Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

Multiscale evolving complex network model of functional connectivity in neuronal cultures

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological selforganization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory. Functional connectivity networks can be constructed by observing patterns of synchrony that evolve during bursts. To capture this evolution, a modelling approach is adopted using a framework of emergent evolving complex networks and, through taking advantage of the multiple time scales of the system, aims to show the importance of sequential and ordered synchronization in network function.

Modelling disease spread and control in networks: implications for plant sciences

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed.

Models for evolving networks: with applications in telecommunication and online activities

Driven by a range of modern applications that includes telecommunications, e-business and on-line social interaction, recent ideas in complex networks can be extended to the case of time-varying connectivity. Here we propose a general frame- work for modelling and simulating such dynamic networks, and we explain how the long time behaviour may reveal important information about the mechanisms underlying the evolution.

Relative order defines a topology for recurrent networks

This paper uses techniques from control theory in the analysis of trained recurrent neural networks. Differential geometry is used as a framework, which allows the concept of relative order to be applied to neural networks. Any system possessing finite relative order has a left-inverse. Any recurrent network with finite relative order also has an inverse, which is shown to be a recurrent network.

Primary evolving networks and the comparative analysis of robust and fragile structures

In this paper we consider the structure of dynamically evolving networks modelling information and activity moving across a large set of vertices. We adopt the communicability concept that generalizes that of centrality which is defined for static networks. We define the primary network structure within the whole as comprising of the most influential vertices (both as senders and receivers of dynamically sequenced activity). We present a methodology based on successive vertex knockouts, up to a very small fraction of the whole primary network,that can characterize the nature of the primary network as being either relatively robust and lattice-like (with redundancies built in) or relatively fragile and tree-like (with sensitivities and few redundancies). We apply these ideas to the analysis of evolving networks derived from fMRI scans of resting human brains. We show that the estimation of performance parameters via the structure tests of the corresponding primary networks is subject to less variability than that observed across a very large population of such scans. Hence the differences within the population are significant.

Weighted alpha-rate dominating sets in social networks

We are looking into variants of a domination set problem in social networks. While randomised algorithms for solving the minimum weighted domination set problem and the minimum alpha and alpha-rate domination problem on simple graphs are already present in the literature, we propose here a randomised algorithm for the minimum weighted alpha-rate domination set problem which is, to the best of our knowledge, the first such algorithm. A theoretical approximation bound based on a simple randomised rounding technique is given. The algorithm is implemented in Python and applied to a UK Twitter mentions networks using a measure of individuals’ influence (klout) as weights. We argue that the weights of vertices could be interpreted as the costs of getting those individuals on board for a campaign or a behaviour change intervention. The minimum weighted alpha-rate dominating set problem can therefore be seen as finding a set that minimises the total cost and each individual in a network has at least alpha percentage of its neighbours in the chosen set. We also test our algorithm on generated graphs with several thousand vertices and edges. Our results on this real-life Twitter networks and generated graphs show that the implementation is reasonably efficient and thus can be used for real-life applications when creating social network based interventions, designing social media campaigns and potentially improving users’ social media experience.

Learning across business sectors: Knowledge sharing between aerospace and construction

This report addresses the extent that managerial practices can be shared between the aerospace and construction sectors. Current recipes for learning from other industries tend to be oversimplistic and often fail to recognise the embedded and contextual nature of managerial knowledge. Knowledge sharing between business sectors is best understood as an essential source of innovation. The process of comparison challenges assumptions and better equips managers to cope with future change. Comparisons between the aerospace and construction sectors are especially useful because they are so different. The two sectors differ hugely in terms of their institutional context, structure and technological intensity. The aerospace sector has experienced extensive consolidation and is dominated by a small number of global companies. Aerospace companies operate within complex networks of global interdependency such that collaborative working is a commercial imperative. In contrast, the construction sector remains highly fragmented and is characterised by a continued reliance on small firms. The vast majority of construction firms compete within localised markets that are too often characterised by opportunistic behaviour. Comparing construction to aerospace highlights the unique characteristics of both sectors and helps explain how managerial practices are mediated by context. Detailed comparisons between the two sectors are made in a range of areas and guidance is provided for the implementation of knowledge sharing strategies within and across organisations. The commonly accepted notion of ‘best practice’ is exposed as a myth. Indeed, universal models of best practice can be detrimental to performance by deflecting from the need to adapt continuously to changing circumstances. Competitiveness in the construction sector too often rests on efficiency in managing contracts, with a particular emphasis on the allocation of risk. Innovation in construction tends to be problem-driven and is rarely shared from project to project. In aerospace, the dominant model of competitiveness means that firms have little choice other than to invest in continuous innovation, despite difficult trading conditions. Research and development (R&D) expenditure in aerospace continues to rise as a percentage of turnovers. A sustained capacity for innovation within the aerospace sector depends crucially upon stability and continuity of work. In the construction sector, the emergence of the ‘hollowed-out’ firm has undermined the industry’s capacity for innovation. Integrated procurement contexts such as prime contracting in construction potentially provide a more supportive climate for an innovation-based model of competitiveness. However, investment in new ways of working depends upon a shift in thinking not only amongst construction contractors, but also amongst the industry’s major clients.

