Modify alpha value of EMA method and brown method: A data forecasting comparison of COVID-19

2021 ◽  
Author(s):  
Syaharuddin ◽  
Habib Ratu Perwira Negara ◽  
Malik Ibrahim ◽  
Ahmad ◽  
Muhammad Zulfikri ◽  
...  
Keyword(s):  
Ema Method

Investigating the Dynamics of Suicidal Ideation

Crisis ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 65-69 ◽  
Author(s):  
Nina Hallensleben ◽  
Lena Spangenberg ◽  
Thomas Forkmann ◽  
Dajana Rath ◽  
Ulrich Hegerl ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Small Sample Size ◽  
Unipolar Depression ◽  
Small Sample ◽  
History Of ◽  
Severity Of Depression ◽  
Ecological Momentary ◽  
Depressive Episodes ◽  
Ema Method ◽  
Shed Light

Abstract. Background: Although the fluctuating nature of suicidal ideation (SI) has been described previously, longitudinal studies investigating the dynamics of SI are scarce. Aim: To demonstrate the fluctuation of SI across 6 days and up to 60 measurement points using smartphone-based ecological momentary assessments (EMA). Method: Twenty inpatients with unipolar depression and current and/or lifetime suicidal ideation rated their momentary SI 10 times per day over a 6-day period. Mean squared successive difference (MSSD) was calculated as a measure of variability. Correlations of MSSD with severity of depression, number of previous depressive episodes, and history of suicidal behavior were examined. Results: Individual trajectories of SI are shown to illustrate fluctuation. MSSD values ranged from 0.2 to 21.7. No significant correlations of MSSD with several clinical parameters were found, but there are hints of associations between fluctuation of SI and severity of depression and suicidality. Limitations: Main limitation of this study is the small sample size leading to low power and probably missing potential effects. Further research with larger samples is necessary to shed light on the dynamics of SI. Conclusion: The results illustrate the dynamic nature and the diversity of trajectories of SI across 6 days in psychiatric inpatients with unipolar depression. Prediction of the fluctuation of SI might be of high clinical relevance. Further research using EMA and sophisticated analyses with larger samples is necessary to shed light on the dynamics of SI.

Investigation of the technique of ultrasonic thickness control using the EMA method

2010 ◽  
Vol 46 (6) ◽  
pp. 440-457 ◽  
Author(s):  
N. P. Razygraev ◽  
A. N. Razygraev ◽  
S. N. Ponomarev ◽  
G. Ya. Bezlyud’ko ◽  
M. Mikhovski ◽  
...  
Keyword(s):  
Thickness Control ◽  
Ultrasonic Thickness ◽  
Ema Method

scholarly journals INFLUENCE OF DYNAMIC MAGNETIZATION TO IMPROVE THE EFFICIENCY OF ELECTROMAGNETIC-ACOUSTIC TRANSFORMATION WITH WAVEGUIDE CONTROL RODS

2017 ◽  
Vol 8 (3) ◽  
pp. 236-245 ◽  
Author(s):  
D. V. Zlobin ◽  
L. V. Volkova
Keyword(s):  
Acoustic Pulse ◽  
Signal Amplitude ◽  
Pulse Amplitude ◽  
Bias Field ◽  
Ultrasonic Waves ◽  
Multiple Reflections ◽  
Field Dynamic ◽  
Low Efficiency ◽  
Ema Method ◽  
Dynamic Bias

The disadvantage of the electromagnetic-acoustic (EMA) method receiving ultrasonic waves are low efficiency. The traditional way to enhance its effectiveness is increase the bias field. The aim of the study was research the way to improve the efficiency of the EMA transformation, using a time-varying bias field.The researches held with the help of a specially designed installation that allows the magnetization to be performed by a constant and alternating magnetic field (dynamic bias), synchronously with the passage of the received pulse. The object of the study were rods made of different grades of steel with a diameter of 4–6 mm, in which the symmetrical zero mode S0 of the rod wave was excited by the EMA method (in the frequency range of about 40 kHz). A comparative analysis of the amplitudes and form pulses of multiple reflections during static and dynamic reversal of magnetization and with a full cycle of magnetization reversal conducted.The result of the efficiency measurements EMA reception during static and dynamic bias found a significant (up to 5 times) increase in the signal amplitude on the receiving transducer. Taking into account that the main contribution to the excitation mechanism and the reception mechanism made the magnetostrictive effect on low frecuncy, it can assumed that using a dynamic bias field is impacting significant on the effective mobility of magnetic domains (that is changes the dynamic magnetic susceptibility of the material). It is established that it is possible to monitor steel at lower values of the bias field, and, consequently, to reduce the mass dimensions of the magnetic system.Thus, in the course of the researchers found of effect of dynamic bias and effect of dynamic bias increase acoustic pulse amplitude of the signal of the received EMA method. Using this method will improve the quality EMA testing by creating more efficient EMA transducer. Taking into account that the value of the detected effect depends significantly on the steel grade, we can assume its possible application in the methods of express analysis, estimation of structural and stressed states. 

