International Journal of Modern Physics C

Cluster deposition is a technique recently developed for the production of films of a nanometric thickness. The technique consists on driving a beam of clusters onto a substrate with an energy sufficient to obtain their fragmentation and the adhesion of the fragments to the substrate. Therefore the main mechanisms involved by this technique are (i) cluster fragmentation, (ii) cluster–substrate adhesion and (iii) coalescence of the deposited fragments.

The focus of this study is on these three mechanisms. To illustrate their peculiar features, three structures have been selected. Two of these structures, that is a cluster and a cluster dimer, are free standing. The third one represents a cluster deposited onto a substrate. The temperature response of the two free standing structures illustrates the effects of the cluster geometry and shape on the response to an external energy input. The comparison of these responses with the behavior occurring during deposition illustrates the main properties of cluster–substrate interactions.

The focus of this study is on the stability of grains of metallic oxides, i.e., TiO₂ and SnO₂, with a rutile lattice. To this purpose, the grain binding and fragmentation energies have been evaluated quantum mechanically at semi-empirical level using the extended Debye–Hueckel approximation. The grain size and shape are variable and the shapes have been chosen to reproduce, with some approximation, realistic structures found in nanocrystalline technologies. The results show noticeable differences with respects to the known behavior of homonuclear structures. In fact, the grain stability generally increases with the cluster size, as in the homonuclear case. However, its primary dependence is on the oxygen content, rather than on the grain size. On the contrary, the density of states has a critical dependence on the cluster size and is almost equal for the two materials.

We define some appropriate statistical quantities that indicate the physical significance (reliability) of confidence intervals in the framework of both Frequentist and Bayesian statistical theories. We consider the expectation value of the upper limit in the absence of a signal (that we propose to call "exclusion potential", instead of "sensitivity" as done by Feldman and Cousins) and its standard deviation, we define the "Pull" of a null result, expressing the reliability of an experimental upper limit, and the "upper and lower detection functions", that give information on the possible outcome of an experiment if there is a signal. We also give a new appropriate definition of "sensitivity", that quantifies the capability of an experiment to reveal the signal that is searched for at the given confidence level.

An approach to lattice Boltzmann simulation is described, which makes a direct connection between classical kinetic theory and contemporary lattice Boltzmann modeling methods. This approach can lead to greater accuracy, improved numerical stability and significant reductions in computational needs, while giving a new philosophical point of view to lattice Boltzmann calculations for a large range of applications.

We introduce the basic theory of quantization of radiation field in quantum physics and explain how it relates to the theory of recursive functions in computer science. We outline the basic differences between quantum mechanics (QM) and quantum field theory (QFT) and explain why QFT is better suited for a computational paradigm — based on algorithmic requirement, countably infinite degrees of freedom and the creation of macroscopic output objects. The quanta of the radiation field correspond to the non-negative integers and the harmonic oscillator spectra correspond to the recursive computation — with the creation and annihilation operators, respectively, playing the same role as the successor and predecessor in computability theory. Accordingly, this approach relates the classical computational model and the quantum physical model more directly than the Turing machine approach used earlier. Also, the application of Lambda calculus formalism and the associated denotational semantics (that is widely used in the classical computational paradigm involving recursive functions) for applications to computational paradigm based on quantum field theory is described. Finally, we explain where QFT and conventional paradigm depart from each other, and examine the concept of fixed points, phase transitions, programmability, emergent computation and related open problems.

The social effects of wolf pack survival are simulated by combining the Makowiec model with Stauffer's simple aging model.

We have analyzed possible mechanisms of the crossover to the Gaussian distribution of the logarithmic returns in the Cont–Bouchaud herding model of the stock market. Either the underlying cluster distribution is not in the Lévy attraction regime, or a cut-off effect is responsible for the crossover. The cut-off can be due to the finite size of the system, where clusters are created. If such finite size effects are responsible for the crossover, a delicate interplay between the size dependence of the deviation from the Gaussian and of the number of values to be summed up in one step may result in a size-independent crossover value of the activity. It is shown that this is the case for percolation clusters in spatial dimensions from 2 to 6. A further origin of the cut-off can be the limited number of clusters taken into account.

We consider a number of techniques for storing patterns in orthogonal normal modes of a Hamiltonian system. Such techniques, along with the method of selecting the most excited mode, enable us to introduce a new class of pattern recognition systems that we call Hamiltonian neural networks. In contrast to all traditional neural networks, our Hamiltonian neural networks are nondissipative. Quantization of the Hamiltonian of our network is expected to serve as a means for future work on quantum analogs of classical information processing.

We show that the intermittent collective behavior shown by several CA has the statistical properties of the on–off intermittency. The average transient time for trajectories to fall in a ε neighborhood of the off state scales with ε^-1. The laminar and burst phases are symmetric and the scaling exponent is -3/2 for the distribution of the phases. Also, we show that the size distribution of clusters follows a power law with a scaling exponent of -2.

Based on the concepts of educational psychology, sociology and statistical physics, a mathematical model for a new type of social learning process that takes place when individuals interact via the Internet is proposed and studied. The noise of the interaction (misunderstandings, lack of well organized participative activities, etc.) dramatically restricts the number of individuals that can be efficiently in mutual contact and drives phase transitions between "ordered states" such as the achievements of the individuals are satisfactory and "disordered states" with negligible achievements.

For the variable-coefficient Fisher-type equation, which models such spatial spread as of an advantageous gene in a population or of early farming, we, in this paper, make use of computerized symbolic computation and report a new auto-Bäcklund transformation and a couple of new families of soliton-like solutions. Sample solitary waves are presented as the special cases.

In the present work, we investigate the ability of recently proposed computational operators to characterize the problem of pattern formation by fluid flow and mass transport in a viscous binary fluid mixture under osmosedimentation. We perform a numerical investigation of these patterns using three computational operators (R² → R), computed from the spatial mass distribution, the streamlines, and the velocity fields. Our main goal is to demonstrate the ability of these computational operators to distinguish different dynamical regimes in the complex patterns arising from the osmosedimentation process.