Emergent discourses of construction competitiveness: localized learning and embeddedness

Research is described that sought to understand how senior managers within regional contracting firms conceptualize and enact competitiveness. Existing formal discourses of construction competitiveness include the discourse of 'best practice' and the various theories of competitiveness as routinely mobilized within the academic literature. Such discourses consistently underplay the influence of contextual factors in shaping how competitiveness is enacted. An alternative discourse of competitiveness is outlined based on the concepts of localized learning and embeddedness. Two case studies of regional construction firms provide new insights into the emergent discourses of construction competitiveness. The empirical findings resonate strongly with the concepts of localized learning and embeddedness. The case studies illustrate the importance of de-centralized structures which enable multiple business units to become embedded within localized markets. A significant degree of autonomy is essential to facilitate localized entrepreneurial behaviour. In essence, sustained competitiveness was found to depend upon the extent to which de-centralized business units enact ongoing processes of localized learning. Once local business units have become embedded within localized markets the essential challenge is how to encourage continued entrepreneurial behaviour while maintaining a degree of centralized control and coordination. Of key importance is the recognition that the capabilities that make companies competitive transcend organizational boundaries such that they become situated within complex networks of relational ties.

Brain computer interface control via functional connectivity dynamics

The dynamics of inter-regional communication within the brain during cognitive processing – referred to as functional connectivity – are investigated as a control feature for a brain computer interface. EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity. Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.

Mining in Ghana’s forests: cross-sectoral linkages and the prospects for REDD

Strategies to Reduce Emissions from Deforestation and Degradation (REDD) are being pursued in numerous developing countries. Proponents contest that REDD mechanisms could deliver sustainable development by contributing to both environmental protection and economic development, particularly in poor forest communities. However, among the challenges to REDD, and natural resource management more generally, is the need to develop a comprehensive understanding of cross-sectoral linkages and addressing how they impact the pursuit of sustainable development. Drawing on an exploratory case-study of Ghana, this paper aims to outline the linkages between the forestry and minerals sectors. It is argued that contemporary debates give incommensurate attention to the reclamation of large-scale mine sites located in forest reserves, and neglect to consider more nuanced links which characterise the forestry-mining nexus in Ghana. A review of key stakeholders further elucidates the complex networks which characterise these linkages and highlights the important role of traditional authorities in governing across sectors. If the multiple roles of local resource users and traditional authorities continue to be neglected in policy mechanisms, schemes such as REDD will continue to fall short of achieving sustainable development.

Syntheses of two new 1D and 3D networks of Cu(II) and Co(II) using malonate and urotropine as bridging ligands: Crystal structures and magnetic studies

Two new metal-organic based polymeric complexes, [Cu-4(O2CCH2CO2)(4)(L)].7H(2)O (1) and [CO2(O2CCH2CO2)(2)(L)].2H(2)O (2) [L = hexamethylenetetramine (urotropine)], have been synthesized and characterized by X-ray crystal structure determination and magnetic studies. Complex 1 is a 1D coordination polymer comprising a carboxylato, bridged Cu-4 moiety linked by a tetradentate bridging urotropine. Complex 2 is a 3D coordination polymer made of pseudo-two-dimensional layers of Co(II) ions linked by malonate anions in syn-anticonformation which are bridged by bidentate urotropine in trans fashion, Complex 1 crystallizes in the orthothombic system, space group Pmmn, with a = 14,80(2) Angstrom, b = 14.54(2) Angstrom, c = 7.325(10) Angstrom, beta = 90degrees, and Z = 4. Complex 2 crystallizes in the orthorhombic system, space group Imm2, a = 7.584(11) Angstrom, b = 15.80(2) Angstrom, c = 6.939(13) Angstrom, beta = 90.10degrees(1), and Z = 4. Variable temperature (300-2 K) magnetic behavior reveals the existence of ferro- and antiferromagnetic interactions in 1 and only antiferromagnetic interactions in 2. The best fitted parameters for complex 1 are J = 13.5 cm(-1), J = -18.1 cm(-1), and g = 2.14 considering only intra-Cu-4 interactions through carboxylate and urotropine pathways. In case of complex 2, the fit of the magnetic data considering intralayer interaction through carboxylate pathway as well as interlayer interaction via urotropine pathway gave no satisfactory result at this moment using any model known due to considerable orbital contribution of Co(II) ions to the magnetic moment and its complicated structure. Assuming isolated Co(II) ions (without any coupling, J = 0) the shape of the chi(M)T curve fits well with experimental data except at very low temperatures.

