Livres sur le sujet « Resolved particles »

The intense Cambridge–Vienna controversy, which was carried out in the literature and in private correspondence, lasted six years. It was resolved in December 1928, when Chadwick visited Meyer’s institute in Vienna and found that under Pettersson and Kirsch’s influence their women scintillation counters had fallen prey to a misleading psychological effect. That was never published in the literature, however, so outsiders could only sense that something had gone seriously wrong in Meyer’s institute, which greatly affected its scientific reputation. The major positive consequence of the controversy was that it encouraged the further development of electrical techniques for counting charged particles to replace human scintillation counters.

The technique of small angle solution scattering has been revolutionized in the last two decades. Exponential increases in computing power, parallel algorithm development, and the development of synchrotron, free-electron X-ray sources, and neutron sources, have combined to allow new classes of studies for biological specimens. These include time-resolved experiments in which functional motions of proteins are monitored on a picosecond timescale, and the first steps towards determining actual electron density fluctuations within particles. In addition, more traditional experiments involving the determination of size and shape, and contrast matching that isolate substructures such as nucleic acid, have become much more straightforward to carry out, and simultaneously require much less material. These new capabilities have sparked an upsurge of interest in solution scattering on the part of investigators in related disciplines. Thus, this book seeks to guide structural biologists to understand the basics of small angle solution scattering in both the X-ray and neutron case, to appreciate its strengths, and to be cognizant of its limitations. It is also directed at those who have a general interest in its potential. The book focuses on three areas: theory, practical aspects and applications, and the potential of developing areas. It is an introduction and guide to the field but not a comprehensive treatment of all the potential applications.

A brief introduction to particle physics and quantum field theory (QFT) is presented in the main thread of chapter 5. The impasse of unification in particle physics is historically reviewed, showing that the dynamical paradigm pervades the development of particle physics and QFT. Thus, as with the conundrums of general relativity and quantum mechanics, dynamical explanation in the mechanical universe is responsible for the impasse regarding unification in particle physics as per QFT. It is shown that RBW’s adynamical approach provides an entirely new view of unification and particle physics. Philosophy of Physics for Chapter 5 uses RBW to resolve the interpretational issues of gauge invariance, gauge fixing, the Aharonov–Bohm effect, regularization, and renormalization, and largely discharges the problems of Poincaré invariance in a graphical approach, inequivalent representations, and Haag’s theorem. Foundational Physics for Chapter 5 shows how classical field theory is related to QFT and introduces gauge fields per QFT.

Chapter 11 introduces the essential mystery of quantum mechanics, shows how it is resolved, and then goes on to examine related phenomena. The mystery is how individual photons, not electromagnetic waves, can give rise to the interference pattern seen in the two-slit experiment. They are shown to do so because the relevant quantum amplitude is a linear combination of two terms, leading to the pattern when neither slit is identified as the one the photon went through, but collapsing to one term and no pattern if the slit is identified. Similar results are shown to occur when individual electrons are incident on the two slits, and when large molecules, which have de Broglie wavelengths, are scattered by diffraction gratings. Many other interference experiments are described, including those where a photon was shown to be in two places simultaneously and one where a photon displayed both wave and particle features.

The scenario of heavy-ion reactions around the Fermi energy is explored. The quantum BUU equation is solved numerically with and without nonlocal corrections and the effect of nonlocal corrections on experimental values is calculated. A practical recipe is presented which allows reproducing the correct asymptotes of scattering by acting on the point of closest approach. The better description of dynamical correlations by the nonlocal kinetic equation is demonstrated by an enhancement of the high-energy part of the particle spectra and the enhancement of mid-rapidity charge distributions. The time-resolved solution shows the enhancement of neck formation. It is shown that the dissipated energy increases due to the nonlocal collision scenario which is responsible for the observed effects and not due to the enhancement of collisions. As final result, a method is presented how to incorporate the effective mass and quasiparticle renormalisation with the help of the nonlocal simulation scenario.

Chapter 13 addresses Bose condensation in superfluids (and superconductors), which involves the field operator ψ‎ having a c-number component (<ψ(x,t)>≠0), challenging number conservation. The nonlinear Gross-Pitaevskii equation is derived for this condensate wave function<ψ>=ψ−ψ˜, facilitating identification of the coherence length and the core region of vortex motion. The noncondensate Green’s function G˜1(1,1′)=−i<(ψ˜(1)ψ˜+(1′))+> and the nonvanishing anomalous correlation function F˜∗(2,1′)=−i<(ψ˜+(2)ψ˜+(1′))+> describe the dynamics and elementary excitations of the non-condensate states and are discussed in conjunction with Landau’s criterion for viscosity. Associated concepts of off-diagonal long-range order and the interpretation of <ψ> as a superfluid order parameter are also introduced. Anderson’s Bose-condensed state, as a phase-coherent wave packet superposition of number states, resolves issues of number conservation. Superconductivity involves bound Cooper pairs of electrons capable of Bose condensation and superfluid behavior. Correspondingly, the two-particle Green’s function has a term involving a product of anomalous bound-Cooper-pair condensate wave functions of the type F(1,2)=−i<(ψ(1)ψ(2))+>≠0, such that G2(1,2;1′,2′)=F(1,2)F+(1′,2′)+G˜2(1,2;1′,2′). Here, G˜2 describes the dynamics/excitations of the non-superfluid-condensate states, while nonvanishing F,F+ represent a phase-coherent wave packet superposition of Cooper-pair number states and off-diagonal long range order. Employing this form of G2 in the G1-equation couples the condensed state with the non-condensate excitations. Taken jointly with the dynamical equation for F(1,2), this leads to the Gorkov equations, encompassing the Bardeen–Cooper–Schrieffer (BCS) energy gap, critical temperature, and Bogoliubov-de Gennes eigenfunction Bogoliubons. Superconductor thermodynamics and critical magnetic field are discussed. For a weak magnetic field, the Gorkov-equations lead to Ginzburg–Landau theory and a nonlinear Schrödinger-like equation for the pair wave function and the associated supercurrent, along with identification of the Cooper pair density. Furthermore, Chapter 13 addresses the apparent lack of gauge invariance of London theory with an elegant variational analysis involving re-gauging the potentials, yielding a manifestly gauge invariant generalization of the London equation. Consistency with the equation of continuity implies the existence of Anderson’s acoustic normal mode, which is supplanted by the plasmon for Coulomb interaction. Type II superconductors and the penetration (and interaction) of quantized magnetic flux lines are also discussed. Finally, Chapter 13 addresses Josephson tunneling between superconductors.

