Periodic Behavior and Stochastic Fluctuations of Solar Activity: Proper Orthogonal Decomposition Analysis

We use the Proper Orthogonal Decomposition (POD) to investigate the spatiotemporal features of the solar activity. Daily observation in the period 1949–1996 of the green coronal emission line at 530.3 nm are used as indicators of the activity behavior. We show that few POD modes suffice in describing both the space and time main periodicities. In particular, being affected by a strongly energetic stochastic behavior, daily data are described by five POD modes, while two POD modes are enough to describe the butterfly diagram in monthly averaged data. Apart from the basic period T₀ = 11 years, using daily data we found evidences for intercycle temporal periodicities.     

Scaling law of the reduced magnetic helicity in fast streams

The reduced magnetic helicity is a quantity related to the handedness of the magnetic field fluctuations. In the present paper we study the scaling law of the reduced magnetic helicity in fast streams in the solar wind by using high-resolution magnetic field data by the Ulysses spacecraft. We show that at high frequencies both the left-hand and the right-hand helicity survives, implying that there is no predominance of a single sign. In addition, the scaling law of the magnetic helicity exhibits a strong dependence on the data set analyzed and we do not observe any universal behavior.     

Mineralogical and chemical variations of ochreous precipitates from acid sulphate waters (asw) at the Roşia Montană gold mine (Romania)

The mineralogical and chemical variations of ochreous precipitates forming from acid sulphate waters discharged from the lowest mine adit (“Sf. Cruci din Orlea”) of the Ro?ia Montan? Gold Mine (Romania) were investigated by a multianalytical approach (XRPD, IR, TEM, ICP) applied to surface precipitates and associated waters. The mineralogy of the precipitates changed significantly as a consequence of the variations in the chemical parameters of the circulating solutions (mainly pH, Eh, and sulphate concentrations) which were mainly controlled by mixing with unpolluted waters of Ro?ia River. Ochreous precipitates are characterized by high concentrations of potentially toxic elements (PTEs; in particular Cr, Co, Ni, Cu, Zn, As, Cd, and Pb) and consist of a mixture, in variable proportion, of jarosite and schwertmannite, which represent the stable secondary minerals along the investigated transect of Ro?ia River. Particular regard is given to the ability of authigenic phases to selectively scavenge selected PTEs from contaminated solutions during their genesis and minerogenetic evolution. Furthermore, laboratory kinetic batch experiments on natural heterogeneous samples of ochreous precipitates were carried out to investigate the release processes involving PTEs and to verify the type and the amount of elements that can be temporarily/permanently trapped by the solid phase from the contaminated solutions. The comparative analysis of the precipitates and waters of the Ro?ia Montan? mining area indicated that the role of secondary minerals as “mitigating agents” can be limited because even minor pH–Eh oscillations would cause mineralogical transformations that could lead to trace elements mobilization in the environment.     

Comparing Human and Neural Network Learning of Climate Categories

This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories.     

Intermittency of magnetic turbulence in slow solar wind

We investigate the properties of intermittency of magnetic turbulence by using magnetic field data collected by the Helios spacecraft in the inner heliosphere. Clear scaling laws for magnetic structure functions are visible in periods where the velocity of the bulk plasma is low. Within these periods we found that intermittency of magnetic turbulence is high with respect to velocity field. A comparison with fluid flows where passive scalars are more intermittent than velocity, yields to consider the magnetic field like a “passive vector”.     

Scaling exponents of the velocity structure functions in the interplanetary medium

We analyze the scaling exponents of the velocity structure functions, obtained from the velocity fluctuations measured in the interplanetary space plasma. Using the expression for the energy transfer rate which seems the most relevant in describing the evolution of the pseudo-energy densities in the interplanetary medium, we introduce an energy cascade model derived from a simple fragmentation process, which takes into account the intermittency effect. In the absence and in the presence of the large-scale magnetic field decorrelation effect the model reduces to the fluid and the hydromagnetic p-model, respectively. We show that the scaling exponents of the q-th power of the velocity structure functions, as obtained by the model in the absence of the decorrelation effect, furnishes the best-fit to the data analyzed from the Voyager 2 velocity field measurements at 8.5 AU. Our results allow us to hypothesize a new kind of scale-similarity for magnetohydrodynamic turbulence when the decorrelation effect is at work, related to the fourth-order velocity structure function.