Swarm pattern transformation methodologies

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.

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.

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.

The transformation of earth-system observations into information of socio-economic value in GEOSS

The Group on Earth Observations System of Systems, GEOSS, is a co-ordinated initiative by many nations to address the needs for earth-system information expressed by the 2002 World Summit on Sustainable Development. We discuss the role of earth-system modelling and data assimilation in transforming earth-system observations into the predictive and status-assessment products required by GEOSS, across many areas of socio-economic interest. First we review recent gains in the predictive skill of operational global earth-system models, on time-scales of days to several seasons. We then discuss recent work to develop from the global predictions a diverse set of end-user applications which can meet GEOSS requirements for information of socio-economic benefit; examples include forecasts of coastal storm surges, floods in large river basins, seasonal crop yield forecasts and seasonal lead-time alerts for malaria epidemics. We note ongoing efforts to extend operational earth-system modelling and assimilation capabilities to atmospheric composition, in support of improved services for air-quality forecasts and for treaty assessment. We next sketch likely GEOSS observational requirements in the coming decades. In concluding, we reflect on the cost of earth observations relative to the modest cost of transforming the observations into information of socio-economic value.

The synthesis, structure, reactivity and electrochemical properties of ruthenium complexes featuring cyanoacetylide ligands

The complex [Ru(C&3bond; CC&3bond; N)(dppe)Cp*] (1) is readily obtained (ca. 70%) from the sequential reaction of [Ru(C=CH2)(dppe)Cp*]PF6 with (BuLi)-Bu-n and phenyl cyanate. The complex behaves as a typical transition metal acetylide upon reaction with tetracyanoethene, affording a metallated pentacyanobutadiene. Complex I is a useful metalloligand, and its reactions with [W(thf)(CO)5], [RuCl(PPh3)(2)Cp], [RuCl(dppe)Cp*] or cis-[RuCl2(dppe)(2)] all afforded products featuring the M-C&3bond; CC&3bond; N-M' motif, for which ground state structures indicate a degree of polarisation. Electrochemical and spectroelectrochemical studies reveal moderate interactions between the metal centres in the 35-electron dications [{Cp*(dppe)Ru}(mu-C&3bond; CC&3bond; N){RuL2Cp'}](2+) Ru(PPh3)(2)CP, Ru(dppe)Cp*).

Multistep electrochemical reduction path of clusters [Os3(CO)10(α-diimine)]: comparison of electrochemical and photochemical Os-Os(α-diimine) bond cleavage

Electrochemical reduction of the triangular clusters [Os-3(CO)(10)(alpha-dimine)] (alpha-dimine = 2,2'-bipyridine (bpy), 2,2'-bipyrimidine (bpym)) and [Os-3(CO)(10)(mu-bpym) ReBr(CO)(3)] produces primarily the corresponding radical anions. Their stability is strongly determined by the pi acceptor ability of the reducible alpha-dimine ligand, which decreases in the order mu-bpym > bpym >> bpy. Along this series, increasing delocalisation of the odd electron density in the radical anion over the Os(alpha-dimine) chelate ring causes weakening of the axial (CO)(4)Os-Os(CO)(2)(alpha-dimine) bond and its facile cleavage for alpha-diimine = bpy. In contrast, the cluster radical anion is inherently stable for the bridging bpym ligand, the strongest pi-acceptor in the studied series. In the absence of the partial delocalisation of the unpaired electron over the Re( bpym) chelate bond, the Os-3-core of the radical anion remains intact only at low temperatures. Subsequent one-electron reduction of [Os-3(CO)(10)(bpym)](center dot-) at T = 223 K gives the open-triosmium core (= Os-3*) dianion, [Os-3*(CO)(10)(bpym)](2-). Its oxidation leads to the recovery of parent [Os-3(CO)(10)( bpym)]. At room temperature, [Os-3*( CO)(10)(bpym)](2-) is formed along a two-electron (ECE) reduction path. The chemical step (C) results in the formation of an open- core radical anion that is directly reducible at the cathodic potential of the parent cluster in the second electrochemical (E) step. In weakly coordinating tetrahydrofuran, [Os-3*(CO)(10)( bpym)](2-) rapidly attacks yet non- reduced parent cluster molecules, producing the relatively stable open- core dimer [Os-3*(CO)(10)(bpym)](2)(2-) featuring two open- triangle cluster moieties connected with an ( bpym) Os - Os( bpym) bond. In butyronitrile, [Os-3*( CO)(10)(bpym)](2-) is stabilised by the solvent and the dimer [Os-3*(CO)(10)(bpym)](2)(2-) is then mainly formed by reoxidation of the dianion on reverse potential scan. The more reactive cluster [Os-3(CO)(10)(bpy)] follows the same reduction path, as supported by spectroelectrochemical results and additional valuable evidence obtained from cyclic voltammetric scans. The ultimate process in the reduction mechanism is fragmentation of the cluster core triggered by the reduction of the dimer [Os-3*(CO)(10)(alpha- diimine)](2)(2-). The products formed are [Os-2(CO)(8)](2-) and {Os(CO)(2)(alpha- diimine)}(2). The latter dinuclear fragments constitute a linear polymeric chain [Os( CO)(2)(alpha-dimine)] n that is further reducible at the alpha-dimine ligands. For alpha-dimine = bpy, the charged polymer is capable of reducing carbon dioxide. The electrochemical opening of the triosmium core in the [Os-3( CO)(10)(alpha-dimine)] clusters exhibits several common features with their photochemistry. The same Os-alpha-dimine bond dissociates in both cases but the intimate mechanisms are different.