tamás; Vincze, Áron; Biro, Edina; Lovasz, Barbara Dorottya; Kurti, Zsuzsanna; Szepes, Zoltan; Molnár, Tamás; Lakatos, Péter L

2017-06-01

Biosimilar infliximab CT-P13 received European Medicines Agency [EMA] approval in June 2013 for all indications of the originator product. In the present study, we aimed to evaluate the predictors of short- and medium-term clinical outcome in patients treated with the biosimilar infliximab at the participating inflammatory bowel disease [IBD] centres in Hungary. Demographic data were collected and a harmonised monitoring strategy was applied. Clinical and biochemical activities were evaluated at Weeks 14, 30, and 54. Trough level [TL] and anti-drug antibody [ADA] concentrations were measured by enzyme-linked immunosorbent assay [ELISA] [LT-005, Theradiag, France] at baseline at 14, 30 and 54 weeks and in two centres at Weeks 2 and 6. A total of 291 consecutive IBD patients (184 Crohn's disease [CD] and 107 ulcerative colitis [UC]) were included. In UC, TLs at Week 2 predicted both clinical response and remission at Weeks 14 and 30 (clinical response/remission at Week 14: area under the curve [AUC] = 0.81, p < 0.001, cut-off: 11.5 μg/ml/AUC = 0.79, p < 0.001, cut-off: 15.3μg/ml; clinical response/remission at Week 30: AUC = 0.79, p = 0.002, cut-off: 11.5 μg/ml/AUC = 0.74, p = 0.006, cut-off: 14.5 μg/ml), whereas ADA positivity at Week 14 was inversely associated with clinical response at Week 30 [58.3% vs 84.8% ,p = 0.04]. Previous anti-tumour necrosis factor [TNF] exposure was inversely associated with short-term clinical remission [Week 2: 18.8% vs 47.8%, p = 0.03, at Week 6: 38.9% vs 69.7%, p = 0.013, at Week 14: 37.5% vs 2.5%, p = 0.06]. In CD, TLs at Week 2 predicted short-term [Week 14 response/remission, AUCTLweek2 = 0.715-0.721, p = 0.05/0.005] but not medium-term clinical efficacy. In addition, early ADA status by Week 14 [p = 0.04-0.05 for Weeks 14 and 30], early clinical response [p < 0.001 for Weeks 30/54] and normal C-reactive protein [CRP] at Week 14 [p = 0.005-0.0001] and previous anti-TNF exposure [p = 0.03-0.0001 for Weeks 14, 30, and 54] were

Using decadal climate prediction to characterize and manage changing drought and flood risks in Colorado

Science.gov (United States)

Lazrus, H.; Done, J.; Morss, R. E.

2017-12-01

A new branch of climate science, known as decadal prediction, seeks to predict the time-varying trajectory of climate over the next 3-30 years and not just the longer-term trends. Decadal predictions bring climate information into the time horizon of decision makers, particularly those tasked with managing water resources and floods whose master planning is often on the timescale of decades. Information from decadal predictions may help alleviate some aspects of vulnerability by helping to inform decisions that reduce drought and flood exposure and increase adaptive capacities including preparedness, response, and recovery. This presentation will highlight an interdisciplinary project - involving atmospheric and social scientists - on the development of decadal climate information and its use in decision making. The presentation will explore the skill and utility of decadal drought and flood prediction along Colorado's Front Range, an area experiencing rapid population growth and uncertain climate variability and climate change impacts. Innovative statistical and dynamical atmospheric modeling techniques explore the extent to which Colorado precipitation can be predicted on decadal scales using remote Pacific Ocean surface temperature patterns. Concurrently, stakeholder interviews with flood managers in Colorado are being used to explore the potential utility of decadal climate information. Combining the modeling results with results from the stakeholder interviews shows that while there is still significant uncertainty surrounding precipitation on decadal time scales, relevant and well communicated decadal information has potential to be useful for drought and flood management.

Short-term memory

Science.gov (United States)

Toulouse, G.

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

Women's fertility across the cycle increases the short-term attractiveness of creative intelligence.

Science.gov (United States)

Haselton, Martie G; Miller, Geoffrey F

2006-03-01

Male provisioning ability may have evolved as a "good dad" indicator through sexual selection, whereas male creativity may have evolved partly as a "good genes" indicator. If so, women near peak fertility (midcycle) should prefer creativity over wealth, especially in short-term mating. Forty-one normally cycling women read vignettes describing creative but poor men vs. uncreative but rich men. Women's estimated fertility predicted their short-term (but not long-term) preference for creativity over wealth, in both their desirability ratings of individual men (r=.40, p<.01) and their forced-choice decisions between men (r=.46, p<.01). These preliminary results are consistent with the view that creativity evolved at least partly as a good genes indicator through mate choice.

Time-Series Prediction: Application to the Short-Term Electric Energy Demand

OpenAIRE

Troncoso Lora, Alicia; Riquelme Santos, Jesús Manuel; Riquelme Santos, José Cristóbal; Gómez Expósito, Antonio; Martínez Ramos, José Luis

2003-01-01

This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed by means of a conventional dynamic regression technique, where the parameters are estimated by solving a least squares problem. Finally, results obtained from the application of both techniques to the Spanish transmission system are compared in terms of maximum, average and ...

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

Science.gov (United States)

Sanderson, David J; Bannerman, David M

2011-04-01

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

Retrieval-Induced Inhibition in Short-Term Memory.

Science.gov (United States)

Kang, Min-Suk; Choi, Joongrul

2015-07-01

We used a visual illusion called motion repulsion as a model system for investigating competition between two mental representations. Subjects were asked to remember two random-dot-motion displays presented in sequence and then to report the motion directions for each. Remembered motion directions were shifted away from the actual motion directions, an effect similar to the motion repulsion observed during perception. More important, the item retrieved second showed greater repulsion than the item retrieved first. This suggests that earlier retrieval exerted greater inhibition on the other item being held in short-term memory. This retrieval-induced motion repulsion could be explained neither by reduced cognitive resources for maintaining short-term memory nor by continued inhibition between short-term memory representations. These results indicate that retrieval of memory representations inhibits other representations in short-term memory. We discuss mechanisms of retrieval-induced inhibition and their implications for the structure of memory. © The Author(s) 2015.

Evaluation of Short Term Memory Span Function In Children

Directory of Open Access Journals (Sweden)

Barış ERGÜL

2016-12-01

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

Distinct electrophysiological indices of maintenance in auditory and visual short-term memory.

