Spatial density distributions of C2, C3, and CH radicals by laser-induced fluorescence in a diamond depositing dc-arcjet

1997 ◽  
Vol 82 (5) ◽  
pp. 2072-2081 ◽  
Author(s):  
J. Luque ◽  
W. Juchmann ◽  
J. B. Jeffries
Keyword(s):  
Laser Induced Fluorescence ◽  
Spatial Density ◽  
Density Distributions

The 4He Trimer: Structure and Energetics of a Very Unusual Molecule

2003 ◽  
Vol 68 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Cono Di Paola ◽  
Franco A. Gianturco ◽  
Gerardo Delgado-Barrio ◽  
Salvador Miret-Artés ◽  
Pablo Villarreal
Keyword(s):  
Monte Carlo ◽  
Stochastic Analysis ◽  
Binding Energies ◽  
Geometric Constraints ◽  
Spatial Density ◽  
Spatial Distributions ◽  
Diffusion Monte Carlo ◽  
Density Distributions ◽  
Total Binding ◽  
Full Interaction

The 4He3 weakly interacting system is analysed by constructing the full interaction as a sum of two-body (2B) potentials chosen among the most recent proposals from the literature. The spatial density distributions of the three bound atoms are obtained using a diffusion Monte Carlo (DMC) algorithm and a stochastic analysis under specific geometric constraints is carried out with the resulting densities in order to recover a more conventional structural picture for such floppy system. The total binding energies were obtained with the chosen potentials analysed in the present work, using the DMC algorithm, and are compared with previous published results. The ensuing spatial distributions are analysed in some detail to select the dominant structures from a conventional triangular description of this very floppy molecule.

A generalized Potts model for confocal microscopy images

2015 ◽  
Vol 29 (08) ◽  
pp. 1550048
Author(s):  
Gabriell Máté ◽  
Dieter W. Heermann
Keyword(s):  
Confocal Microscopy ◽  
Potts Model ◽  
Ground Truth ◽  
Spatial Density ◽  
Model Parameters ◽  
Generative Process ◽  
Density Distributions ◽  
Real Cell ◽  
Microscopy Images

Much as being among the least invasive mainstream imaging technologies in life sciences, the resolution of confocal microscopy is limited. Imaged structures, e.g., chromatin-fiber loops, have diameters around or beyond the diffraction limit, and microscopy images show seemingly random spatial density distributions only. While such images are important because the organization of the chromosomes influences different cell mechanisms, many interesting questions can also be related to the observed patterns. These concern their spatial aspects, the role of randomness, the possibility of modeling these images with a random generative process, the interaction between the densities of adjacent loci, the length-scales of these influences, etc. We answer these questions by implementing a generalization of the Potts model. We show how to estimate the model parameters, test the performance of the estimation process and numerically prove that the obtained values converge to the ground truth. Finally, we generate images with a trained model and show that they compare well to real cell images.

scholarly journals Evaluation of peak functions on ultra-coarse grids

2013 ◽  
Vol 469 (2153) ◽  
pp. 20120665 ◽  
Author(s):  
Natalia Petrovskaya ◽  
Nina Embleton
Keyword(s):  
Random Variable ◽  
Spatial Density ◽  
Pest Species ◽  
Sampled Data ◽  
Monitoring And Control ◽  
Density Distributions ◽  
Integration Error ◽  
Peak Functions ◽  
Coarse Grids ◽  
Integrand Function

Integration of sampled data arises in many practical applications, where the integrand function is available from experimental measurements only. One extensive field of research is the problem of pest monitoring and control where an accurate evaluation of the population size from the spatial density distribution is required for a given pest species. High aggregation population density distributions (peak functions) are an important class of data that often appear in this problem. The main difficulty associated with the integration of such functions is that the function values are usually only available at a few locations; therefore, new techniques are required to evaluate the accuracy of integration as the standard approach based on convergence analysis does not work when the data are sparse. Thus, in this paper, we introduce the new concept of ultra-coarse grids for high aggregation density distributions. Integration of the density function on ultra-coarse grids cannot provide the prescribed accuracy because of insufficient information (uncertainty) about the integrand function. Instead, the results of the integration should be treated probabilistically by considering the integration error as a random variable, and we show how the corresponding probabilities can be calculated. Handling the integration error as a random variable allows us to evaluate the accuracy of integration on very coarse grids where asymptotic error estimates cannot be applied.

Reconstruction of Objects from their Projections Using Orthogonal Functions

1973 ◽  
Vol 31 ◽  
pp. 268-269
Author(s):  
Elrnar Zeitler
Keyword(s):  
Unit Area ◽  
Three Dimensional ◽  
One Dimension ◽  
Spatial Density ◽  
Dimensional Representation ◽  
Orthogonal Functions ◽  
Object A ◽  
Plane Normal ◽  
Dimensional Object ◽  
Spatial Density Distribution

Considering any finite three-dimensional object, a “projection” is here defined as a two-dimensional representation of the object's mass per unit area on a plane normal to a given projection axis, here taken as they-axis. Since the object can be seen as being built from parallel, thin slices, the relation between object structure and its projection can be reduced by one dimension. It is assumed that an electron microscope equipped with a tilting stage records the projectionWhere the object has a spatial density distribution p(r,ϕ) within a limiting radius taken to be unity, and the stage is tilted by an angle 9 with respect to the x-axis of the recording plane.

Fabrication of Nanoscale Hydrophobic Regions on Anodic Alumina for Selective Adhesion of Biologic Molecules

2003 ◽  
Vol 773 ◽  
Author(s):  
Xiefan Lin ◽  
Anthony S. W. Ham ◽  
Natalie A. Villani ◽  
Whye-Kei Lye ◽  
Qiyu Huang ◽  
...  
Keyword(s):  
Anodic Alumina ◽  
Biological Molecules ◽  
Spatial Density ◽  
Length Scale ◽  
Adhesion Sites ◽  
A Cell ◽  
Adhesive Molecules ◽  
Receptor Clusters ◽  
Molecular Bonding ◽  
Fabrication Parameters

AbstractStudies of selective adhesion of biological molecules provide a path for understanding fundamental cellular properties. A useful technique is to use patterned substrates, where the pattern of interest has the same length scale as the molecular bonding sites of a cell, in the tens of nanometer range. We employ electrochemical methods to grow anodic alumina, which has a naturally ordered pore structure (interpore spacing of 40 to 400 nm) controlled by the anodization potential. We have also developed methods to selectively fill the alumina pores with materials with contrasting properties. Gold, for example, is electrochemically plated into the pores, and the excess material is removed by backsputter etching. The result is a patterned surface with closely separated islands of Au, surrounded by hydrophilic alumina. The pore spacing, which is determined by fabrication parameters, is hypothesized to have a direct effect on the spatial density of adhesion sites. By attaching adhesive molecules to the Au islands, we are able to observe and study cell rolling and adhesion phenomena. Through the measurements it is possible to estimate the length scale of receptor clusters on the cell surface. This information is useful in understanding mechanisms of leukocytes adhesion to endothelial cells as well as the effect of adhesion molecules adaptation on transmission of extracellular forces. The method also has applications in tissue engineering, drug and gene delivery, cell signaling and biocompatibility design.

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