Data Mining - Concepts and Techniques.

Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Traffic and related self-driven many-particle systems

In this chapter, we will restrict our attention to the ferrites and a few other closely related materials. The great interest in ferrites stems from their unique combination of a spontaneous magnetization and a high electrical resistivity. The observed magnetization results from the difference in the magnetizations of two non-equivalent sub-lattices of the magnetic ions in the crystal structure. Materials of this type should strictly be designated as “ferrimagnetic” and in some respects are more closely related to anti-ferromagnetic substances than they are to ferromagnetics in which the magnetization results from the parallel alignment of all the magnetic moments present. We shall not adhere to this special nomenclature except to emphasize effects, which are due to the existence of the sub-lattices.

Ferromagnetic materials

This thesis concerns the discovery of superconductivity in the intercalated graphite compounds C6Yb and C6Ca. A novel technique for synthesis of these intercalates has been developed, and is presented in detail. These two materials are shown to superconduct at 6.5K and 11.5K respectively. The superconductivity is demonstrated by measurements of the magnetisation and resistivity. Initial measurements of the superconducting transition of these materials as a function of pressure shows an increase in the transition with increasing pressure.

Superconductivity in the intercalated graphite compounds C6Yb and C6Ca

Knowledge and foundational understanding of phenomena associated with the behavior of materials at the nanoscale is one of the key scientific challenges toward a sustainable energy future. Size reduction from bulk to the nanoscale leads to a variety of exciting and anomalous phenomena due to enhanced surface-to-volume ratio, reduced transport length, and tunable nanointerfaces. Nanostructured metal hydrides are an important class of materials with significant potential for energy storage applications. Hydrogen storage in nanoscale metal hydrides has been recognized as a potentially transformative technology, and the field is now growing steadily due to the ability to tune the material properties more independently and drastically compared to those of their bulk counterparts. The numerous advantages of nanostructured metal hydrides compared to bulk include improved reversibility, altered heats of hydrogen absorption/desorption, nanointerfacial reaction pathways with faster rates, and new surface states cap...

Nanostructured Metal Hydrides for Hydrogen Storage.

Band-gap engineering of SnO2

Monte Carlo simulation study of magnetocaloric effect in NdMnO 3 perovskite

The magnetic properties of a decorated Ising nanotube examined by the use of the Monte Carlo simulations

On the basis of ab initio calculations (FPLO) and Monte Carlo Simulations (MCS) the phase diagrams and magnetic properties of the bulk perovskite LaMnO3 have been studied, using the Heisenberg model It is shown, using ab initio calculations in the scalar relativistic scheme, that the stable phase is the antiferromagnetic A-type, which corresponds to ferromagnetic order of the manganese ions in the basal planes ( a , b ) and antiferromagnetic order of these ions between these planes along the c axis Using the full four-component relativistic scheme, in order to calculate the magnetic anisotropy energy and constants, it is found that the favorable magnetic direction is the (010) b axis The transition temperatures and the critical exponents are obtained in the framework of Monte Carlo simulations The magnetic anisotropy and the exchange couplings of the Heisenberg model are deduced from ab initio calculations They lead, by using Monte Carlo simulations, to a quantitative agreement with the experimental transition temperatures

A. Benyoussef

Papers

Band-gap engineering of SnO2

Monte Carlo simulation study of magnetocaloric effect in NdMnO 3 perovskite

The magnetic properties of a decorated Ising nanotube examined by the use of the Monte Carlo simulations

Monte Carlo study of phase transitions and magnetic properties of LaMnO 3 : Heisenberg model

Structural, optical and magnetic characterizations of Mn-doped MgO nanoparticles