Science.gov (United States)

Lefebvre, Christine; Vachon, François; Grimault, Stephan; Thibault, Jennifer; Guimond, Synthia; Peretz, Isabelle; Zatorre, Robert J; Jolicœur, Pierre

2013-11-01

We compared the electrophysiological correlates for the maintenance of non-musical tones sequences in auditory short-term memory (ASTM) to those for the short-term maintenance of sequences of coloured disks held in visual short-term memory (VSTM). The visual stimuli yielded a sustained posterior contralateral negativity (SPCN), suggesting that the maintenance of sequences of coloured stimuli engaged structures similar to those involved in the maintenance of simultaneous visual displays. On the other hand, maintenance of acoustic sequences produced a sustained negativity at fronto-central sites. This component is named the Sustained Anterior Negativity (SAN). The amplitude of the SAN increased with increasing load in ASTM and predicted individual differences in the performance. There was no SAN in a control condition with the same auditory stimuli but no memory task, nor one associated with visual memory. These results suggest that the SAN is an index of brain activity related to the maintenance of representations in ASTM that is distinct from the maintenance of representations in VSTM. © 2013 Elsevier Ltd. All rights reserved.

Exploring the applicability of future air quality predictions based on synoptic system forecasts

International Nuclear Information System (INIS)

Yuval; Broday, David M.; Alpert, Pinhas

2012-01-01

For a given emissions inventory, the general levels of air pollutants and the spatial distribution of their concentrations are determined by the physiochemical state of the atmosphere. Apart from the trivial seasonal and daily cycles, most of the variability is associated with the atmospheric synoptic scale. A simple methodology for assessing future levels of air pollutants' concentrations based on synoptic forecasts is presented. At short time scales the methodology is comparable and slightly better than persistence and seasonal forecasts at categorical classification of pollution levels. It's utility is shown for air quality studies at the long time scale of a changing climate scenario, where seasonality and persistence cannot be used. It is demonstrated that the air quality variability due to changes in the pollution emissions can be expected to be much larger than that associated with the effects of climatic changes. - Highlights: ► A method for short and long term air quality forecasts is introduced. ► The method is based on prediction of synoptic systems. ► The method beats simple benchmarks in short term forecasts. ► Assessment of future air pollution in a changing climate scenario is demonstrated. - Air quality in a changing climate scenario can be studied using air pollution predictions based on synoptic system forecasts.

Effect of acoustic similarity on short-term auditory memory in the monkey.

Science.gov (United States)

Scott, Brian H; Mishkin, Mortimer; Yin, Pingbo

2013-04-01

Recent evidence suggests that the monkey's short-term memory in audition depends on a passively retained sensory trace as opposed to a trace reactivated from long-term memory for use in working memory. Reliance on a passive sensory trace could render memory particularly susceptible to confusion between sounds that are similar in some acoustic dimension. If so, then in delayed matching-to-sample, the monkey's performance should be predicted by the similarity in the salient acoustic dimension between the sample and subsequent test stimulus, even at very short delays. To test this prediction and isolate the acoustic features relevant to short-term memory, we examined the pattern of errors made by two rhesus monkeys performing a serial, auditory delayed match-to-sample task with interstimulus intervals of 1 s. The analysis revealed that false-alarm errors did indeed result from similarity-based confusion between the sample and the subsequent nonmatch stimuli. Manipulation of the stimuli showed that removal of spectral cues was more disruptive to matching behavior than removal of temporal cues. In addition, the effect of acoustic similarity on false-alarm response was stronger at the first nonmatch stimulus than at the second one. This pattern of errors would be expected if the first nonmatch stimulus overwrote the sample's trace, and suggests that the passively retained trace is not only vulnerable to similarity-based confusion but is also highly susceptible to overwriting. Copyright © 2013 Elsevier B.V. All rights reserved.

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

Science.gov (United States)

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

2018-05-01

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

Evaluation of Short Term Memory Span Function In Children

OpenAIRE

Barış ERGÜL; Arzu ALTIN YAVUZ; Ebru GÜNDOĞAN AŞIK

2016-01-01

Although details of the information encoded in the short-term memory where it is stored temporarily be recorded in the working memory in the next stage. Repeating the information mentally makes it remain in memory for a long time. Studies investigating the relationship between short-term memory and reading skills that are carried out to examine the relationship between short-term memory processes and reading comprehension. In this study information coming to short-term memory and the factors ...

Weighted integration of short-term memory and sensory signals in the oculomotor system.

Science.gov (United States)

Deravet, Nicolas; Blohm, Gunnar; de Xivry, Jean-Jacques Orban; Lefèvre, Philippe

2018-05-01

Oculomotor behaviors integrate sensory and prior information to overcome sensory-motor delays and noise. After much debate about this process, reliability-based integration has recently been proposed and several models of smooth pursuit now include recurrent Bayesian integration or Kalman filtering. However, there is a lack of behavioral evidence in humans supporting these theoretical predictions. Here, we independently manipulated the reliability of visual and prior information in a smooth pursuit task. Our results show that both smooth pursuit eye velocity and catch-up saccade amplitude were modulated by visual and prior information reliability. We interpret these findings as the continuous reliability-based integration of a short-term memory of target motion with visual information, which support modeling work. Furthermore, we suggest that saccadic and pursuit systems share this short-term memory. We propose that this short-term memory of target motion is quickly built and continuously updated, and constitutes a general building block present in all sensorimotor systems.

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

Science.gov (United States)

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

2016-09-01

We investigated the capacity of visual short-term memory (VSTM) in a phase discrimination task that required judgments about the configural relations between pairs of black and white features. Sewell et al. (2014) previously showed that VSTM capacity in an orientation discrimination task was well described by a sample-size model, which views VSTM as a resource comprised of a finite number of noisy stimulus samples. The model predicts the invariance of [Formula: see text] , the sum of squared sensitivities across items, for displays of different sizes. For phase discrimination, the set-size effect significantly exceeded that predicted by the sample-size model for both simultaneously and sequentially presented stimuli. Instead, the set-size effect and the serial position curves with sequential presentation were predicted by an attention-weighted version of the sample-size model, which assumes that one of the items in the display captures attention and receives a disproportionate share of resources. The choice probabilities and response time distributions from the task were well described by a diffusion decision model in which the drift rates embodied the assumptions of the attention-weighted sample-size model. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Adaptation to climate change. Key terms

International Nuclear Information System (INIS)

Levina, E.; Tirpak, D.

