Diffusion in Copper/Cobalt Systems under High Magnetic Fields

Comprehensive research on a high magnetic field’s effect on diffusion is lacking; hence, this study investigates the effect of the magnetization of such a field on diffusion using a copper/cobalt diffusion couple in the diamagnetic/ferromagnetic states, respectively. The diffusion couple was formed using explosive welding to avoid diffusion during manufacturing. The diffusion couple annealed within a temperature range of 1165 –1265 K under a 0–6-T high magnetic field. The angle between the diffusion and magnetic field directions was set as 0° and then 180°. The penetration profiles of cobalt volume diffusion in the copper and grain-boundary diffusion of copper in cobalt were constructed using an electron probe micro analyzer. The high magnetic field increased the volume diffusivity of cobalt in copper, but had no evident effect on the grain-boundary diffusivity of copper in cobalt, irrespective of the magnetic field direction. An Arrhenius plot of the cobalt volume diffusivity in copper demonstrated that the applied high magnetic field enhanced diffusion by changing the frequency factor rather than the activation energy; this can be attributed to the increased diffusion entropy caused by changing the vacancy concentration, which resulted from the introduction of magnetization under a high magnetic field.

Biophotons and Emergence of Quantum Coherence—A Diffusion Entropy Analysis

We study the emission of photons from germinating seeds using an experimental technique designed to detect light of extremely small intensity. We analyze the dark count signal without germinating seeds as well as the photon emission during the germination process. The technique of analysis adopted here, called diffusion entropy analysis (DEA) and originally designed to measure the temporal complexity of astrophysical, sociological and physiological processes, rests on Kolmogorov complexity. The updated version of DEA used in this paper is designed to determine if the signal complexity is generated either by non-ergodic crucial events with a non-stationary correlation function or by the infinite memory of a stationary but non-integrable correlation function or by a mixture of both processes. We find that dark count yields the ordinary scaling, thereby showing that no complexity of either kinds may occur without any seeds in the chamber. In the presence of seeds in the chamber anomalous scaling emerges, reminiscent of that found in neuro-physiological processes. However, this is a mixture of both processes and with the progress of germination the non-ergodic component tends to vanish and complexity becomes dominated by the stationary infinite memory. We illustrate some conjectures ranging from stress induced annihilation of crucial events to the emergence of quantum coherence.

Self-Similar Models: Relationship between the Diffusion Entropy Analysis, Detrended Fluctuation Analysis and Lévy Models

Financial and geophysical data, like many other low and high frequency time series, are known to exhibit some memory effects. These memory effects may be long or short, permanent or temporal depending on the event that is being modeled. The purpose of this study is to investigate the memory effects characterized by the financial market closing values and volcanic eruption time series as well as to investigate the relation between the self-similar models used and the Lévy process. This paper uses highly effective scaling methods including Lévy processes, Detrended Fluctuation Analysis (DFA) and Diffusion Entropy Analysis (DEA) to examine long-range persistence behavior in time series by estimating their respective parameters. We use the parameter of the Lévy process (α) characterizing the data and the scaling parameters of DFA (H) and DEA (δ) characterizing the self-similar property to generate a relationship between the three (3) aforementioned scaling methods. Findings from the numerical simulations confirm the existence of long-range persistence (long-memory behavior) in both the financial and geophysical time series. Furthermore, the numerical results from this study indicates an approximate inverse relationship between the parameter of the Lévy process and the scaling parameters of the DFA and DEA (i.e., H , δ ≈ 1 α ), which we prove analytically.

Diffusion Entropy Analysis: Stability of Indian Stock Market

The large amount of available data of stock markets becomes very beneficial when it is transformed to valuable information. The analysis of this huge data is essential to extract out the useful information. In the present work, we employ the method of diffusion entropy to study time series of different indexes of Indian stock market. We analyze the stability of Nifty50 index of National Stock Exchange (NSE) India and SENSEX index of Bombay Stock Exchange (BSE), India in the vicinity of global financial crisis of 2008. We also apply the technique of diffusion entropy to analyze the stability of Dow Jones Industrial Average (DJIA) index of USA. We compare the results of Indian Stock market with the USA stock market (DJIA index). We conduct an empirical analysis of the stability of Nifty50, Sensex and DJIA indexes. We find significant drop in the value of diffusion entropy of Nifty50, Sensex and DJIA during the period of crisis. Both Indian and USA stock markets show bull market effects in the pre-crisis and post-crisis periods and bear market effect during the period of crisis. Our findings reveal that diffusion entropy technique can replicate the price fluctuations as well as critical events of the stock market.

ATP-Dependent Diffusion Entropy and Homogeneity in Living Cells

Intracellular dynamics is highly complex, and includes diffusion of poly-dispersed objects in a non-homogeneous, out-of-equilibrium medium. Assuming non-equilibrium steady-state, we developed a framework that relates non-equilibrium fluctuations to diffusion, and generalized entropy in cells. We employed imaging of live Jurkat T cells, and showed that active cells have higher diffusion parameters (Kα and α) and entropy relative to the same cells after ATP depletion. Kα and α were related in ATP-depleted cells while this relation was not apparent in untreated cells, probably due to non-equilibrium applied work. Next we evaluated the effect of intracellular diffusion and entropy on the cell content homogeneity, which was displayed by the extent of its liquid–liquid phase separation (LLPS). Correlations between intracellular diffusion parameters, entropy and cell homogeneity could be demonstrated only in active cells while these correlations disappeared after ATP depletion. We conclude that non-equilibrium contributions to diffusivity and entropy by ATP-dependent mechanical work allow cells to control their content homogeneity and LLPS state. Such understanding may enable better intervention in extreme LLPS conditions associated with various cell malignancies and degenerative diseases.