Gravity Theory-Based Affinity Propagation Clustering Algorithm and Its Applications

The original Affinity Propagation clustering algorithm (AP) only used the Euclidean distance of data sample as the only standard for similarity calculation. This method of calculation had great limitations for data with high dimension and sparsity when the original algorithm was running. Due to the single calculation method of similarity, the convergence and clustering accuracy of the algorithm were greatly affected. On the other hand, in the universe, we can consider the formation of galaxies is a clustering process. In addition, the interaction between different celestial bodies are achieved through universal gravitation. This paper introduced the Density Peak clustering algorithm (DP) and gravitational thought into the AP algorithm, and constructed the density property to calculate the similarity, put forward the Affinity Propagation clustering algorithm based on Gravity (GAP). The proposed algorithm was more accurate to calculate similarity of simple points through the local density of corresponding points, and then used the gravity formula to update the similarity matrix. The data clustering process could be seen as the sample points spontaneously attract each other based on ‘gravitation’. Experimental results showed that the convergence performance of GAP algorithm is obviously improved over the AP algorithm, and the clustering effect was better

On the nature, formation and diversity of particulate coherent structures in microgravity conditions and their relevance to materials science and problems of astrophysical interest

Different phenomena related to the spontaneous accumulation of solid particles dispersed in a fluid medium in microgravity conditions are discussed, with an emphasis on recent discoveries and potential links with the general field of astrophysical fluid-dynamics on the one hand, and with terrestrial applications in the field of materials science on the other hand. With special attention to the typical physical forces at play in such an environment, namely, surface-tension gradients, oscillatory residual gravity components, inertial disturbances and forces of an electrostatic nature, specific experimental and numerical examples are presented to provide inputs for an increased understanding of the underlying cause-and-effect relationships. Studying these systems can be seen as a matter of understanding how macroscopic scenarios arise from the cooperative behaviour of sub-parts or competing mechanisms (nonlinearities and interdependencies on various spatial and temporal scales). Through a critical assessment of the properties displayed by the resulting structures (which appear in the form of one-dimensional circuits formed by aligned particles, planar accumulation surfaces, three-dimensional compact structures resembling “quadrics”, micro-crystallites or fractal aggregates), we discuss a possible classification of the related particle attractors in the space of parameters according to the prevailing effect

Control and manipulation of nanoparticles for fabrication of metal matrix composites

The mechanical properties of composite materials are mainly determined by their microstructures that depend on comprising phases and their properties, the shape and size of those phases, and their distribution. By controlling and optimizing the various aspects of the microstructure, composites with improved mechanical properties can be created. One of the challenges, however, is the lack of scalable fabrication method capable of making complex structures. The conventional fabrication techniques for MMCs have been limited to fabricating simple structures with homogeneous dispersion of constituents. In this work, various fabrication approaches that can control the microstructure in metal matrix reinforced with nanoparticles have been studied. Mechanical alloying (ball milling) was used to control the dispersion of graphene sheets in homogeneous reinforced aluminum composites. Spray assisted deposition of nanoparticles was used to fabricate layered composites with uniformly and hierarchically reinforced interfaces. Magnetic field assisted deposition was studied to manipulate and deposit nanoparticles into micro-patterns that can be used to create hierarchically layered composites. Homogeneously reinforced aluminum alloy (Al6061) reinforced composites with graphene have been synthesized using mechanical alloying followed by semisolid sintering. The ball milling was used to control the dispersion as well as the cluster size of the graphene within the matrix. The effect of ball milling time on the fabricated composites was studied. A significant enhancement in the mechanical properties of the graphene reinforced composites was observed compared with the matrix material processed at the same condition. Layered composites, which are uniformly or hierarchically reinforced at the interfaces, have been synthesized by implementing two processing concepts: spray assisted deposition and metallurgy (semi-solid sintering). Ultrasonic spray deposition creates nano-/micro-/meso-scale patterns on metallic sheets, which are then stacked together, densified, and synthesized into a composite through pressure assisted semi-solid sintering process. Silicon carbide (SiC) nanoparticle reinforced lightweight alloys (i.e. Magnesium Alloy (AZ31) and Al6061) have been synthesized. The synthesized composites showed an improvement in the strength with minor decrease on the total elongation. Magnetic field directed manipulation of nanoparticles was demonstrated to self-assemble and deposit nanoparticles into user-defined micro-patterns on Al substrate for potential use in synthesis of hierarchically structure layered composites. The magnetic field was modulated by machining (e.g. micro-milling and laser machining) user-defined pattern of protrusions on the magnetic source surface. The deposition of magnetic particles as well as mixtures of magnetic and nonmagnetic nanoparticles was studied