2006-05-01

Adaptation has become an important issue in international and domestic discussions on climate change. Numerous terms and concepts have come into common usage as a result of IPCC reports, discussions in the context of the UNFCCC and dialogs by the climate community at large. This paper examines the key adaptation terms and concepts used by the climate change community and other institutions. Conflicts and contradictions are noted with the aim of sensitizing different bodies to the differences, but particularly the Parties to the Convention and experts participating in the IPCC. Given the need to promote a common understanding among various stakeholders and the potential financial implications of various definitions, it appears important for the IPCC and the UNFCCC to work toward common definitions, at least for a core set of terms and concepts

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Short-term predictability of crude oil markets: A detrended fluctuation analysis approach

International Nuclear Information System (INIS)

Alvarez-Ramirez, Jose; Alvarez, Jesus; Rodriguez, Eduardo

2008-01-01

This paper analyzes the auto-correlations of international crude oil prices on the basis of the estimation of the Hurst exponent dynamics for returns over the period from 1987 to 2007. In doing so, a model-free statistical approach - detrended fluctuation analysis - that reduces the effects of non-stationary market trends and focuses on the intrinsic auto-correlation structure of market fluctuations over different time horizons, is used. Tests for time variations of the Hurst exponent indicate that over long horizons the crude oil market is consistent with the efficient market hypothesis. However, meaningful auto-correlations cannot be excluded for time horizons smaller than one month where the Hurst exponent manifests cyclic, non-periodic dynamics. This means that the market exhibits a time-varying short-term inefficient behavior that becomes efficient in the long term. The proposed methodology and its findings are put in perspective with previous studies and results. (author)

On the relationship between short- and long-term memory

DEFF Research Database (Denmark)

Sørensen, Thomas Alrik

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

Doppler Radar and Analysis for Climate Model Verification and Numerical Weather Prediction

National Research Council Canada - National Science Library

Xu, Qin

1998-01-01

... (Qiu and Xu, 1996, Mon. Wea. Rev., 1132-1144). The LS method was further upgraded to including background wind fields and used to improve the initial condition for the ARPS model's short-term prediction...

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

Science.gov (United States)

Norris, Dennis

2017-09-01

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

Fast Weight Long Short-Term Memory

OpenAIRE

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

2018-01-01

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

Landscape genomic prediction for restoration of a Eucalyptus foundation species under climate change.

Science.gov (United States)

Supple, Megan Ann; Bragg, Jason G; Broadhurst, Linda M; Nicotra, Adrienne B; Byrne, Margaret; Andrew, Rose L; Widdup, Abigail; Aitken, Nicola C; Borevitz, Justin O

2018-04-24

As species face rapid environmental change, we can build resilient populations through restoration projects that incorporate predicted future climates into seed sourcing decisions. Eucalyptus melliodora is a foundation species of a critically endangered community in Australia that is a target for restoration. We examined genomic and phenotypic variation to make empirical based recommendations for seed sourcing. We examined isolation by distance and isolation by environment, determining high levels of gene flow extending for 500 km and correlations with climate and soil variables. Growth experiments revealed extensive phenotypic variation both within and among sampling sites, but no site-specific differentiation in phenotypic plasticity. Model predictions suggest that seed can be sourced broadly across the landscape, providing ample diversity for adaptation to environmental change. Application of our landscape genomic model to E. melliodora restoration projects can identify genomic variation suitable for predicted future climates, thereby increasing the long term probability of successful restoration. © 2018, Supple et al.

Plants, birds and butterflies: short-term responses of species communities to climate warming vary by taxon and with altitude.

Science.gov (United States)

Roth, Tobias; Plattner, Matthias; Amrhein, Valentin

2014-01-01

As a consequence of climate warming, species usually shift their distribution towards higher latitudes or altitudes. Yet, it is unclear how different taxonomic groups may respond to climate warming over larger altitudinal ranges. Here, we used data from the national biodiversity monitoring program of Switzerland, collected over an altitudinal range of 2500 m. Within the short period of eight years (2003-2010), we found significant shifts in communities of vascular plants, butterflies and birds. At low altitudes, communities of all species groups changed towards warm-dwelling species, corresponding to an average uphill shift of 8 m, 38 m and 42 m in plant, butterfly and bird communities, respectively. However, rates of community changes decreased with altitude in plants and butterflies, while bird communities changed towards warm-dwelling species at all altitudes. We found no decrease in community variation with respect to temperature niches of species, suggesting that climate warming has not led to more homogenous communities. The different community changes depending on altitude could not be explained by different changes of air temperatures, since during the 16 years between 1995 and 2010, summer temperatures in Switzerland rose by about 0.07°C per year at all altitudes. We discuss that land-use changes or increased disturbances may have prevented alpine plant and butterfly communities from changing towards warm-dwelling species. However, the findings are also consistent with the hypothesis that unlike birds, many alpine plant species in a warming climate could find suitable habitats within just a few metres, due to the highly varied surface of alpine landscapes. Our results may thus support the idea that for plants and butterflies and on a short temporal scale, alpine landscapes are safer places than lowlands in a warming world.

Climate change and predicting soil loss from rainfall

Science.gov (United States)

Kinnell, Peter

2017-04-01

Conceptually, rainfall has a certain capacity to cause soil loss from an eroding area while soil surfaces have a certain resistance to being eroded by rainfall. The terms "rainfall erosivity' and "soil erodibility" are frequently used to encapsulate the concept and in the Revised Universal Soil Loss Equation (RUSLE), the most widely used soil loss prediction equation in the world, average annual values of the R "erosivity" factor and the K "erodibility" factor provide a basis for accounting for variation in rainfall erosion associated with geographic variations of climate and soils. In many applications of RUSLE, R and K are considered to be independent but in reality they are not. In RUSLE2, provision has been made to take account of the fact that K values determined using soil physical factors have to be adjusted for variations in climate because runoff is not directly included as a factor in determining R. Also, the USLE event erosivity index EI30 is better related to accounting for event sediment concentration than event soil loss. While the USLE-M, a modification of the USLE which includes runoff as a factor in determining the event erosivity index provides better estimates of event soil loss when event runoff is known, runoff prediction provides a challenge to modelling event soil loss as climate changes

Does the stress response predict the ability of wild birds to adjust to short-term captivity? A study of the rock pigeon (Columbia livia).

Science.gov (United States)

Angelier, Frédéric; Parenteau, Charline; Trouvé, Colette; Angelier, Nicole

2016-12-01

Although the transfer of wild animals to captivity is crucial for conservation purposes, this process is often challenging because some species or individuals do not adjust well to captive conditions. Chronic stress has been identified as a major concern for animals held on long-term captivity. Surprisingly, the first hours or days of captivity have been relatively overlooked. However, they are certainly very stressful, because individuals are being transferred to a totally novel and confined environment. To ensure the success of conservation programmes, it appears crucial to better understand the proximate causes of interspecific and interindividual variability in the sensitivity to these first hours of captivity. In that respect, the study of stress hormones is relevant, because the hormonal stress response may help to assess whether specific individuals or species adjust, or not, to such captive conditions ('the stress response-adjustment to captivity hypothesis'). We tested this hypothesis in rock pigeons by measuring their corticosterone stress response and their ability to adjust to short-term captivity (body mass loss and circulating corticosterone levels after a day of captivity). We showed that an increased corticosterone stress response is associated with a lower ability to adjust to short-term captivity (i.e. higher body mass loss and circulating corticosterone levels). Our study suggests, therefore, that a low physiological sensitivity to stress may be beneficial for adjusting to captivity. Future studies should now explore whether the stress response can be useful to predict the ability of individuals from different populations or species to not only adjust to short-term but also long-term captivity.

