Suppression of growth by multiplicative white noise in a parametric resonant system

The author studied the growth of the amplitude in a Mathieu-like equation with multiplicative white noise. The approximate value of the exponent at the extremum on parametric resonance regions was obtained theoretically by introducing the width of time interval, and the exponents were calculated numerically by solving the stochastic differential equations by a symplectic numerical method. The Mathieu-like equation contains a parameter $\alpha$ that is determined by the intensity of noise and the strength of the coupling between the variable and the noise. The value of $\alpha$ was restricted not to be negative without loss of generality. It was shown that the exponent decreases with $\alpha$, reaches a minimum and increases after that. It was also found that the exponent as a function of $\alpha$ has only one minimum at $\alpha \neq 0$ on parametric resonance regions of $\alpha = 0$. This minimum value is obtained theoretically and numerically. The existence of the minimum at $\alpha \neq 0$ indicates the suppression of the growth by multiplicative white noise.Comment: The title and the description in the manuscript are change

Ion association in concentrated NaCI brines from ambient to supercritical conditions: results from classical molecular dynamics simulations

Highly concentrated NaCl brines are important geothermal fluids; chloride complexation of metals in such brines increases the solubility of minerals and plays a fundamental role in the genesis of hydrothermal ore deposits. There is experimental evidence that the molecular nature of the NaCl–water system changes over the pressure–temperature range of the Earth's crust. A transition of concentrated NaCl–H(2)O brines to a "hydrous molten salt" at high P and T has been argued to stabilize an aqueous fluid phase in the deep crust. In this work, we have done molecular dynamic simulations using classical potentials to determine the nature of concentrated (0.5–16 m) NaCl–water mixtures under ambient (25°C, 1 bar), hydrothermal (325°C, 1 kbar) and deep crustal (625°C, 15 kbar) conditions. We used the well-established SPCE model for water together with the Smith and Dang Lennard-Jones potentials for the ions (J. Chem. Phys., 1994, 100, 3757). With increasing temperature at 1 kbar, the dielectric constant of water decreases to give extensive ion-association and the formation of polyatomic (Na(n)Cl(m))(n-m )clusters in addition to simple NaCl ion pairs. Large polyatomic (Na(n)Cl(m))(n-m )clusters resemble what would be expected in a hydrous NaCl melt in which water and NaCl were completely miscible. Although ion association decreases with pressure, temperatures of 625°C are not enough to overcome pressures of 15 kbar; consequently, there is still enhanced Na–Cl association in brines under deep crustal conditions

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology

MEG Network Differences between Low- and High-Grade Glioma Related to Epilepsy and Cognition

OBJECTIVE: To reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups. METHODS: We constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients. RESULTS: LGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4-8 Hz), similar to NGL patients. HGG patients' networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients. CONCLUSION: Lesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients' networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline

Limitations of the rigid planar nonpolarizable models of water.

We analyzed the ability of variants of the SPC/E and TIP4P types of water models to describe the temperature dependence of their second virial coefficients, liquid-vapor phase envelopes, and corresponding coexistence vapor pressure. We complete the characterization of the two most promising models by testing their adequacy to predict the structure of the 13 known crystalline phases of ice by (Parrinello-Rahman) isothermal-isobaric Monte Carlo simulations. While these models perform well for the description of properties to which their force fields were fitted (density, heat of vaporization, structure at the level of pair correlations), their transferability to the entire phase diagram is unsatisfactory, i.e., none could significantly mitigate the shortcomings of the original models. In fact, the most appropriate alternative appears to be the TIP4P-EW model, i.e., the recent reparametrization of the original TIP4P water model. Model parametrizations aimed at improving the description of ice behavior fail even in the description of the liquid phase

Computer simulation of the 13 crystalline phases of ice.

As a reference for follow-up studies toward more accurate model parametrizations, we performed molecular-dynamics and Monte Carlo simulations for all known crystalline phases of ice, as described by the simple point-charge/extended and TIP4P water models. We started from the measured structures, densities, and temperatures, and carried out classical canonical simulations for all these arrangements. All simulated samples were cooled down close to 0 K to facilitate the comparison with theoretical estimates. We determined configurational internal energies as well as pressures, and monitored how accurately the measured configurations were preserved during the simulations. While these two models predicted very similar thermophysical and structural properties for water at ambient conditions, the predicted features for the corresponding ice polymorphs may differ significantly