Variable-structure interacting multiple-model estimation for group targets tracking with random matrices

2017 ◽  
Vol 232 (7) ◽  
pp. 1201-1211
Author(s):  
Zhen-Xing Li ◽  
Yun Wang ◽  
Jin-Mang Liu ◽  
Ni Peng ◽  
Lin-Hai Gan
Keyword(s):  
Variable Structure ◽  
Weight Coefficient ◽  
Extended Model ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Weighting Method ◽  
Basic Model ◽  
Fixed Parameter ◽  
Model Set ◽  
Ema Method

In order to improve the estimation performance of interacting multiple model tracking algorithm for group targets, the expected-mode augmentation variable-structure interacting multiple model (EMA-VSIMM) and the best model augmentation variable-structure interacting multiple model (BMA-VSIMM) tracking algorithms are presented in this paper. First, by using the EMA method, a more proper expected-mode set has been chosen from the basic model set of group targets, which can make the selected tracking models better match up to the true mode. The BMA algorithm uses a fixed parameter model of different structures to constitute a candidate model set and selects a minimum difference model from target state as the present extended model from the set of candidates at real time. Second, in the filtering process of VSIMM, the fusion estimation of extension state is implemented by the scalar coefficients weighting method, where weight coefficient is calculated by the trace of the corresponding covariance matrix of extension state. The performances of the proposed EMA-VSIMM and BMA-VSIMM algorithms are evaluated via simulation of a generic group targets maneuvering tracking problem.

A comparison between optical properties of TiO2 nanowires obtained by EMA method and experiment

2011 ◽  
Vol 406 (18) ◽  
pp. 3383-3388 ◽  
Author(s):  
S. Ramezani Sani ◽  
A. Morteza Ali ◽  
R. Jafari
Keyword(s):  
Optical Properties ◽  
Tio2 Nanowires ◽  
Ema Method

scholarly journals Mood and Stress Evaluation of Adult Patients With Moyamoya Disease in Korea: Ecological Momentary Assessment Method Using a Mobile Phone App

10.2196/17034 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e17034 ◽  
Author(s):  
Yong Sook Yang ◽  
Gi Wook Ryu ◽  
Chang Gi Park ◽  
Insun Yeom ◽  
Kyu Won Shim ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Moyamoya Disease ◽  
Ecological Momentary Assessment ◽  
Public Place ◽  
The Mean ◽  
Ecological Momentary ◽  
Ema Method ◽  
Momentary Assessment ◽  
Different Levels ◽  
Over Time

Background Moyamoya disease (MMD) is a known progressive obstructive cerebrovascular disorder. Monitoring and managing mood and stress are critical for patients with MMD, as they affect clinical outcomes. The ecological momentary assessment (EMA) method is a longitudinal study design by which multiple variable assessments can be performed over time to detect momentary fluctuations and changes in psychological dimensions such as mood and stress over time. Objective This study aimed to identify predicting factors associated with momentary mood and stress at both the within-person and between-person levels and to examine individual fluctuation of mood over time in the short term using an EMA method combined with a mobile phone app. Methods Participants aged older than 18 years were recruited from a tertiary hospital in Seoul, Korea, between July 2018 and January 2019. The PsyMate scale for negative affect (NA) and positive affect (PA) and the Trier Inventory for Chronic Stress Scale were uploaded on patient mobile phones. Using a mobile app, data were collected four times a day for 7 days. Pearson correlations and mixed modeling were used to predict relationships between repeatedly measured variables at both the between-person and within-person levels. Results The mean age of the 93 participants was 40.59 (SD 10.06) years, 66 (71%) were female, and 71 (76%) were married. Participants provided 1929 responses out of a possible 2604 responses (1929/2604, 74.08%). The mean momentary NA and PA values were 2.15 (SD 1.12) and 4.70 (SD 1.31) out of 7, respectively. The momentary stress value was 2.03 (SD 0.98) out of 5. Momentary NA, PA, and stress were correlated (P<.001) and varied over time in relation to momentary variables. Common momentary variables associated with momentary mood and stress at both the within-person (level 1) and between-person (level 2) levels were identified. Momentary NA increased when being alone and being at the hospital at both levels, whereas momentary PA increased when eating or drinking, resting, being at a café, restaurant or a public place but decreased when being alone at both levels. Momentary stress increased when being at the office, at a public place, or as the time of the day went by but decreased when resting or during the weekend. Different factors affecting mood and stress at different levels were identified. Fluctuations in individual momentary mood over time at the within-person level were captured. Conclusions The EMA method using a mobile phone app demonstrated its ability to capture changes in mood and stress in various environmental contexts in patients with MMD. The results could provide baseline information for developing interventions to manage negative mood and stress of patients with MMD based on the identified predictors affecting mood and stress at two different levels.

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