Predicting phenology by integrating ecology, evolution and climate science

Science.gov (United States)

Pau, Stephanie; Wolkovich, Elizabeth M.; Cook, Benjamin I.; Davies, T. Jonathan; Kraft, Nathan J.B.; Bolmgren, Kjell; Betancourt, Julio L.; Cleland, Elsa E.

2011-01-01

Forecasting how species and ecosystems will respond to climate change has been a major aim of ecology in recent years. Much of this research has focused on phenology — the timing of life-history events. Phenology has well-demonstrated links to climate, from genetic to landscape scales; yet our ability to explain and predict variation in phenology across species, habitats and time remains poor. Here, we outline how merging approaches from ecology, climate science and evolutionary biology can advance research on phenological responses to climate variability. Using insight into seasonal and interannual climate variability combined with niche theory and community phylogenetics, we develop a predictive approach for species' reponses to changing climate. Our approach predicts that species occupying higher latitudes or the early growing season should be most sensitive to climate and have the most phylogenetically conserved phenologies. We further predict that temperate species will respond to climate change by shifting in time, while tropical species will respond by shifting space, or by evolving. Although we focus here on plant phenology, our approach is broadly applicable to ecological research of plant responses to climate variability.

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

Science.gov (United States)

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

2018-04-01

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

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

International Nuclear Information System (INIS)

Wolter, H.

1990-01-01

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

Global predictability of temperature extremes

Science.gov (United States)

Coughlan de Perez, Erin; van Aalst, Maarten; Bischiniotis, Konstantinos; Mason, Simon; Nissan, Hannah; Pappenberger, Florian; Stephens, Elisabeth; Zsoter, Ervin; van den Hurk, Bart

2018-05-01

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world’s population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations.

Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm

Directory of Open Access Journals (Sweden)

Jianzhou Wang

2015-01-01

Full Text Available This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP and optimized support vector regression (SVR. Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA, particle swarm optimization algorithm (PSO, and cuckoo optimization algorithm (COA. Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1 analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2 the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3 the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.

An ensemble prediction approach to weekly Dengue cases forecasting based on climatic and terrain conditions

Directory of Open Access Journals (Sweden)

Sougata Deb

2017-11-01

Full Text Available Introduction: Dengue fever has been one of the most concerning endemic diseases of recent times. Every year, 50-100 million people get infected by the dengue virus across the world. Historically, it has been most prevalent in Southeast Asia and the Pacific Islands. In recent years, frequent dengue epidemics have started occurring in Latin America as well. This study focused on assessing the impact of different short and long-term lagged climatic predictors on dengue cases. Additionally, it assessed the impact of building an ensemble model using multiple time series and regression models, in improving prediction accuracy. Materials and Methods: Experimental data were based on two Latin American cities, viz. San Juan (Puerto Rico and Iquitos (Peru. Due to weather and geographic differences, San Juan recorded higher dengue incidences than Iquitos. Using lagged cross-correlations, this study confirmed the impact of temperature and vegetation on the number of dengue cases for both cities, though in varied degrees and time lags. An ensemble of multiple predictive models using an elaborate set of derived predictors was built and validated. Results: The proposed ensemble prediction achieved a mean absolute error of 21.55, 4.26 points lower than the 25.81 obtained by a standard negative binomial model. Changes in climatic conditions and urbanization were found to be strong predictors as established empirically in other researches. Some of the predictors were new and informative, which have not been explored in any other relevant studies yet. Discussion and Conclusions: Two original contributions were made in this research. Firstly, a focused and extensive feature engineering aligned with the mosquito lifecycle. Secondly, a novel covariate pattern-matching based prediction approach using past time series trend of the predictor variables. Increased accuracy of the proposed model over the benchmark model proved the appropriateness of the analytical approach

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

Directory of Open Access Journals (Sweden)

Juan Du

2018-05-01

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

Toward Improving Predictability of Extreme Hydrometeorological Events: the Use of Multi-scale Climate Modeling in the Northern High Plains

Science.gov (United States)

Munoz-Arriola, F.; Torres-Alavez, J.; Mohamad Abadi, A.; Walko, R. L.

2014-12-01

Our goal is to investigate possible sources of predictability of hydrometeorological extreme events in the Northern High Plains. Hydrometeorological extreme events are considered the most costly natural phenomena. Water deficits and surpluses highlight how the water-climate interdependence becomes crucial in areas where single activities drive economies such as Agriculture in the NHP. Nonetheless we recognize the Water-Climate interdependence and the regulatory role that human activities play, we still grapple to identify what sources of predictability could be added to flood and drought forecasts. To identify the benefit of multi-scale climate modeling and the role of initial conditions on flood and drought predictability on the NHP, we use the Ocean Land Atmospheric Model (OLAM). OLAM is characterized by a dynamic core with a global geodesic grid with hexagonal (and variably refined) mesh cells and a finite volume discretization of the full compressible Navier Stokes equations, a cut-grid cell method for topography (that reduces error in computational gradient computation and anomalous vertical dispersion). Our hypothesis is that wet conditions will drive OLAM's simulations of precipitation to wetter conditions affecting both flood forecast and drought forecast. To test this hypothesis we simulate precipitation during identified historical flood events followed by drought events in the NHP (i.e. 2011-2012 years). We initialized OLAM with CFS-data 1-10 days previous to a flooding event (as initial conditions) to explore (1) short-term and high-resolution and (2) long-term and coarse-resolution simulations of flood and drought events, respectively. While floods are assessed during a maximum of 15-days refined-mesh simulations, drought is evaluated during the following 15 months. Simulated precipitation will be compared with the Sub-continental Observation Dataset, a gridded 1/16th degree resolution data obtained from climatological stations in Canada, US, and

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

OpenAIRE

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

2009-01-01

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

Social evolution and genetic interactions in the short and long term.

