scholarly journals The impact of aging and hearing status on verbal short-term memory

2013 ◽  
Vol 21 (4) ◽  
pp. 464-482 ◽  
Author(s):  
Clémence Verhaegen ◽  
Fabienne Collette ◽  
Steve Majerus
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Hearing Status ◽  
The Impact

Captions and reduced forms instruction: The impact on EFL students’ listening comprehension

ReCALL ◽  
2013 ◽  
Vol 26 (1) ◽  
pp. 44-61 ◽  
Author(s):  
Jie Chi Yang ◽  
Peichin Chang
Keyword(s):  
Language Learners ◽  
Listening Comprehension ◽  
Short Term Memory ◽  
Limited Capacity ◽  
Short Term ◽  
Term Memory ◽  
Efl Learners ◽  
Efl Students ◽  
The Impact ◽  
Reduced Forms

AbstractFor many EFL learners, listening poses a grave challenge. The difficulty in segmenting a stream of speech and limited capacity in short-term memory are common weaknesses for language learners. Specifically, reduced forms, which frequently appear in authentic informal conversations, compound the challenges in listening comprehension. Numerous interventions have been implemented to assist EFL language learners, and of these, the application of captions has been found highly effective in promoting learning. Few studies have examined how different modes of captions may enhance listening comprehension. This study proposes three modes of captions: full, keyword-only, and annotated keyword captions and investigates their contribution to the learning of reduced forms and overall listening comprehension. Forty-four EFL university students participated in the study and were randomly assigned to one of the three groups. The results revealed that all three groups exhibited improvement on the pre-test while the annotated keyword caption group exhibited the best performance with the highest mean score. Comparing performances between groups, the annotated keyword caption group also emulated both the full caption and the keyword-only caption groups, particularly in the ability to recognize reduced forms. The study sheds light on the potential of annotated keyword captions in enhancing reduced forms learning and overall listening comprehension.

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Temporal Correlations ◽  
Proposed Model ◽  
The Mean ◽  
Long Short Term Memory ◽  
The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

scholarly journals Analysis of Groundwater Level Variations Caused by the Changes in Groundwater Withdrawals Using Long Short-Term Memory Network

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 64 ◽  
Author(s):  
Mun-Ju Shin ◽  
Soo-Hyoung Moon ◽  
Kyung Goo Kang ◽  
Duk-Chul Moon ◽  
Hyuk-Joon Koh
Keyword(s):  
Groundwater Level ◽  
Short Term Memory ◽  
Groundwater Resources ◽  
Prediction Performance ◽  
Groundwater Withdrawal ◽  
Short Term ◽  
Maximum Increase ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

To properly manage the groundwater resources, it is necessary to analyze the impact of groundwater withdrawal on the groundwater level. In this study, a Long Short-Term Memory (LSTM) network was used to evaluate the groundwater level prediction performance and analyze the impact of the change in the amount of groundwater withdrawal from the pumping wells on the change in the groundwater level in the nearby monitoring wells located in Jeju Island, Korea. The Nash–Sutcliffe efficiency between the observed and simulated groundwater level was over 0.97. Therefore, the groundwater prediction performance of LSTM was remarkably high. If the groundwater level is simulated on the assumption that the future withdrawal amount is reduced by 1/3 of the current groundwater withdrawal, the range of the maximum rise of the groundwater level would be 0.06–0.13 m compared to the current condition. In addition, assuming that no groundwater is taken, the range of the maximum increase in the groundwater level would be 0.11–0.38 m more than the current condition. Therefore, the effect of groundwater withdrawal on the groundwater level in this area was exceedingly small. The method and results can be used to develop new groundwater withdrawal sources for the redistribution of groundwater withdrawals.

scholarly journals Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Štifanić ◽  
Jelena Musulin ◽  
Adrijana Miočević ◽  
Sandi Baressi Šegota ◽  
Roman Šubić ◽  
...  
Keyword(s):  
Crude Oil ◽  
Stock Prices ◽  
Short Term Memory ◽  
Oil Price ◽  
Stationary Wavelet Transform ◽  
Crude Oil Price ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

scholarly journals Using long short-term memory networks for river flow prediction

2020 ◽  
Vol 51 (6) ◽  
pp. 1358-1376
Author(s):  
Wei Xu ◽  
Yanan Jiang ◽  
Xiaoli Zhang ◽  
Yi Li ◽  
Run Zhang ◽  
...  
Keyword(s):  
River Flow ◽  
Short Term Memory ◽  
River Basins ◽  
Short Term ◽  
Term Memory ◽  
Learning Efficiency ◽  
Flow Prediction ◽  
Non Linear ◽  
Long Short Term Memory ◽  
The Impact

Abstract Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.

scholarly journals Do elite breath-hold divers suffer from mild short-term memory impairments?

