A technique for determination of character and intensity of interaction between the elements of complex systems based on reconstruction of model equations for phase dynamics is extended to the case of short and noisy time series. Corrections, which eliminate systematic errors of the estimates, and expressions for confidence intervals are derived. Analytic results are presented for a particular case of linear uncoupled systems, and their validity for a much wider range of situations is demonstrated with numerical examples. The technique should be useful for the analysis of nonstationary processes in real time, including the situations of significant noise and restrictions on the observation time.
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http://dx.doi.org/10.1103/PhysRevE.68.046209 | DOI Listing |
Bayesian Anal
June 2024
Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
The exponential random graph model (ERGM) is a popular model for social networks, which is known to have an intractable likelihood function. Sampling from the posterior for such a model is a long-standing problem in statistical research. We analyze the performance of the stochastic gradient Langevin dynamics (SGLD) algorithm (also known as noisy Longevin Monte Carlo) in tackling this problem, where the stochastic gradient is calculated via running a short Markov chain (the so-called inner Markov chain in this paper) at each iteration.
View Article and Find Full Text PDFHear Res
January 2025
Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
The cortical tracking of the acoustic envelope is a phenomenon where the brain's electrical activity, as recorded by electroencephalography (EEG) signals, fluctuates in accordance with changes in stimulus intensity (the acoustic envelope of the stimulus). Understanding speech in a noisy background is a key challenge for people with hearing impairments. Speech stimuli are therefore more ecologically valid than clicks, tone pips, or speech tokens (e.
View Article and Find Full Text PDFPediatr Res
January 2025
Division of Growth and Development, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Background: Equipping parents with play skills can foster child development. This study examined the effects of Power of Play Parent Intervention, a short, online, asynchronous, play skill enhancement program, on the frequency of total parent-child play, parents' attitude towards play, and children's screen time in Thailand.
Methods: From September to December 2023, 112 parents of children aged 12-36 months from social media platforms were block-randomized into two-arm, parallel groups (56 intervention and 56 waitlist control).
Sci Rep
January 2025
Advanced Power and Energy Center (APEC), Electrical Engineering Department, Khalifa University, Abu Dhabi, UAE.
Although detailed analytical models for droop-controlled microgrids are available, they are computationally complex and do not consider real-time variations in microgrid parameters and operating conditions. This paper proposes Kurtosis-Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) to identify the dominant modes in droop-controlled inverter-based microgrids (IBMGs) using local real-time measurements. In the proposed approach, a short-duration small disturbance is applied to the selected DG's active power droop gain, and then, the system's dominant modes are estimated from its local measurements.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Industrial Engineering, King Khalid University, Abha, 61421, Saudi Arabia.
The integration of renewable energy sources has resulted in an increasing intricacy in the functioning and organization of power systems. Accurate load forecasting, particularly taking into account dynamic factors like as climatic and socioeconomic impacts, is essential for effective management. Conventional statistical analysis and machine learning methods struggle with accurately capturing the intricate temporal relationships present in load data.
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