Science.gov (United States)

Van Cleve, Jeremy

2015-08-01

The evolution of social traits remains one of the most fascinating and feisty topics in evolutionary biology even after half a century of theoretical research. W.D. Hamilton shaped much of the field initially with his 1964 papers that laid out the foundation for understanding the effect of genetic relatedness on the evolution of social behavior. Early theoretical investigations revealed two critical assumptions required for Hamilton's rule to hold in dynamical models: weak selection and additive genetic interactions. However, only recently have analytical approaches from population genetics and evolutionary game theory developed sufficiently so that social evolution can be studied under the joint action of selection, mutation, and genetic drift. We review how these approaches suggest two timescales for evolution under weak mutation: (i) a short-term timescale where evolution occurs between a finite set of alleles, and (ii) a long-term timescale where a continuum of alleles are possible and populations evolve continuously from one monomorphic trait to another. We show how Hamilton's rule emerges from the short-term analysis under additivity and how non-additive genetic interactions can be accounted for more generally. This short-term approach reproduces, synthesizes, and generalizes many previous results including the one-third law from evolutionary game theory and risk dominance from economic game theory. Using the long-term approach, we illustrate how trait evolution can be described with a diffusion equation that is a stochastic analogue of the canonical equation of adaptive dynamics. Peaks in the stationary distribution of the diffusion capture classic notions of convergence stability from evolutionary game theory and generally depend on the additive genetic interactions inherent in Hamilton's rule. Surprisingly, the peaks of the long-term stationary distribution can predict the effects of simple kinds of non-additive interactions. Additionally, the peaks

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

Energy Technology Data Exchange (ETDEWEB)

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

1999-03-01

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

Operative factors associated with short-term outcome in horses with large colon volvulus: 47 cases from 2006 to 2013.

Science.gov (United States)

Gonzalez, L M; Fogle, C A; Baker, W T; Hughes, F E; Law, J M; Motsinger-Reif, A A; Blikslager, A T

2015-05-01

There is an important need for objective parameters that accurately predict the outcome of horses with large colon volvulus. To evaluate the predictive value of a series of histomorphometric parameters on short-term outcome, as well as the impact of colonic resection on horses with large colon volvulus. Retrospective cohort study. Adult horses admitted to the Equine and Farm Animal Veterinary Center at North Carolina State University, Peterson and Smith and Chino Valley Equine Hospitals between 2006 and 2013 that underwent an exploratory coeliotomy, diagnosed with large colon volvulus of ≥360 degrees, where a pelvic flexure biopsy was obtained, and that recovered from general anaesthesia, were selected for inclusion in the study. Logistic regression was used to determine associations between signalment, histomorphometric measurements of interstitium-to-crypt ratio, degree of haemorrhage, percentage loss of luminal and glandular epithelium, as well as colonic resection with short-term outcome (discharge from the hospital). Pelvic flexure biopsies from 47 horses with large colon volvulus were evaluated. Factors that were significantly associated with short-term outcome on univariate logistic regression were Thoroughbred breed (P = 0.04), interstitium-to-crypt ratio >1 (P = 0.02) and haemorrhage score ≥3 (P = 0.005). Resection (P = 0.92) was not found to be associated significantly with short-term outcome. No combined factors increased the likelihood of death in forward stepwise logistic regression modelling. A digitally quantified measurement of haemorrhage area strengthened the association of haemorrhage with nonsurvival in cases of large colon volvulus. Histomorphometric measurements of interstitium-to-crypt ratio and degree of haemorrhage predict short-term outcome in cases of large colon volvulus. Resection was not associated with short-term outcome in horses selected for this study. Accurate quantification of mucosal haemorrhage at the time of surgery may

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

Directory of Open Access Journals (Sweden)

Peter K. Jonason

2013-12-01

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

Short-term energy outlook, annual supplement 1994

International Nuclear Information System (INIS)

1994-08-01

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

Short-term energy outlook annual supplement, 1993

Energy Technology Data Exchange (ETDEWEB)

NONE

1993-08-06

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

Vehicle emissions of short-lived and long-lived climate forcers: trends and tradeoffs.

Science.gov (United States)

Edwards, Morgan R; Klemun, Magdalena M; Kim, Hyung Chul; Wallington, Timothy J; Winkler, Sandra L; Tamor, Michael A; Trancik, Jessika E

2017-08-24

Evaluating technology options to mitigate the climate impacts of road transportation can be challenging, particularly when they involve a tradeoff between long-lived emissions (e.g., carbon dioxide) and short-lived emissions (e.g., methane or black carbon). Here we present trends in short- and long-lived emissions for light- and heavy-duty transport globally and in the U.S., EU, and China over the period 2000-2030, and we discuss past and future changes to vehicle technologies to reduce these emissions. We model the tradeoffs between short- and long-lived emission reductions across a range of technology options, life cycle emission intensities, and equivalency metrics. While short-lived vehicle emissions have decreased globally over the past two decades, significant reductions in CO 2 will be required by mid-century to meet climate change mitigation targets. This is true regardless of the time horizon used to compare long- and short-lived emissions. The short-lived emission intensities of some low-CO 2 technologies are higher than others, and thus their suitability for meeting climate targets depends sensitively on the evaluation time horizon. Other technologies offer low intensities of both short-lived emissions and CO 2 .

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

Science.gov (United States)

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

2015-04-01

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

Parametric decadal climate forecast recalibration (DeFoReSt 1.0

Directory of Open Access Journals (Sweden)

A. Pasternack

2018-01-01

Full Text Available Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt, a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS. Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

Science.gov (United States)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

Familiarity speeds up visual short-term memory consolidation.

Science.gov (United States)

Xie, Weizhen; Zhang, Weiwei

2017-06-01

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).

In Search of Decay in Verbal Short-Term Memory

Science.gov (United States)

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

2009-01-01

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

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

Science.gov (United States)

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

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

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

Science.gov (United States)

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

2012-01-01

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

Improving Transit Predictions of Known Exoplanets with TERMS

Directory of Open Access Journals (Sweden)

Mahadevan S.

2011-02-01

Full Text Available Transiting planet discoveries have largely been restricted to the short-period or low-periastron distance regimes due to the bias inherent in the geometric transit probability. Through the refinement of planetary orbital parameters, and hence reducing the size of transit windows, long-period planets become feasible targets for photometric follow-up. Here we describe the TERMS project that is monitoring these host stars at predicted transit times.

Economics of solar energy: Short term costing

Science.gov (United States)

Klee, H.

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

Climate Change as a Predictable Surprise

International Nuclear Information System (INIS)

Bazerman, M.H.

2006-01-01

In this article, I analyze climate change as a 'predictable surprise', an event that leads an organization or nation to react with surprise, despite the fact that the information necessary to anticipate the event and its consequences was available (Bazerman and Watkins, 2004). I then assess the cognitive, organizational, and political reasons why society fails to implement wise strategies to prevent predictable surprises generally and climate change specifically. Finally, I conclude with an outline of a set of response strategies to overcome barriers to change

Life history and spatial traits predict extinction risk due to climate change

Science.gov (United States)

Pearson, Richard G.; Stanton, Jessica C.; Shoemaker, Kevin T.; Aiello-Lammens, Matthew E.; Ersts, Peter J.; Horning, Ned; Fordham, Damien A.; Raxworthy, Christopher J.; Ryu, Hae Yeong; McNees, Jason; Akçakaya, H. Reşit

2014-03-01

There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate. Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change based on the expectation that established assessments such as the IUCN Red List need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened, no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally different from other threats in terms of assessing extinction risks.

