scholarly journals Attentional control in middle childhood is highly dynamic - Strong initial distraction is followed by advanced attention control

2021 ◽  
Author(s):  
Sindram Volkmer ◽  
Nicole Wetzel ◽  
Andreas Widmann ◽  
Florian Scharf
Keyword(s):  
Middle Childhood ◽  
Multilevel Models ◽  
Attention Control ◽  
Experimental Session ◽  
Learning Mechanisms ◽  
Short Term ◽  
Distraction Effect ◽  
Data Set ◽  
Categorization Task ◽  
Distraction Control

The ability to shield against distraction while focusing on a task requires the operation of executive functions and is essential for successful learning. We investigated the short-term dynamics of distraction control in a data set of 269 children aged 4–10 years and 51 adults pooled from three studies using multilevel models. Participants performed a visual categorization task while a task-irrelevant sequence of sounds was presented which consisted of frequently repeated standard sounds and rarely interspersed novel sounds. On average, participants responded slower in the categorization task after novel sounds. This distraction effect was more pronounced in children. Throughout the experiment, the initially strong distraction effects declined to level of adults in the groups of 6- to 10-year-olds. Such a decline was neither observed in the groups of the 4- and 5-year-olds, who consistently show a high level of distraction, nor in adults, who showed a constantly low level of distraction throughout the experimental session. Results indicate that distraction control is a highly dynamic process that qualitatively and quantitatively differs between age groups.We conclude that the analysis of short-term dynamics provides valuable insights into the development of attention control and might explain inconsistent findings regarding distraction control in middle childhood. In addition, models of attention control need to be refined to account for age-dependent rapid learning mechanisms. Our findings have implications for the design of learning situations and provide an additional source of information for diagnosis and treatment of attention deficit disorders.

Short-Term Flood Prediction System

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Konrad Nering
Keyword(s):  
Flash Floods ◽  
Prediction System ◽  
Short Term ◽  
Data Set ◽  
Flood Prediction ◽  
Full Operation

AbstractThis paper describes a fully functional short-term flood prediction system. Its effect has been tested on watershed of Lubieńka river in Małopolska. To use this system it must have a data set also described in this paper. A modification of the system to adopt for predicting flash floods was described. Full operation of the system is shown on example of real flood on Lubieńka river in June 2011.

scholarly journals Investigating the Broken-Heart Effect: a Model for Short-Term Dependence between the Remaining Lifetimes of Joint Lives

2012 ◽  
Vol 7 (2) ◽  
pp. 236-257 ◽  
Author(s):  
Jaap Spreeuw ◽  
Iqbal Owadally
Keyword(s):  
Mortality Rates ◽  
Short Term ◽  
Numerical Work ◽  
Data Set ◽  
Multiple State ◽  
Multiple State Model ◽  
Insurance Data ◽  
Over Time ◽  
Assurance Contracts

AbstractWe analyze the mortality of couples by fitting a multiple state model to a large insurance data set. We find evidence that mortality rates increase after the death of a partner and, in addition, that this phenomenon diminishes over time. This is popularly known as a “broken-heart” effect and we find that it affects widowers more than widows. Remaining lifetimes of joint lives therefore exhibit short-term dependence. We carry out numerical work involving the pricing and valuation of typical contingent assurance contracts and of a joint life and survivor annuity. If insurers ignore dependence, or mis-specify it as long-term dependence, then significant mis-pricing and inappropriate provisioning can result. Detailed numerical results are presented.

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

2021 ◽  
Author(s):  
Eva van der Kooij ◽  
Marc Schleiss ◽  
Riccardo Taormina ◽  
Francesco Fioranelli ◽  
Dorien Lugt ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
Heavy Rainfall ◽  
Predictive Performance ◽  
Heavy Precipitation ◽  
Early Warning Systems ◽  
Training Data ◽  
Short Term ◽  
Data Set ◽  
Radar Images

<p>Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.</p><p>We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.</p><p>We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.</p><p>To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.</p>

scholarly journals The Impact of Teachers Unions on Teachers’ Well-Being Under Various Legal Institutions: Evidence From District–Teacher Matched Data

AERA Open ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 233285841986729 ◽  
Author(s):  
Eunice S. Han
Keyword(s):  
Multilevel Models ◽  
Causal Effect ◽  
Well Being ◽  
Legal Environment ◽  
Legal Institutions ◽  
Data Set ◽  
Teachers Unions ◽  
The Difference ◽  
Matched Data ◽  
The Impact

This article examines how teachers unions affect teachers’ well-being under various legal institutions. Using a district–teacher matched data set, this study identifies the union effects by three approaches. First, I contrast teacher outcomes across different state laws toward unions. Second, I compare the union–nonunion differentials within the same legal environment, using multilevel models and propensity score matching. Finally, unexpected legal changes restricting the collective bargaining of teachers in four states form a natural experiment, allowing me to use the difference-in-difference estimation to identify the causal effect of weakening unionism on teacher outcomes. I find that (a) many teachers join unions even when bargaining is rarely or never available, and meet-and-confer or union membership rate affects teachers’ lives in the absence of a bargaining contract; (b) how unions influence teacher outcomes vary greatly by different legal environment; and (c) the changes in public policy limiting teachers’ bargaining rights significantly decrease teacher compensation.

scholarly journals Interactive effects of ambient ozone and climate measured on growth of mature loblolly pine trees

