An application of bi-directional evolutionary structural optimisation for optimising energy absorbing structures using a material damage model

The Bi-directional Evolutionary Structural Optimisation (BESO) method is a numerical topology optimisation method developed for use in finite element analysis. This paper presents a particular application of the BESO method to optimise the energy absorbing capability of metallic structures. The optimisation objective is to evolve a structural geometry of minimum mass while ensuring that the kinetic energy of an impacting projectile is reduced to a level which prevents perforation. Individual elements in a finite element mesh are deleted when a prescribed damage criterion is exceeded. An energy absorbing structure subjected to projectile impact will fail once the level of damage results in a critical perforation size. It is therefore necessary to constrain an optimisation algorithm from producing such candidate solutions. An algorithm to detect perforation was implemented within a BESO framework which incorporated a ductile material damage model.

Generation of overdense and high-energy electron-positron-pair plasmas by irradiation of a thin foil with two ultraintense lasers

A scheme for enhanced quantum electrodynamics (QED) production of electron-positron-pair plasmas is proposed that uses two ultraintense lasers irradiating a thin solid foil from opposite sides. In the scheme, under a proper matching condition, in addition to the skin-depth emission of gamma-ray photons and Breit-Wheeler creation of pairs on each side of the foil, a large number of high-energy electrons and photons from one side can propagate through it and interact with the laser on the other side, leading to much enhanced gamma-ray emission and pair production. More importantly, the created pairs can be collected later and confined to the center by opposite laser radiation pressures when the foil becomes transparent, resulting in the formation of unprecedentedly overdense and high-energy pair plasmas. Two-dimensional QED particle-in-cell simulations show that electron-positron-pair plasmas with overcritical density 10(22) cm(-3) and a high energy of 100s of MeV are obtained with 10 PW lasers at intensities 10(23) W/cm(2), which are of key significance for laboratory astrophysics studies.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.