Climate Prediction Center - Global Tropical Hazards Assessment

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Organization Search Go Search the CPC Go Climate Outlooks Climate & Weather Link El Niño/La Niña MJO Teleconnections AO NAO PNA AAO Blocking Storm Tracks Climate Glossary Outreach About Us Our Mission Who We Are

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

NARCIS (Netherlands)

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

1997-01-01

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

A novel economy reflecting short-term load forecasting approach

International Nuclear Information System (INIS)

Lin, Cheng-Ting; Chou, Li-Der

2013-01-01

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

Short-Term Reciprocity in Late Parent-Child Relationships

Science.gov (United States)

Leopold, Thomas; Raab, Marcel

2011-01-01

Long-term concepts of parent-child reciprocity assume that the amount of support given and received is only balanced in a generalized fashion over the life course. We argue that reciprocity in parent-child relationships also operates in the short term. Our analysis of short-term reciprocity focuses on concurrent exchange in its main upward and…

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

OpenAIRE

Logvinova, Veronika

2015-01-01

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

Long-term wave climate at DanWEC

DEFF Research Database (Denmark)

Tetu, Amélie; Kofoed, Jens Peter

andthe current network of sensors are also presented. The numerical model used toobtain the 35 years hindcast data is introduced together with its validation againstbuoy-measured data and with the description of the dataset utilised for thelong-term climate definition. The recommendations from [IEC 62600...... buoys are continuously recorded and the data is analysed on a quarterly basis. The directional wave measuring buoys were first installed in March 2015. As two years is not sufficient for long-term wave climate definition, modelled data was more appropriate for the task. Thelong-term wave climate around...... Hanstholm is defined in the present report using the hindcast data from the MIKE 21 Spectral Wave model provided by DHI, one of the partners of the project. Before the actual wave climate definition, a description of the site includinglocation and bathymetry is included. The historical wave data of the area...

Long-term climate change effects on dynamics of microorganisms and carbon in the root-zone

DEFF Research Database (Denmark)

Reinsch, Sabine

Climate change factors such as elevated CO2 concentration, warming and changes in precipitation patterns have been shown to affect terrestrial carbon (C) cycling. The objective of this Ph.D. project is to track recently assimilated C into belowground compartments to investigate the effects...... of climate change on belowground C allocation. The impacts of climate change as single and combined treatments were applied to heath/grassland vegetation and the short-term terrestrial C turnover was investigated using in-situ 13CO2 pulse-labeling. We developed a mobile and low-cost pulse-labeling setup...... have major impacts on the C balance under changing climatic conditions. A comparison of C allocation under ambient and simulated future climatic conditions showed that the terrestrial C balance might be changed by reducing soil organic matter mineralization. Our results suggest that the impact...

Decay uncovered in nonverbal short-term memory.

Science.gov (United States)

Mercer, Tom; McKeown, Denis

2014-02-01

Decay theory posits that memory traces gradually fade away over the passage of time unless they are actively rehearsed. Much recent work exploring verbal short-term memory has challenged this theory, but there does appear to be evidence for trace decay in nonverbal auditory short-term memory. Numerous discrimination studies have reported a performance decline as the interval separating two tones is increased, consistent with a decay process. However, most of this tone comparison research can be explained in other ways, without reference to decay, and these alternative accounts were tested in the present study. In Experiment 1, signals were employed toward the end of extended retention intervals to ensure that listeners were alert to the presence and frequency content of the memoranda. In Experiment 2, a mask stimulus was employed in an attempt to distinguish between a highly detailed sensory trace and a longer-lasting short-term memory, and the distinctiveness of the stimuli was varied. Despite these precautions, slow-acting trace decay was observed. It therefore appears that the mere passage of time can lead to forgetting in some forms of short-term memory.

Short-term energy outlook annual supplement, 1993

International Nuclear Information System (INIS)

1993-01-01

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the third quarter of 1993 through the fourth quarter of 1994. Values for the second quarter of 1993, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding

NASA's Earth Observing System: The Transition from Climate Monitoring to Climate Change Prediction

Science.gov (United States)

King, Michael D.; Herring, David D.

1998-01-01

Earth's 4.5 billion year history is a study in change. Natural geological forces have been rearranging the surface features and climatic conditions of our planet since its beginning. There is scientific evidence that some of these natural changes have not only led to mass extinctions of species (e.g., dinosaurs), but have also severely impacted human civilizations. For instance, there is evidence that a relatively sudden climate change caused a 300-year drought that contributed to the downfall of Akkadia, one of the most powerful empires in the Middle-East region around 2200 BC. More recently, the "little ice age" from 1200-1400 AD forced the Vikings to abandon Greenland when temperatures there dropped by about 1.5 C, rendering it too difficult to grow enough crops to sustain the population. Today, there is compelling scientific evidence that human activities have attained the magnitude of a geological force and are speeding up the rate of global change. For example, carbon dioxide levels have risen 30 percent since the industrial revolution and about 40 percent of the world's land surface has been transformed by humans. We don't understand the cause-and-effect relationships among Earth's land, ocean, and atmosphere well enough to predict what, if any, impacts these rapid changes will have on future climate conditions. We need to make many measurements all over the world, over a long period of time, in order to assemble the information needed to construct accurate computer models that will enable us to forecast climate change. In 1988, the Earth System Sciences Committee, sponsored by NASA, issued a report calling for an integrated, long-term strategy for measuring the vital signs of Earth's climate system. The report urged that the measurements must all be intimately coupled with focused process studies, they must facilitate development of Earth system models, and they must be stored in an information system that ensures open access to consistent, long-term data

Parent-Offspring Conflict over Short-Term Mating Strategies

Directory of Open Access Journals (Sweden)

Spyroulla Georgiou

2011-12-01

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

Climate modelling, uncertainty and responses to predictions of change

International Nuclear Information System (INIS)

Henderson-Sellers, A.

1996-01-01

Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

Role of self-efficacy and social support in short-term recovery after total hip replacement: a prospective cohort study.

