SWARM system

Quantum chemical methods, exemplified by CASSCF and other MCSCF methods, have now evolved to an extent where it is possible to routinely treat accurately the excited electronic states of molecules containing a number of atoms. Mixed nuclear dynamics, such as swarm of trajectory based surface hopping or Ehrenfest dynamics, or the Gaussian wavepacket based multiple spawning method, use an approximate representation of the nuclear wavepacket based on classical trajectories. They are thus able to use the infoiination from quantum chemistry calculations required for the propagation of the nuclei in the form of forces. These methods seem able to reproduce, at least qualitatively, the dynamics of non-adiabatic systems. Test calculations have now been run using duect dynamics, and these show that even a small number of trajectories is able to produce useful mechanistic infomiation about the photochemistry of a system. In some cases it is even possible to extract some quantitative information. 

Swarm is a multi-agent simulation platform for the study of complex adaptive systems. It is currently under development at the Santa Fe Institute ... 

This section is based on the papers An Overview of the Swann simulation system, by 94 Swarm Team, Santa Pe Institute and The Swarm simulation. system and individual-based modeling, by D. Hiebler. 

Swarm has been intentionally designed to include as few ad-hoc assumptions about the design of a complex system as possible, so as to provide a convenient, reliable and standardized set of software tools that can be tailored by researchers to specific systems. 

Hierarchical Structure. In order to be better able to simulate the hierarchical nature of many real-world complex systems, in which agent behavior can itself be best described as being the result of the collective behavior of some swarm of constituent agents. Swarm is designed so that agents themselves can be swarms of other agents. Moreover, Swarm is designed around a time hierarchy, Thus, Swarm is both a nested hierarchy of swarms and a nested hierarchy of schedules. 

Only one publication on gas-liquid mass transfer in bubble-column slurry reactors has come to the author s attention. However, a relatively large volume of information regarding mass transfer between single bubbles or bubble swarms and pure liquid containing no suspended solids is available, and this information is probably of some relevance to the analysis of systems... 

Most theoretical studies of heat or mass transfer in dispersions have been limited to studies of a single spherical bubble moving steadily under the influence of gravity in a clean system. It is clear, however, that swarms of suspended bubbles, usually entrained by turbulent eddies, have local relative velocities with respect to the continuous phase different from that derived for the case of a steady rise of a single bubble. This is mainly due to the fact that in an ensemble of bubbles the distributions of velocities, temperatures, and concentrations in the vicinity of one bubble are influenced by its neighbors. It is therefore logical to assume that in the case of dispersions the relative velocities and transfer rates depend on quantities characterizing an ensemble of bubbles. For the case of uniformly distributed bubbles, the dispersed-phase volume fraction O, particle-size distribution, and residence-time distribution are such quantities. 

Any colony optimization (ACO) and swarm intelligence are forms of agent-based modeling inspired by colonies of social animals such as ants and bees [32]. ACO has become popular in engineering for optimal routing in water distribution systems [33, 34]. Particle swarm optimization has been successfully used to train ANNs, for instance, ANNs to predict river water levels [35], for parameter estimation, for example, in hydrology [36]. 

This result can also be applied directly to coarse particle swarms. For fine particle systems, the suspending fluid properties are assumed to be modified by the fines in suspension, which necessitates modifying the fluid properties in the definitions of the Reynolds and Archimedes numbers accordingly. Furthermore, because the particle drag is a direct function of the local relative velocity between the fluid and the solid (the interstitial relative velocity, Fr), it is this velocity that must be used in the drag equations (e.g., the modified Dallavalle equation). Since Vr = Vs/(1 — Reynolds number and drag coefficient for the suspension (e.g., the particle swarm ) are (after Barnea and Mizrahi, 1973) ... 

The immune system is equal in complexity to the combined intricacies of the brain and nervous system. The success of the immune system in defending the body relies on a dynamic regulatory communications network consisting of millions and millions of cells. Organized into sets and subsets, these cells pass information back and forth like clouds of bees swarming around a hive. The result is a sensitive system of checks and balances that produces an immune response that is prompt, appropriate, effective, and self-limiting. 

Self-organization systems under kinetic control (biological systems with genomic, enzymatic and/or evolutionary control), such as protein biosynthesis, virus assembly, formation of beehive and anthill, swarm intelligence. 

The Seven Mysteries of Life by Guy Murchie Butterfly Economics A New General Theory of Social and Economic Behavior by Paul Omerod Paul Ormerod, Swarm Intelligence From Natural to Artificial Systems by Eric Bonabeau, Marco Dorigo Guy Theraulaz, Hidden Order How Adaptation Builds Complexity by John H. Holland Heather Mimnaugh, Turtles, Termites, and Traffic Jams by Mitchel Resnick The Evolution of Cooperation by Robert Axelrod. 

This method is applicable when data are to be inspected and characterized. PCA is easily understood by graphical illustrations, for example, by a two-dimensional co-ordinate system with a number of points in it (Figure 6.25). The first principal component (PC) is the line with the closest fit to these points [12]. Unless the point swarm has, for example, the shape of a circle, the position of the first PC is unambiguous. Because the first PC is the line of closest fit, it is also the line that explains most of the variation (maximum variance) in the data [13]. Therefore it is called the principal component. 

Let s first look at the benefits breast milk offers your baby. Then we ll examine the contamination issue. Breast milk is not just food. It is also medicine. It swarms with antibodies and white blood cells drawn from your own body. By drinking it, your infant comes to share your immune system. And benefits mightily from it. Breastfed infants have lower rates of hospitalization and death. They develop fewer respiratory infections,... 

However, the total dissociation wavefunction is useful in order to visualize the overall dissociation path in the upper electronic state as illustrated in Figure 2.3(a) for the two-dimensional model system. The variation of the center of the wavefunction with r intriguingly illustrates the substantial vibrational excitation of the product in this case. As we will demonstrate in Chapter 5, I tot closely resembles a swarm of classical trajectories launched in the vicinity of the ground-state equilibrium. Furthermore, we will prove in Chapter 4 that the total dissociation function is the Fourier transform of the evolving wavepacket in the time-dependent formulation of photodissociation. The evolving wavepacket, the swarm of classical trajectories, and the total dissociation wavefunction all lead to the same general picture of the dissociation process. 

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