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.

Chestnut and mimosa tannin silages: Effects in sheep differ for apparent digestibility, nitrogen utilisation and losses

This paper reports effects of chestnut and mimosa tannins on N utilisation in sheep. Tannins were added to grass either at ensilage or incorporated into grass silage at feeding. The study used an 8 × 5 incomplete Latin Square design with eight mature wether sheep and five 21-day periods. Tannin additions reduced in vivo apparent digestibilities of dry matter (DM), organic matter (OM) and neutral detergent fibre (aNDFom) compared with the untreated control silage (P<0.001). Reductions ranged from 7.6% for DM to 8.5% for aNDFom. Chestnut compared to mimosa tannin silages produced higher values for DM intake (734 g/day versus 625 g/day) and in vivo digestibility for DM, OM and aNDFom (0.66, 0.68 and 0.69 versus 0.61, 0.63 and 0.62; P<0.001). A substantial shift occurred in the pattern of N excretion in sheep fed the tannin versus control silages. As a proportion of daily N intake, urinary N losses were lower (56.4 g/100 g N versus 65.1 g/100 g N intake) and faecal N losses were higher (40.2 g/100 g N versus 29.8 g/100 g N intake) for sheep fed the tannin silages compared with those fed the control grass silage (P<0.001). Nitrogen intake was higher in sheep fed the chestnut compared to mimosa tannin silages (16.2 g/day versus 13.4 g/day; P<0.001), reflecting the lower DM intake of sheep fed the mimosa silages. However, faecal N loss was lower for chestnut compared to mimosa tannin silage fed sheep (38.2 g/100 g N versus 42.1 g/100 g N intake; P<0.01), resulting in higher N retentions with the chestnut compared to the mimosa silage fed sheep (5.49 g/100 g N versus 1.38 g/100 g N intake). Faecal N losses were also higher when tannins were added during ensiling rather than at feeding (P<0.05). Although there was no overall effect of tannins on N retention in mature wether sheep, it is likely that productive ruminants with higher protein requirements would retain more N from silages containing chestnut tannins. Tannins added externally to grass silages may generate some benefits on N utilisation and environmental N excretions in sheep fed the silages.

Strategies for optimizing nitrogen use by ruminants

The efficiency of N utilization in ruminants is typically low (around 25%) and highly variable (10% to 40%) compared with the higher efficiency of other production animals. The low efficiency has implications for the production performance and environment. Many efforts have been devoted to improving the efficiency of N utilization in ruminants, and while major improvements in our understanding of N requirements and metabolism have been achieved, the overall efficiency remains low. In general, maximal efficiency of N utilization will only occur at the expense of some losses in production performance. However, optimal production and N utilization may be achieved through the understanding of the key mechanisms involved in the control of N metabolism. Key factors in the rumen include the efficiency of N capture in the rumen (grams of bacterial N per grams of rumen available N) and the modification of protein degradation. Traditionally, protein degradation has been modulated by modifying the feed (physical and chemical treatments). Modifying the rumen microflora involved in peptide degradation and amino acid deamination offers an alternative approach that needs to be addressed. Current evidence indicates that in typical feeding conditions there is limited net recycling of N into the rumen (blood urea-N uptake minus ammonia-N absorption), but understanding the factors controlling urea transport across the rumen wall may reverse the balance to take advantage of the recycling capabilities of ruminants. Finally, there is considerable metabolism of amino acids (AA) in the portal-drained viscera (PDV) and liver. However, most of this process occurs through the uptake of AA from the arterial blood and not during the ‘absorptive’ process. Therefore, AA are available to the peripheral circulation and to the mammary gland before being used by PDV and the liver. In these conditions, the mammary gland plays a key role in determining the efficiency of N utilization because the PDV and liver will use AA in excess of those required by the mammary gland. Protein synthesis in the mammary gland appears to be tightly regulated by local and systemic signals. The understanding of factors regulating AA supply and absorption in the mammary gland, and the synthesis of milk protein should allow the formulation of diets that increase total AA uptake by the mammary gland and thus reduce AA utilization by PDV and the liver. A better understanding of these key processes should allow the development of strategies to improve the efficiency of N utilization in ruminants.

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.