Science.gov (United States)

Brembo, Espen Andreas; Kapstad, Heidi; Van Dulmen, Sandra; Eide, Hilde

2017-04-11

Despite the overall success of total hip replacement (THR) in patients with symptomatic osteoarthritis (OA), up to one-quarter of patients report suboptimal recovery. The aim of this study was to determine whether social support and general self-efficacy predict variability in short-term recovery in a Norwegian cohort. We performed secondary analysis of a prospective multicenter study of 223 patients who underwent THR for OA in 2003-2004. The total score of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at 3 months after surgery was used as the recovery variable. We measured self-efficacy using the General Self-Efficacy Scale (GSES) and social support with the Social Provisions Scale (SPS). Preoperative and postoperative scores were compared using Wilcoxon tests. The Mann-Whitney U test compared scores between groups that differed in gender and age. Spearman's rho correlation coefficients were used to evaluate associations between selected predictor variables and the recovery variable. We performed univariate and multiple linear regression analyses to identify independent variables and their ability to predict short-term recovery after THR. The median preoperative WOMAC score was 58.3 before and 23.9 after surgery. The mean absolute change was 31.9 (standard deviation [SD] 17.0) and the mean relative change was 54.8% (SD 26.6). Older age, female gender, higher educational level, number of comorbidities, baseline WOMAC score, self-efficacy, and three of six individual provisions correlated significantly with short-term recovery after THR and predicted the variability in recovery in the univariate regression model. In multiple regression models, baseline WOMAC was the most consistent predictor of short-term recovery: a higher preoperative WOMAC score predicted worse short-term recovery (β = 0.44 [0.29, 0.59]). Higher self-efficacy predicted better recovery (β = -0.44 [-0.87, -0.02]). Reliable alliance was a significant predictor

Using Forecasting to Predict Long-Term Resource Utilization for Web Services

Science.gov (United States)

Yoas, Daniel W.

2013-01-01

Researchers have spent years understanding resource utilization to improve scheduling, load balancing, and system management through short-term prediction of resource utilization. Early research focused primarily on single operating systems; later, interest shifted to distributed systems and, finally, into web services. In each case researchers…

Long-term trends of heat stress and energy use implications in subtropical climates

Energy Technology Data Exchange (ETDEWEB)

Lam, Joseph C.; Wan, Kevin K.W.; Wong, S.L.; Lam, Tony N.T. [Building Energy Research Group, Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR (China)

2010-02-15

Past and future trends of human comfort in terms of heat and cold stresses under the local subtropical climates using measured meteorological data as well as predictions from general climate models were investigated. Summer discomfort showed an increasing trend (and winter discomfort a decreasing trend) over the past 41 years from 1968 to 2008. Monthly mean minimum and maximum temperatures and moisture content predictions from a general climate model (MIROC3.2-H) were used to determine summer and winter discomfort for future years (2009-2100) based on two emissions scenarios B1 and A1B (low and medium forcing). The 92-year (2009-2100) mean cold stress would be reduced from the 41-year (1968-2008) mean value of 8.7 to about three for both emissions scenarios. The 92-year mean heat stress would be 115.9 and 120.6 for B1 and A1B, respectively, representing 31.6% and 36.9% increase over the 1968-2008 long-term average of 88.1. These suggest that the already small winter heating requirement in subtropical Hong Kong would become even more insignificant in future years, whereas the increasing trend of summer discomfort would result in more cooling demand in the built environment. (author)

A federal partnership to pursue operational prediction at the weather-climate interface

Science.gov (United States)

Sandgathe, Scott A.; Eleuterio, Daniel; Warren, Steven

2012-10-01

Earth System Prediction Capability Workshop Washington, D. C., 21-23 March 2012 A meeting to advance a federal partnership toward operational prediction of the physical environment at subseasonal to decadal time scales was held in Washington, D. C. Scientists, headquarters representatives, and program managers from the Department of Energy, NASA, the National Oceanic and Atmospheric Administration (NOAA), the National Science Foundation, the U.S. Air Force, and the U.S. Navy met to discuss pressing agency requirements for extended-range environmental prediction to inform economic, energy, agricultural, national security, and infrastructure decisions. After significant review and discussion, participants agreed that the highest potential for progress was at the interseasonal to interannual (ISI) time scales (Advancing the Science of Climate Change (2010), Board on Atmospheric Sciences and Climate (BASC), http://www.nap.edu/openbook.php?record_id=12782). They agreed to pursue a joint effort, identifying five areas for near-term demonstrations of predictability and establishing volunteer coordinators to organize the demonstration efforts. The demonstrations will establish operational extended-range predictive skill, inform further research, enhance interagency collaboration, and push forward environmental prediction technical and computational capabilities.

Circadian modulation of short-term memory in Drosophila.

Science.gov (United States)

Lyons, Lisa C; Roman, Gregg

2009-01-01

Endogenous biological clocks are widespread regulators of behavior and physiology, allowing for a more efficient allocation of efforts and resources over the course of a day. The extent that different processes are regulated by circadian oscillators, however, is not fully understood. We investigated the role of the circadian clock on short-term associative memory formation using a negatively reinforced olfactory-learning paradigm in Drosophila melanogaster. We found that memory formation was regulated in a circadian manner. The peak performance in short-term memory (STM) occurred during the early subjective night with a twofold performance amplitude after a single pairing of conditioned and unconditioned stimuli. This rhythm in memory is eliminated in both timeless and period mutants and is absent during constant light conditions. Circadian gating of sensory perception does not appear to underlie the rhythm in short-term memory as evidenced by the nonrhythmic shock avoidance and olfactory avoidance behaviors. Moreover, central brain oscillators appear to be responsible for the modulation as cryptochrome mutants, in which the antennal circadian oscillators are nonfunctional, demonstrate robust circadian rhythms in short-term memory. Together these data suggest that central, rather than peripheral, circadian oscillators modulate the formation of short-term associative memory and not the perception of the stimuli.

Storm impacts on a high energy sandy beach system, northwest Ireland: short (event) to long term (decadal) behaviour

Science.gov (United States)

Guisado-Pintado, Emilia; Jackson, Derek; Cooper, Andrew; O'Connor, Marianne

2017-04-01

Long-term monitoring of beach dynamics is an important element in risk prevention and management of both natural and human resources at the coast. The predicted intensification in storminess (frequency, duration and magnitude), partly associated with climate change, represents a pressing concern for coastal communities globally and has undoubtedly led to an improvement in available techniques and technologies for observation and analysis. Here we examine a high energy Atlantic beach system at Five Fingers strand (NW Ireland) to help understand hydrodynamic forcing on beach response under various wave energy scenarios. The system, which has been modally attuned to a large swell wave environment, periodically undergoes significant morphological changes over various spatial and temporal scales manifest in the development and movements of dynamic nearshore bars and a nearshore ebb-tide delta. A combination of field and laboratory techniques (GPS, Terrestrial Laser Scanning (TLS) Instrumentation, Drone surveys) implemented from the shoreface to the beach, captures the response and evolution of the system over the short (event), medium (weeks to months) and long-term (multiyear) timescale. Numerical modelling of nearshore wave hydrodynamics (using SWAN wave simulation model) helps understanding wave forcing across shoreface area and is ran under a number of iterative time intervals. Here, we investigate the role of infrequent and sometimes extreme events in the system to understand the importance of clustering of storminess and the occurrence of single high-magnitude storm events that perturb the inlet-beach system and thus induce key morphodynamic changes. Preliminary results show that ultimately the configuration of the ebb-tide channel influences the geomorphic response of the system. In the short term, a storm induced erosion of the shoreface is observed, which also appears to lead to changes in the ebb-tide channel, and ultimately the welding of a nearshore bar