1996 ◽  
Vol 26 (4) ◽  
pp. 670-681 ◽  
Author(s):  
S.B. McLaughlin ◽  
D.J. Downing
Keyword(s):  
Soil Moisture ◽  
Loblolly Pine ◽  
Moisture Stress ◽  
Growth Patterns ◽  
Forest Growth ◽  
Interactive Effects ◽  
Forest Trees ◽  
Short Term ◽  
Data Set ◽  
Ambient Ozone

Seasonal growth patterns of mature loblolly pine (Pinustaeda L.) trees over the interval 1988–1993 have been analyzed to evaluate the effects of ambient ozone on growth of large forest trees. Patterns of stem expansion and contraction of 34 trees were examined using serial measurements with sensitive dendrometer band systems. Study sites, located in eastern Tennessee, varied significantly in soil moisture, soil fertility, and stand density. Levels of ozone, rainfall, and temperature varied widely over the 6-year study interval. Regression analysis identified statistically significant influences of ozone on stem growth patterns, with responses differing widely among trees and across years. Ozone interacted with both soil moisture stress and high temperatures, explaining 63% of the high frequency, climatic variance in stem expansion identified by stepwise regression of the 5-year data set. Observed responses to ozone were rapid, typically occurring within 1–3 days of exposure to ozone at ≥40 ppb and were significantly amplified by low soil moisture and high air temperatures. Both short-term responses, apparently tied to ozone-induced increases in whole-tree water stress, and longer term cumulative responses were identified. These data indicate that relatively low levels of ambient ozone can significantly reduce growth of mature forest trees and that interactions between ambient ozone and climate are likely to be important modifiers of future forest growth and function. Additional studies of mechanisms of short-term response and interspecies comparisons are clearly needed.

scholarly journals A Dense Long Short-Term Memory Model for Enhancing the Imagery-Based Brain-Computer Interface

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaofei Zhang ◽  
Tao Wang ◽  
Qi Xiong ◽  
Yina Guo
Keyword(s):  
Short Term Memory ◽  
Recognition Algorithm ◽  
Training Process ◽  
Short Term ◽  
Data Set ◽  
Static State ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Model Training ◽  
The One

Imagery-based brain-computer interfaces (BCIs) aim to decode different neural activities into control signals by identifying and classifying various natural commands from electroencephalogram (EEG) patterns and then control corresponding equipment. However, several traditional BCI recognition algorithms have the “one person, one model” issue, where the convergence of the recognition model’s training process is complicated. In this study, a new BCI model with a Dense long short-term memory (Dense-LSTM) algorithm is proposed, which combines the event-related desynchronization (ERD) and the event-related synchronization (ERS) of the imagery-based BCI; model training and testing were conducted with its own data set. Furthermore, a new experimental platform was built to decode the neural activity of different subjects in a static state. Experimental evaluation of the proposed recognition algorithm presents an accuracy of 91.56%, which resolves the “one person one model” issue along with the difficulty of convergence in the training process.

scholarly journals An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
Indirect Method ◽  
Short Term Memory ◽  
Groundwater Table ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

scholarly journals Are we adequately reviewing confusion inducing drugs in patients referred to the memory assessment service?

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S193-S193
Author(s):  
Siddhant Hegde ◽  
Rashi Negi ◽  
Hari Shanmugaratnam
Keyword(s):  
Quality Improvement ◽  
Cognitive Impairment ◽  
Royal College ◽  
Support Worker ◽  
Short Term ◽  
Data Set ◽  
Memory Assessment ◽  
Dementia Syndrome ◽  
Diagnosis Of Dementia ◽  
The Impact

AimsThe aim of this quality improvement evaluation project is to establish the standard of current practice in relation to reviewing confusion inducing drugs (CIDs) at the time of referral, as it has been hypothesised that these medications contribute to short term cognitive impairment. This is essential in order to establish the validity of the diagnostic processes of dementia syndrome in the memory assessment services.BackgroundIt has long been established that anti-cholinergic medications (ACMs) have contributed to short-term cognitive impairment in patients taking them. This is compounded with the fact that these medications may be continued without review, for longer than was originally intended. The impact of polypharmacy, subsequent anti-cholinergic burden, and the overlapping presence of delirium, may call into question the validity of a diagnosis of dementia in patients who have not been correctly vetted during the course of their assessment. This quality improvement evaluation aims to assess whether patients’ medications are being reviewed before diagnosing a memory disorder. This is in accordance with guidance set out by the NG97 NICE guidelines, The Royal College of Psychiatrists Memory Service National Accreditation Programme (MSNAP), and the National Institute on Ageing and Alzheimer's Association (NIA-AA).MethodAll new referrals to the memory assessment service during July and August 2019 were systematically reviewed and data extracted from the memory referral document and entries on RIO from first point of contact. The following data were recorded: patient ID, GPCOG/6CIT score, final diagnosis, CID prescriptions and CID review.ResultThe results were collated using a data-set of 216 patients (136 females and 80 males,) of which the mean age was 79 years. It was noted that 36% of patients had not had any sort of cognitive assessment before referral, which identifies an area for improvement. However the most substantial finding was that only 10 patients (5%) had a CID prescription review documented in the RIO notes.ConclusionOur data suggest that in our memory assessment service, only a small proportion of patients are having a documented review of their CIDs prior to diagnosis of dementia. In order to improve this and thus improve compliance with guidelines from the Royal College of Psychiatrists MSNAP and the NIA-AA, measures will be taken to issue each dementia support worker and nurse with a CID prescription review card, which will list those medications to consider and flag for review.

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