Antenna near-field wave studies in magnetized plasmas as part of the optimization of auxiliary heating in fusion devicesAntenna near-field wave studies in magnetized plasmas as part of the optimization of auxiliary heating in fusion devices

C

Physics of Neutron Star Crusts

The physics of neutron star crusts is vast, involving many different research fields, from nuclear and condensed matter physics to general relativity. This review summarizes the progress, which has been achieved over the last few years, in modeling neutron star crusts, both at the microscopic and macroscopic levels. The confrontation of these theoretical models with observations is also briefly discussed.Comment: 182 pages, published version available at <http://www.livingreviews.org/lrr-2008-10

Unraveling the intricacies of spatial organization of the ErbB receptors and downstream signaling pathways

Faced with the complexity of diseases such as cancer which has 1012 mutations, altering gene expression, and disrupting regulatory networks, there has been a paradigm shift in the biological sciences and what has emerged is a much more quantitative field of biology. Mathematical modeling can aid in biological discovery with the development of predictive models that provide future direction for experimentalist. In this work, I have contributed to the development of novel computational approaches which explore mechanisms of receptor aggregation and predict the effects of downstream signaling. The coupled spatial non-spatial simulation algorithm, CSNSA is a tool that I took part in developing, which implements a spatial kinetic Monte Carlo for capturing receptor interactions on the cell membrane with Gillespies stochastic simulation algorithm, SSA, for temporal cytosolic interactions. Using this framework we determine that receptor clustering significantly enhances downstream signaling. In the next study the goal was to understand mechanisms of clustering. Cytoskeletal interactions with mobile proteins are known to hinder diffusion. Using a Monte Carlo approach we simulate these interactions, determining at what cytoskeletal distribution and receptor concentration optimal clustering occurs and when it is inhibited. We investigate oligomerization induced trapping to determine mechanisms of clustering, and our results show that the cytoskeletal interactions lead to receptor clustering. After exploring the mechanisms of clustering we determine how receptor aggregation effects downstream signaling. We further proceed by implementing the adaptively coarse grained Monte Carlo, ACGMC to determine if \u27receptor-sharing\u27 occurs when receptors are clustered. In our proposed \u27receptor-sharing\u27 mechanism a cytosolic species binds with a receptor then disassociates and rebinds a neighboring receptor. We tested our hypothesis using a novel computational approach, the ACGMC, an algorithm which enables the spatial temporal evolution of the system in three dimensions by using a coarse graining approach. In this framework we are modeling EGFR reaction-diffusion events on the plasma membrane while capturing the spatial-temporal dynamics of proteins in the cytosol. From this framework we observe \u27receptor-sharing\u27 which may be an important mechanism in the regulation and overall efficiency of signal transduction. In summary, I have helped to develop predictive computational tools that take systems biology in a new direction.\u2

Effects of ultrafines on the hydrodynamics of gas fluidized beds

It has been reported in the literature that the addition of small quantities of so called fines (usually defined as particles less than 10μm in diameter) to Group A/C and B materials can significantly change the fluidization characteristics of the host materials; e.g. suppress bubbling, cause very large bed expansions and reduce elutriation from the bed. This change in fluidization behaviour can be linked to the physical properties of the material. The aim of this project was to study, by a combination of experiments, the changes in fluidization behaviour of typical Group A and C materials with increasing quantities of ultrafines. The work was divided into two sections: (i) fluidization and (ii) systematic study of properties relevant to it. The fluidization experiments mainly consisted of measurements of minimum fluidization and minimum bubbling velocities as well as the bed expansion for a range of bed conditions. Bed collapse experiments were also carried out to determine the dense phase expansion together with the expansion due to bubbles. The latter basically included measurements of physical properties such as particle size and its distribution, pore size distribution and various densities of powders for several combinations of ultrafines and host materials. Cracking catalyst (FCC), silica and kieselguhr were the host materials while a range of fumed silica were used as the ultrafine in the experiments. Additives appeared to have a considerable effect on FCC and silica by increasing the expansion up to 50%, increasing the deaeration velocity and reducing the elutriation loss while the fluidization was much smoother in general. The data obtained with kieselguhr, a typical Group C material, indicated that under some conditions, the presence of the additives resulted in significant increases in bed expansion and improved the fluidization behaviour. The extent of these effects appeared to depend on the concentration of the ultrafines. Additives appeared to change the fluidization behaviour of the material by forming a particular network which leads to a looser bed structure. Different predictive criteria for transition from particulate to aggregate fluidization were used and compared to the experimental data. It was concluded that as well as the hydrodynamic effects, interparticle forces were of importance in the stability of the bed