scholarly journals Evaluating Emotional and Biological Sensitivity to Maternal Behavior Among Self-Injuring and Depressed Adolescent Girls Using Nonlinear Dynamics

2017 ◽  
Vol 5 (2) ◽  
pp. 272-285 ◽  
Author(s):  
Sheila E. Crowell ◽  
Jonathan E. Butner ◽  
Travis J. Wiltshire ◽  
Ascher K. Munion ◽  
Mona Yaptangco ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Adolescent Girls ◽  
Time Series Data ◽  
Behavioral Responses ◽  
High Sensitivity ◽  
Series Data ◽  
Physiological Reactivity ◽  
Psychophysiological Reactivity ◽  
Depressed Adolescent ◽  
And Control

High sensitivity and reactivity to behaviors of family members characterize several forms of psychopathology, including self-inflicted injury (SII). We examined mother-daughter behavioral and psychophysiological reactivity during a conflict discussion using nonlinear dynamics to assess asymmetrical associations within time-series data. Depressed, SII, and control adolescents and their mothers participated ( N = 76 dyads). We expected that (a) mothers’ evocative behaviors would affect behavioral and psychophysiological reactivity among depressed and, especially, SII adolescents, (b) adolescents’ behaviors would not evoke mothers’ behavioral or physiological reactivity, and (c) control teens and mothers would be less reactive, with no dynamic associations in either direction. Convergent cross-mapping with dewdrop regression, which identifies directional associations, indicated that mothers’ behaviors evoked behavioral responses among depressed and SII participants, but evoked psychophysiological reactivity for SII teens only. There were no effects of adolescents’ behavior on mothers’ reactivity. Results are interpreted based on sensitivity theories and directions for further research are outlined.

Oil and Methanol Price volatility

2013 ◽  
Vol 03 (08) ◽  
pp. 01-10
Author(s):  
Majid Delavari ◽  
Nadiya Gandali Ali khani ◽  
Esmaeil Naderi
Keyword(s):  
Crude Oil ◽  
Time Series Data ◽  
Price Volatility ◽  
High Sensitivity ◽  
Oil Price ◽  
Series Data ◽  
Cultural Issues ◽  
Crude Oil Price ◽  
Oil Export ◽  
The One

Crude oil as one of the main sources of energy is also the main source of income for members of OPEC. So, the volatility of crude oil price is one of the main economic variables in the world and analysis of the effect of its changes on key economic factors has been always considered as significant. The reason might be the high sensitivity of oil price to political, economic and cultural issues worldwide and consequently its volatility on the one hand, and the high influence of the volatile prices on macroeconomic variables. On the other hand, for different reasons such as oil price volatilities and income from oil export, economic planners and policy makers in Iran have been mainly focused on the promotion of non-oil exports especially during the last few decades. Therefore, methanol as one of the most commonly used petrochemical products has a high potential for production and export of non-oil products in Iran. For this reason, in the present study there was an attempt to examine the relationship between the prices of Iran’s crude oil and methanol using FIGARCH model and based on the weekly time series data related to the research variables. The results of the study showed that the long memory parameter is equal to 0.32 which is meaning the shocks caused by volatility of methanol market and crude oil price to the methanol price were lasting and meaningful and were revealed in the long term.

Modelling the Nexus Between Foreign Aid and Economic Growth: A Case of Afghanistan and Egypt

2021 ◽  
Vol 39 (2) ◽  
Author(s):  
Muhammed Ashiq Villanthenkodath ◽  
Ubaid Mushtaq
Keyword(s):  
Economic Growth ◽  
Foreign Aid ◽  
Time Series Data ◽  
Trade Openness ◽  
Political Stability ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
Annual Time Series ◽  
And Control