Cuando hablamos de optimización en el ámbito de las ciencias de la computación hacemos referencia al mismo concepto coloquial asociado a esa palabra, la concreción de un objetivo utilizando la menor cantidad de recursos disponibles, o en una visión similar, la obtención del mejor objetivo posible utilizando todos los recursos con lo que se cuenta. Los métodos para encontrar la mejor solución (óptima) varían de acuerdo a la complejidad del problema enfrentado. Para problemas triviales, el cerebro humano posee la capacidad de resolverlos (encontrar la mejor solución) directamente, pero a medida que aumenta la complejidad del problema, se hace necesario contar con herramientas adicionales. En esta dirección, existe una amplia variedad de técnicas para resolver problemas complejos. Dentro de estas técnicas, podemos mencionar las técnicas exactas. Este tipo de algoritmos son capaces de encontrar las soluciones óptimas a un problema dado en una cantidad finita de tiempo. Como contrapartida, requiere que el problema a resolver cumpla con condiciones bastante restrictivas. Existen además un conjunto muy amplio de técnica aproximadas, conocidas como metaheurísticas. Estas técnicas se caracterizan por integrar de diversas maneras procedimientos de mejora local y estrategias de alto nivel para crear un proceso capaz de escapar de óptimos locales y realizar una búsqueda robusta en el espacio de búsqueda del problema. En su evolución, estos métodos han incorporado diferentes estrategias para evitar la convergencia a óptimos locales, especialmente en espacios de búsqueda complejos. Este tipo de procedimientos tienen como principal característica que son aplicables a cualquier tipo de problemas, sin requerir ninguna condición particular a cumplir por los mismos. Estas técnicas no garantizan en ningún caso la obtención de los valores óptimos de los problemas en cuestión, pero se ha demostrado que son capaces de alcanzar muy buenos valores de soluciones en períodos de tiempo cortos. Además, es posible aplicarlas a problemas de diferentes tipos sin mayores modificaciones, mostrando su robustez y su amplio espectro de uso. La mayoría de estas técnicas están inspiradas en procesos biológicos y/o físicos, y tratan de simular el comportamiento propio de estos procesos que favorecen la búsqueda y detección de soluciones mejores en forma iterativa. La más difundida de estas técnicas son los algoritmos genéticos, basados en el mecanismo de evolución natural de las especies. Existen diferentes tipos de problemas, y multitud de taxonomías para clasificar los mismos. En el alcance de este trabajo nos interesa diferenciar los problemas en cuanto a la cantidad de objetivos a optimizar. Con esta consideración en mente, surge una primera clasificación evidente, los problemas mono-objetivo, donde existe solo una función objetivo a optimizar, y los problemas multi-objetivo donde existe más de una función objetivo. En el presente trabajo se estudia la utilización de metaheurísticas evolutivas para la resolución de problemas complejos, con uno y con más de un objetivo. Se efectúa un análisis del estado de situación en la materia, y se proponen nuevas variantes de algoritmos existentes, validando que las mismas mejoran resultados reportados en la literatura. En una primera instancia, se propone una mejora a la versión canónica y mono-objetivo del algoritmo PSO, luego de un estudio detallado del patrón de movimientos de las partículas en el espacio de soluciones. Estas mejoras se proponen en las versiones de PSO para espacios continuos y para espacios binarios. Asimismo, se analiza la implementación de una versión paralela de esta técnica evolutiva. Como segunda contribución, se plantea una nueva versión de un algoritmo PSO multiobjetivo (MOPSO Multi Objective Particle Swarm Optimization) incorporando la posibilidad de variar dinámicamente el tamaño de la población, lo que constituye una contribución innovadora en problemas con mas de una función objetivo. Por último, se utilizan las técnicas representativas del estado del arte en optimización multi-objetivo aplicando estos métodos a la problemática de una empresa de emergencias médicas y atención de consultas domiciliarias. Se logró poner en marcha un proceso de asignación de móviles a prestaciones médicas basado en metaheurísticas, logrando optimizar el proceso de asignación de móviles médicos a prestaciones médicas en la principal compañía de esta industria a nivel nacional.