Random noise stimulation technique involves applying any form of energy (for instance, light, mechanical, electrical, sound) with unpredictable intensities through time to the brain or sensory receptors to enhance sensory, motor, or cognitive functions. Random noise stimulation initially employed mechanical noise in auditory and cutaneous stimuli, but electrical energies applied to the brain or the skin are becoming more frequent, with a series of clinical applications. Indeed, recent evidence shows that transcranial random noise stimulation can increase corticospinal excitability, improve cognitive/motor performance, and produce beneficial aftereffects at the behavioral and psychological levels. Here, we present a narrative review about the potential uses of random noise stimulation to treat neurological disorders, including attention deficit hyperactivity disorder, schizophrenia, amblyopia, myopia, tinnitus, multiple sclerosis, post-stroke, vestibular-postural disorders, and sensitivity loss. Many of the reviewed studies reveal that the optimal way to deliver random noise stimulation-based therapies is with the concomitant use of neurological and neuropsychological assessments to validate the beneficial aftereffects. In addition, we highlight the requirement of more randomized controlled trials and more physiological studies of random noise stimulation to discover another optimal way to perform the random noise stimulation interventions.
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http://dx.doi.org/10.4103/1673-5374.339474 | DOI Listing |
Sci Rep
January 2025
School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior.
View Article and Find Full Text PDFFor indirect time-of-flight (iToF) cameras, we proposed a modeling approach focused on addressing random error. Our model characterizes random error comprehensively by detailing the propagation of error introduced by signal light, ambient light, and dark noise through phase calculation and system correction processes. This framework leverages correlations between incident light and tap responses to quantify noise impacts accurately.
View Article and Find Full Text PDFUltrashort pulses experience random quantum motion as they propagate through a mode-locked laser cavity, a phenomenon that inevitably affects the recently introduced pure-quartic solitons. Investigating this process is essential, as quantum-limited noise establishes fundamental performance limits for their application. To date, studies on quantum diffusion and the resulting timing jitter of these solitons remain sparse.
View Article and Find Full Text PDFWe demonstrate a wide-tunable random fiber laser (RFL) with narrow linewidth and low noise. The tunable RFL is achieved by combining random feedback from a disordered fiber Bragg grating array (FBGA) with a broad scattering wavelength range and the gain from an erbium-doped fiber (EDF) with a broad amplification wavelength range. The disordered FBGA is fabricated using a femtosecond laser direct writing technique by varying the random distances and grating periods.
View Article and Find Full Text PDFJ Environ Manage
January 2025
School of Economics and Management, North China Electric Power University, Beijing, China. Electronic address:
In order to reduce the unpredictability of carbon prices caused by their increasingly prominent environmental and market attributes, and to minimize their negative impact on carbon trading, further research on forecasting models for carbon price is urgently needed. To improve the accuracy of prediction, this paper proposes a carbon price forecasting method based on SSA-NSTransformer. The method includes four main steps: Firstly, decomposition of carbon price signals, using Singular Spectrum Analysis to remove noise signals; Secondly, analysis of influencing factors, using Random Forest to identify and select key influencing factors of carbon price signal components from energy price, financial market, socio-economic, and environmental aspects; Furthermore, influencing factors prediction, considering the impact of different carbon reduction targets and predicting future trends of influencing factors; And finally, carbon price prediction, considering the impact of factors based on multi-stage carbon reduction targets, using Non-stationary Transformer to predict the signal components of carbon prices, reconstructing the carbon price time series, and testing the model accuracy.
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