The Role of Short-term Consolidation in Memory Persistence

OpenAIRE

Timothy J. Ricker

2015-01-01

Short-term memory, often described as working memory, is one of the most fundamental information processing systems of the human brain. Short-term memory function is necessary for language, spatial navigation, problem solving, and many other daily activities. Given its importance to cognitive function, understanding the architecture of short-term memory is of crucial importance to understanding human behavior. Recent work from several laboratories investigating the entry of information into s...

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

Science.gov (United States)

Harnsberger, James D.

2002-05-01

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

Predictors of survival and ability to wean from short-term mechanical circulatory support device following acute myocardial infarction complicated by cardiogenic shock.

Science.gov (United States)

Garan, A Reshad; Eckhardt, Christina; Takeda, Koji; Topkara, Veli K; Clerkin, Kevin; Fried, Justin; Masoumi, Amirali; Demmer, Ryan T; Trinh, Pauline; Yuzefpolskaya, Melana; Naka, Yoshifumi; Burkhoff, Dan; Kirtane, Ajay; Colombo, Paolo C; Takayama, Hiroo

2017-11-01

Cardiogenic shock following acute myocardial infarction (AMI-CS) portends a poor prognosis. Short-term mechanical circulatory support devices (MCSDs) provide hemodynamic support for patients with cardiogenic shock but predictors of survival and the ability to wean from short-term MCSDs remain largely unknown. All patients > 18 years old treated at our institution with extra-corporeal membrane oxygenation or short-term surgical ventricular assist device for AMI-CS were studied. We collected acute myocardial infarction details with demographic and hemodynamic variables. Primary outcomes were survival to discharge and recovery from MCSD (i.e. survival without heart replacement therapy including durable ventricular assist device or heart transplant). One hundred and twenty-four patients received extra-corporeal membrane oxygenation or short-term surgical ventricular assist device following acute myocardial infarction from 2007 to 2016; 89 received extra-corporeal membrane oxygenation and 35 short-term ventricular assist device. Fifty-five (44.4%) died in the hospital and 69 (55.6%) survived to discharge. Twenty-six (37.7%) required heart replacement therapy (four transplant, 22 durable ventricular assist device) and 43 (62.3%) were discharged without heart replacement therapy. Age and cardiac index at MCSD implantation were predictors of survival to discharge; patients over 60 years with cardiac index <1.5 l/min per m 2 had a low likelihood of survival. The angiographic result after revascularization predicted recovery from MCSD (odds ratio 9.00, 95% confidence interval 2.45-32.99, p=0.001), but 50% of those optimally revascularized still required heart replacement therapy. Cardiac index predicted recovery from MCSD among this group (odds ratio 4.06, 95% confidence interval 1.45-11.55, p=0.009). Among AMI-CS patients requiring short-term MCSDs, age and cardiac index predict survival to discharge. Angiographic result and cardiac index predict ventricular recovery but 50

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

Science.gov (United States)

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

2016-02-01

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

Can We Envision a Bettor's Guide to Climate Prediction Markets?

Science.gov (United States)

Trexler, M.

2017-12-01

It's one thing to set up a climate prediction market, it's another to find enough informed traders to make the market work. Climate bets could range widely, from purely scientific or atmospheric metrics, to bets that involve the interplay of science, policy, economic, and behavioral outcomes. For a topic as complex and politicized as climate change, a Bettor's Guide to Climate Predictions could substantially expand and diversify the pool of individuals trading in the market, increasing both its liquidity and decision-support value. The Climate Web is an on-line and publically accessible Beta version of such a Bettor's Guide, implementing the knowledge management adage: "if only we knew what we know." The Climate Web not only curates the key literature, news coverage, and websites relating to more than 100 climate topics, from extreme event exceedance curves to climate economics to climate risk scenarios, it extracts and links together thousands of ideas and graphics across all of those topics. The Climate Web integrates the many disciplinary silos that characterize today's often dysfunctional climate policy conversations, allowing rapid cross-silo exploration and understanding. As a Bettor's Guide it would allow prediction market traders to better research and understand their potential bets, and to quickly survey key thinking and uncertainties relating to those bets. The availability of such a Bettor's Guide to Climate Predictions should make traders willing to place more bets than they otherwise would, and should facilitate higher quality betting. The presentation will introduce the knowledge management dimensions and challenges of climate prediction markets, and introduce the Climate Web as one solution to those challenges.

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

DEFF Research Database (Denmark)

Blank, N; Diderichsen, Finn

1995-01-01

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

Short term prediction of the horizontal wind vector within a wake vortex warning system

Energy Technology Data Exchange (ETDEWEB)

Frech, M.; Holzaepfel, F.; Gerz, T. [DLR Deutsches Zentrum fuer Luft- und Raumfahrt e.V., Wessling (Germany). Inst. fuer Physik der Atmosphaere; Konopka, J. [Deutsche Flugsicherung (DFS) GmbH, Langen (Germany)

2000-07-14

A wake vortex warning system (WVWS) has been developed for Frankfurt airport. This airport has two parallel runways which are separated by 518 m, a distance too short to operate them independently because wake vortices may be advected to the adjacent runway. The objective of the WVWS is to enable operation with reduced separation between two aircraft approaching the parallel runways at appropriate wind conditions. The WVWS applies a statistical persistence model to predict the crosswind within a 20 minute period. One of the main problems identified in the old WVWS are discontinuities between successive forecasts. These forecast breakdowns were not acceptable to airtraffic controllers. At least part of the problem was related to the fact that the forecast was solely based on the prediction of crosswind. A new method is developed on the basis of 523 days of sonic anemometer measurements at Frankfurt airport. It is demonstrated that the prediction of the horizontal wind vector avoids these difficulties and significantly improves the system's performance. (orig.)

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

Science.gov (United States)

Di Persio, Luca; Honchar, Oleksandr

2017-11-01

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

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

Science.gov (United States)

Kemps, Eva; Andrade, Jackie

2012-05-01

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

Prospective testing of Coulomb short-term earthquake forecasts

Science.gov (United States)

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

2009-12-01

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