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Simple Binary Prediction of Daily Storm-Level Geomagnetic Activity with Solar Winds and Potential Relevance for Cerebral Function

Caswell J. M., Rouleau N.

International Letters of Chemistry, Physics and Astronomy

2014

Vol. 17, iss. 1

107--113

A number of previous studies have examined various statistical methods for the prediction of geomagnetic activity, particularly based on predictor input of solar wind variables. However, investigation of the potential for a simple binary prediction system based on either “quiet” or storm-level activity of the planetary magnetic field has been severely lacking. The goals of the current analyses were to identify potential space weather models for the accurate prediction of geomagnetic storm events. Furthermore, while the deleterious or negative effects of increases in geomagnetic activity on a range of terrestrial systems have been focused on in the past, theoretical perspectives on the potential benefits of significantly increased geomagnetic perturbations are considered.

Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses

Caswell J. M.

International Letters of Chemistry, Physics and Astronomy

2014

Vol. 15

55--63

Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present study, a combination of wavelet neural networks was adapted from previous research in order to predict daily average values of cosmic ray impulses 30 days in advance. Additional comparison of both neural network and linear regression modelling with and without wavelet decomposition was conducted for further demonstration of increased accuracy with wavelet neural networks in a simple input-output fitting model.

Gravitational and Experimental Electromagnetic Contributions to Cerebral Effects Upon Deviations from Random Number Variations Generated by Electron Tunneling

Caswell J.M., Collins M.W.G., Vares D.A.E., Juden-Kelly L.M., Persinger M.A.

International Letters of Chemistry, Physics and Astronomy

2013

Vol. 11

72--85

New theoretical and traditional quantitative solutions involving a pervasive unit quantum of ~10-20 J within biological and large-scale physical systems predicted that the mass of the human subject, subtle changes in gravitational phenomena, and the energy available within the cerebral volume should affect proximal random number variations produced by electron tunneling. In a series of experiments application of a specific, physiologically-patterned weak magnetic field over the right temporal lobe significantly enhanced the effects of intention upon deviations from random variations created by electron tunneling devices at a distance of 1 m. These variations were strongly (r ~0.80) correlated with the coupling between the forces from the background free oscillations of the earth and the energy differences across the cell width between lunar perigee and apogee. The results support the approach that complex cognitive processes including “intention” can be described by physicochemical parameters and their magnitude of energies are within the range by which interactions or modulations from subtle gravitational forces applied across the cellular membrane and width might occur.