This paper tries to explore the existence of a long-run relationship between foreign aid and economic growth by using the data from the two highest foreign aid recipient countries. Using the annual time series data from 1965 to 2017 this study uses several econometric models such as Johansen and Juselius cointegration, Granger causality and vector auto regression to establish the long and short-run relationships among foreign aid inflows and economic growth while also considering financial development and trade openness from both the countries. The empirical results suggest that no long-run relationship exists among foreign aid inflows and economic growth for both the countries. However, unidirectional causality running from foreign aid to economic growth is indicative in both countries. Therefore, the findings in this paper support the adequate need for foreign aid for effective economic growth amid an upright policy environment, related issues of conditionality and political stability. Our results are robust to independent, and control variables and estimation techniques are also on par with robustness.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

scholarly journals Adults newly infected with hiv in burundi: a box-jenkins arima approach

2020 ◽  
Vol 4 ◽  
pp. 48-56
Author(s):  
Smartson P. NYONI ◽  
Thabani NYONI
Keyword(s):  
Time Series Data ◽  
Hiv Infections ◽  
Series Data ◽  
Annual Number ◽  
Optimal Model ◽  
Applied Model ◽  
Annual Time Series ◽  
And Control ◽  
Blue Print ◽  
Annual Time

Using annual time series data on the number of adults (ages 15 and above) newly infected with HIV in Burundi from 1990 – 2018, the study predicts the annual number of adults who will be newly infected with HIV over the period 2019 – 2025. The study applied the Box-Jenkins ARIMA methodology. The diagnostic ADF tests as well as correlogram analysis show that the G series under consideration is an I (2) variable. Based on the AIC, the study presents the ARIMA (0, 2, 1) model as the optimal model. The residual correlogram and the inverse roots of the applied model further reveal that the presented model is stable and suitable for forecasting new HIV infections in adults in Burundi. The results of the study indicate that the number of new HIV infections in adults in Burundi will most likely decline, over the period 2019 – 2023, from approximately 698 to almost 90 new HIV infections. By 2025, Burundi could experience her first zero new HIV infections in adults! This implies that, despite the fact that Vision Burundi 2025 is a highly ambitious blue-print; Vision Burundi 2025 will largely be achieved as far as HIV/AIDS prevention and control is concerned.

The Dynamics of a Flexible Levitated Droplet

2001 ◽  
Author(s):  
Mihai Dupac ◽  
Dan B. Marghitu ◽  
David G. Beale
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Lyapunov Exponent ◽  
Time Series Data ◽  
Simulated Data ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
Dynamics Analysis ◽  
Attractor Dimension ◽  
Levitated Droplet

Abstract In this paper, a nonlinear dynamics analysis of the simulated data was considered to study the time evolution of an electro-magnetically levitated flexible droplet. The main goals of this work are to study the behavior of the levitated droplet and to investigate its stability. Quantities characterizing time series data such as attractor dimension or largest Lyapunov exponent were computed.

Quantifying Attractor Morphing for High-Sensitivity Detection of Parameter Variations by Point Cloud Averaging

2005 ◽  
Author(s):  
Ali I. Hashmi ◽  
Bogdan I. Epureanu
Keyword(s):  
Atomic Force Microscope ◽  
Point Cloud ◽  
Time Series Data ◽  
Equations Of Motion ◽  
High Sensitivity ◽  
Series Data ◽  
Optimal Basis ◽  
Atomic Force ◽  
Nominal Parameter ◽  
Parameter Values

A novel method of damage detection for systems exhibiting chaotic dynamics is presented. The algorithm reconstructs variations of system parameters without the need for explicit system equations of motion, or knowledge of the nominal parameter values. The concept of a Sensitivity Vector Field (SVF) is developed. This construct captures geometrical deformations of the dynamical attractor of the system in state space. These fields are collected by the means of Point Cloud Averaging (PCA) applied to discrete time series data from the system under healthy (nominal parameter values) and damaged (variations of the parameters) conditions. Test variations are reconstructed from an optimal basis of the SVF snapshots which is generated by means of proper orthogonal decomposition. The method is applied to two system models, a magneto-elastic oscillator and an atomic force microscope. The method is shown to be highly accurate, and capable of identifying multiple simultaneous variations. The success of the method as applied to an atomic force microscope (AFM) and a magneto-elastic oscillator (MEO) indicates a potential for highly accurate sample readings by exploiting recently observed chaotic vibrations.

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