A swarm pattern transformation method based on macroscopic parameter operations

The work reported in this paper is motivated by biomimetic inspiration - the transformation of patterns. The major issue addressed is the development of feasible methods for transformation based on a macroscopic tool. The general requirement for the feasibility of the transformation method is determined by classifying pattern formation approaches an their characteristics. A formal definition for pattern transformation is provided and four special cases namely, elementary and geometric transformation based on repositioning all and some robotic agents are introduced. A feasible method for transforming patterns geometrically, based on the macroscopic parameter operation of a swarm is considered. The transformation method is applied to a swarm model which lends itself to the transformation technique. Simulation studies are developed to validate the feasibility of the approach, and do indeed confirm the approach.

Investigating feasible tools for swarm pattern transformation

Abstract-The work reported in this paper is motivated by the need for developing swarm pattern transformation methodologies. Two methods, namely a macroscopic method and a mathematical method are investigated for pattern transformation. The first method is based on macroscopic parameters while the second method is based on both microscopic and macroscopic parameters. A formal definition to pattern transformation considering four special cases of transformation is presented. Simulations on a physics simulation engine are used to confirm the feasibility of the proposed transformation methods. A brief comparison between the two methods is also presented.

Modeling and simulating a mathematical tool for multi-robot pattern transformation

The work reported in this paper is motivated by the need to investigate general methods for pattern transformation. A formal definition for pattern transformation is provided and four special cases namely, elementary and geometric transformation based on repositioning all and some agents in the pattern are introduced. The need for a mathematical tool and simulations for visualizing the behavior of a transformation method is highlighted. A mathematical method based on the Moebius transformation is proposed. The transformation method involves discretization of events for planning paths of individual robots in a pattern. Simulations on a particle physics simulator are used to validate the feasibility of the proposed method.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

A review and implementation of swarm pattern formation and transformation models

Purpose: The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems. Design/methodology/approach: The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation. Findings: A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment. Originality/value: This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel.

Hydrogen bond-induced pair formation of glycine on the chiral Cu{531} surface

Enantio-specific interactions on intrinsically chiral or chirally modified surfaces can be identified experimentally via comparison of the adsorption geometries of similar nonchiral and chiral molecules. Information about the effects of substrate-related and in interactions on the adsorption geometry of glycine, the only natural nonchiral amino acid, is therefore important for identifying enantio-specific interactions of larger chiral amino acids. We have studied the long- and short-range adsorption geometry and bonding properties of glycine on the intrinsically chiral Cu{531} surface with low-energy electron diffraction, near-edge X-ray absorption One structure spectroscopy, X-ray photoelectron spectroscopy, and temperature-programmed desorption. For coverages between 0.15 and 0.33 ML (saturated chemisorbed layer) and temperatures between 300 and 430 K, glycine molecules adsorb in two different azimuthal orientations, which are associated with adsorption sites on the {110} and {311} microfacets of Cu{531}. Both types of adsorption sites allow a triangular footprint with surface bonds through the two oxygen atoms and the nitrogen atom. The occupation of the two adsorption sites is equal for all coverages, which can be explained by pair formation due to similar site-specific adsorption energies and the possibility of forming hydrogen bonds between molecules on adjacent {110} and {311} sites. This is not the ease for alanine and points toward higher site specificity in the case of alanine, which is eventually responsible for the enantiomeric differences observed for the alanine system.

Transformation of plant cells

Plant cells are transformed by bringing them into contact with a a multiplicity of needle-like bodies on which the cells may be impaled. This causes a rupture in the cell wall allowing entry of transforming DNA either from a surrounding liquid medium or of DNA previously bound to or otherwise entrapped in the needle-like projections.

Transformation of plant cells

Plant cells are transformed by bringing them into contact with a a multiplicity of needle-like bodies on which the cells may be impaled. This causes a rupture in the cell wall allowing entry of transforming DNA either from a surrounding liquid medium or of DNA previously bound to or otherwise entrapped in the needle-like projections.

Chiral molecules on surfaces

chiral molecules can modify surfaces in many ways. Long-range chiral structures can be induced by local chirality, which can act as templates stereo-directing other molecules. Such templates are either based on the arrangement of molecules alone or involve reconstruction of the substrate suface. Stereo-direction can also be achieved buy direct local interaction between chiral moleculesx. Even the adsorption of achiral molecules onto achiral surfaces can induce local chirality due to a reduction ofsymmetry in the presence of the surface. Intrinsically chiral metal and oxide surfaces can act as templates for enantioselective adsorption and surface reactions without any surface modification.

Enantiospecific adsorption of alanine on the chiral Cu{531} surface

We have studied enantiospecific differences in the adsorption of (S)- and (R)-alanine on Cu{531}R using low-energy electron diffraction (LEED), X-ray photoelectron spectroscopy, and near edge X-ray absorption fine structure (NEXAFS) spectroscopy. At saturation coverage, alanine adsorbs as alaninate forming a p(1 4) superstructure. LEED shows a significantly higher degree of long-range order for the S than for the R enantiomer. Also carbon K-edge NEXAFS spectra show differences between (S)- and (R)-alanine in the variations of the ð resonance when the linear polarization vector is rotated within the surface plane. This indicates differences in the local adsorption geometries of the molecules, most likely caused by the interaction between the methyl group and the metal surface and/or intermolecular hydrogen bonds. Comparison with model calculations and additional information from LEED and photoelectron spectroscopy suggest that both enantiomers of alaninate adsorb in two different orientations associated with triangular adsorption sites on {110} and {311} microfacets of the Cu{531} surface. The experimental data are ambiguous as to the exact difference between the local geometries of the two enantiomers. In one of two models that fit the data equally well, significantly more (R)-alaninate molecules are adsorbed on {110} sites than on {311} sites whereas for (S)-alaninate the numbers are equal. The enantiospecific differences found in these experiments are much more pronounced than those reported from other ultrahigh vacuum techniques applied to similar systems.

The Structure of the chiral Pt{531} surface: a combined LEED and DFT study

The structure of the chiral kinked Pt{531} surface has been determined by low-energy electron diffraction intensity-versus-energy (LEED-IV) analysis and density functional theory (DFT). Large contractions and expansions of the vertical interlayer distances with respect to the bulk-terminated surface geometry were found for the first six layers (LEED: d(12) = 0.44 angstrom, d(23) = 0.69 angstrom, d(34) = 0.49 angstrom, d(45) = 0.95 angstrom, d(56) = 0.56 angstrom; DFT: d(12) = 0.51 angstrom, d(23) = 0.55 angstrom, d(34) = 0.74 angstrom, d(45) = 0.78 angstrom, d(56) = 0.63 angstrom; d(bulk) = 0.66 angstrom). Energy-dependent cancellations of LEED spots over unusually large energy ranges, up to 100 eV, can be explained by surface roughness and reproduced by applying a model involving 0.25 ML of vacancies and adatoms in the scattering calculations. The agreement between the results from LEED and DFT is not as good as in other cases, which could be due to this roughness of the real surface.