Generation and optimization of electron currents along the walls of a conical target for fast ignition

The interaction of an ultraintense laser pulse with a conical target is studied by means of numerical particle-in-cell simulations in the context of fast ignition. The divergence of the fast electron beam generated at the tip of the cone has been shown to be a crucial parameter for the efficient coupling of the ignition laser pulse to the precompressed fusion pellet. In this paper, we demonstrate that a focused hot electron beam is produced at the cone tip, provided that electron currents flowing along the surfaces of the cone sidewalls are efficiently generated. The influence of various interaction parameters over the formation of these wall currents is investigated. It is found that the strength of the electron flows is enhanced for high laser intensities, low density targets, and steep density gradients inside the cone. The hot electron energy distribution obeys a power law for energies of up to a few MeV, with the addition of a high-energy Maxwellian tail.

Molecular-assisted immunohistochemical optimization

Immunohistochemistry (IHC) is an essential tool in diagnostic surgical pathology, allowing analysis of protein subcellular localization The use of IHC by different laboratories has lead to inconsistencies in published literature for several antibodies, due to either interpretative (inter-observer venation) or technical reasons These disparities have major implications in both clinical and research settings In this study, we report our experience conducting an IHC optimization of antibodies against five proteins previously identified by proteomic analysis to be breast cancer biomarkers, namely 6PGL (PGLS), CAZ2 (CAPZA2), PA2G4 (EBP1) PSD2 and TKT Large variations in the immunolocalizations and intensities were observed when manipulating the antigen retrieval method and primary antibody incubation concentration However, the use of an independent molecular analysis method provided a clear indication in choosing the appropriate biologically and functionally relevant

Two-stage RBF network construction based on particle swarm optimization

The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.

Complex fiber bundle model for optimization of heterogeneous materials

We propose a complex fiber bundle model for the optimization of heterogeneous materials, which consists of many simple bundles. We also present an exact and compact recursion relation for the failure probability of a simple fiber bundle model with local load sharing, which is more efficient than the ones reported previously. Using a ''renormalization method'' and the recursion relation developed for the simple bundle, we calculate the failure probabilities of the complex fiber bundle. When the total number of fibers is given, we find that there exists an optimum way to organize the complex bundle, in which one gets a stronger bundle than in other ways.