2018 ◽  
Vol 43 (3) ◽  
pp. 247-251 ◽  
Author(s):  
François Billaut ◽  
Patrice Gueit ◽  
Sylvane Faure ◽  
Guillaume Costalat ◽  
Frédéric Lemaître
Keyword(s):  
Short Term Memory ◽  
Breath Hold ◽  
Short Term ◽  
Attentional Processing ◽  
Term Memory ◽  
Memory Impairments ◽  
Neurocognitive Functions ◽  
Neuropsychological Tasks ◽  
The Impact

Repeated apneas are associated with severe hypoxemia that may ultimately lead to loss of consciousness in some breath-hold divers. Despite increasing number of practitioners, the relationship between apnea-induced hypoxia and neurocognitive functions is still poorly understood in the sport of free diving. To shed light onto this phenomenon, we examined the impact of long-term breath-hold diving training on attentional processing, short-term memory, and long-term mnesic and executive functions. Thirty-six men matched for age, height, and weight were separated into the following 3 groups: (i) 12 elite breath-hold divers (EBHD), mean static apnea best time 371 s, 105 months mean apnea experience; (ii) 12 novice breath-hold divers, mean best time 243 s, 8.75 months mean apnea experience; and (iii) 12 physical education students with no breath-hold diving experience; all of these participants performed varied written and computerized neuropsychological tasks. Compared with the 2 other groups, the EBHD group was slower to complete the interference card during a Stroop test (F[1,33] = 4.70, p < 0.05), and presented more errors on the interference card (F[1,33] = 2.96, p < 0.05) and a lower total interference score (F[1,33] = 5.64, p < 0.05). The time to complete the interference card test was positively correlated with maximal static apnea duration (r = 0.73, p < 0.05) and the number of years of breath-hold diving training (r = 0.79, p < 0.001). These findings suggest that breath-hold diving training over several years may cause mild, but persistent, short-term memory impairments.

scholarly journals Voluntary running does not increase capillary blood flow but promotes neurogenesis and short-term memory in the APP/PS1 mouse model of Alzheimer’s disease

2020 ◽  
Author(s):  
Kaja Falkenhain ◽  
Nancy E. Ruiz-Uribe ◽  
Mohammad Haft-Javaherian ◽  
Muhammad Ali ◽  
Pietro E. Michelucci ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Mouse Models ◽  
Short Term Memory ◽  
Wheel Running ◽  
Memory Function ◽  
Brain Inflammation ◽  
Short Term ◽  
Term Memory ◽  
The Impact

ABSTRACTExercise exerts a beneficial effect on the major pathological and clinical symptoms associated with Alzheimer’ s disease in humans and mouse models of the disease. While numerous mechanisms for such benefits from exercise have been proposed, a clear understanding of the causal links remains elusive. Recent studies also suggest that cerebral blood flow in the brain of both Alzheimer’ s patients and mouse models of the disease is decreased and that the cognitive symptoms can be improved when blood flow is restored. We therefore hypothesized that the mitigating effect of exercise on the development and progression of Alzheimer’ s disease may be mediated through an increase in the otherwise reduced brain blood flow. To test this idea, we examined the impact of three months of voluntary wheel running in ∼1-year-old APP/PS1 mice on short-term memory function, brain inflammation, amyloid deposition, and cerebral blood flow. Our findings that exercise led to improved memory function, a trend toward reduced brain inflammation, markedly increased neurogenesis in the dentate gyrus, and no changes in amyloid-beta deposits are consistent with other reports on the impact of exercise on the progression of Alzheimer’ s related symptoms in mouse models. Notably, we did not observe any impact of wheel running on overall cortical blood flow nor on the incidence of non-flowing capillaries, a mechanism we recently identified as one contributing factor to cerebral blood flow deficits in mouse models of Alzheimer’ s disease. Overall, our results replicate previous findings that exercise is able to ameliorate certain aspects of Alzheimer’ s disease pathology, but show that this benefit does not appear to act through increases in cerebral blood flow.

scholarly journals Impact of Uncertainty in the Input Variables and Model Parameters on Predictions of a Long Short Term Memory (LSTM) Based Sales Forecasting Model

2020 ◽  
Vol 2 (3) ◽  
pp. 256-270
Author(s):  
Shakti Goel ◽  
Rahul Bajpai
Keyword(s):  
Short Term Memory ◽  
Forecasting Model ◽  
Model Parameters ◽  
Prediction Errors ◽  
Short Term ◽  
Term Memory ◽  
Corona Virus ◽  
Long Short Term Memory ◽  
Input Variables ◽  
The Impact

A Long Short Term Memory (LSTM) based sales model has been developed to forecast the global sales of hotel business of Travel Boutique Online Holidays (TBO Holidays). The LSTM model is a multivariate model; input to the model includes several independent variables in addition to a dependent variable, viz., sales from the previous step. One of the input variables, “number of active bookers per day”, is estimated for the same day as sales. This need for estimation requires the development of another LSTM model to predict the number of active bookers per day. The number of active bookers is variable, so the predicted is used as an input to the sales forecasting model. The use of a predicted variable as an input variable to another model increases the chance of uncertainty entering the system. This paper discusses the quantum of variability observed in sales predictions for various uncertainties or noise due to the estimation of the number of active bookers. For the purposes of this study, different noise distributions such as normalized, uniform, and logistic distributions are used, among others. Analyses of predictions demonstrate that the addition of uncertainty to the number of active bookers via dropouts as well as to the lagged sales variables leads to model predictions that are close to the observations. The least squared error between observations and predictions is higher for uncertainties modeled using other distributions (without dropouts) with the worst predictions being for Gumbel noise distribution. Gaussian noise added directly to the weights matrix yields the best results (minimum prediction errors). One possibility of this uncertainty could be that the global minimum of the least squared objective function with respect to the model weight matrix is not reached, and therefore, model parameters are not optimal. The two LSTM models used in series are also used to study the impact of corona virus on global sales. By introducing a new variable called the corona virus impact variable, the LSTM models can predict corona-affected sales within five percent (5%) of the actuals. The research discussed in the paper finds LSTM models to be effective tools that can be used in the travel industry as they are able to successfully model the trends in sales. These tools can be reliably used to simulate various hypothetical scenarios also.

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