Two new mu-(1,3-azido)-bridged polymers: alternating single and double bridges in a 1D nickel(II) complex and uniform bridge in a 2D copper(II) complex: Syntheses, single-crystal structures and magnetic studies

Two polymeric azido bridged complexes [Ni2L2(N-3)(3)](n)(ClO4). (1) and [Cu(bpdS)(2)(N-3)],(ClO4),(H2O)(2.5n) (2) [L = Schiff base, obtained from the condensation of pyridine-2-aldehyde with N,N,2,2-tetramethyl-1,3-propanediamine; bpds = 4,4'-bipyridyl disulfide] have been synthesized and their crystal structures have been determined. Complex 1, C26H42ClN15Ni2O4, crystallizes in a triclinic system, space group P1 with a 8.089(13), b = 9.392(14), c = 12.267(18) angstrom, a = 107.28(l), b 95.95(1), gamma = 96.92(1)degrees and Z = 2; complex 2, C20H21ClCuN7O6.5S4, crystallizes in an orthorhombic system, space group Pnna with a = 10.839(14), b = 13.208(17), c = 19.75(2) angstrom and Z = 4. The crystal structure of I consists of 1D polymers of nickel(L) units, alternatively connected by single and double bridging mu-(1,3-N-3) ligand with isolated perchlorate anions. Variable temperature magnetic susceptibility data of the complex have been measured and the fitting,of magnetic data was carried out applying the Borris-Almenar formula for such types of alternating one-dimensional S = 1 systems, based on the Hamiltonian H = -J Sigma(S2iS2i-1 + aS(2i)S(2i+1)). The best-fit parameters obtained are J = -106.7 +/- 2 cm(-1); a = 0.82 +/- 0.02; g = 2.21 +/- 0.02. Complex 2 is a 2D network of 4,4 topology with the nodes occupied by the Cu-II ions, and the edges formed by single azide and double bpds connectors. The perchlorate anions are located between pairs of bpds. The magnetic data have been fitted considering the complex as a pseudo-one-dimensional system, with all copper((II)) atoms linked by [mu(1,3-azido) bridging ligands at axial positions (long Cu...N-3 distances) since the coupling through long bpds is almost nil. The best-fit parameters obtained with this model are J = -1.21 +/- 0.2 cm(-1), g 2.14 +/- 0.02. (c) Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2005).

Syntheses, crystal structures and magnetic properties of carboxylato-bridged polymeric networks of Mn-II

Three new carboxylato-bridged polymeric networks of Mn-II having molecular formula [Mn(ox)(dpyo)](n) (1), {[Mn-2(mal)(2)(bpee)(H2O)(2)]center dot 0.5(bpee)center dot 0.5(CH3OH)}n, (2) and {[Mn-3(btc)(2)(2,2'-bipy)(2)(H2O)(6)]center dot 4H(2)O}(n) (3) [dpyo, 4,4'-bipyridine N,N'dioxide; bpee, trans-1,2 bis(4-pyridyl) ethylene; 2,2'-bipy, 2,2'-bipyridine; ox = oxalate dianion; mal = malonate dianion; btc = 1,3,5-benzenetricarboxylate trianion] have been synthesized and characterized by single-crystal X-ray diffraction studies and low temperature magnetic measurements. Structure determination of complex I reveals a covalent bonded 2D network containing bischelating oxalate and bridging dpyo; complex 2 is a covalent,bonded 3D polymeric architecture, formed by bridging malonate and bpee ligands, resulting in an open framework with channels filled by uncoordinated disordered bpee and methanol molecules. Whereas complex 3, comprising btc anions bound to three metal centers, is a 1D chain which further extends its dimensionality to 3D via pi-pi and H-bonding interactions. Low temperature magnetic measurements reveal the existence of weak antiferromagnetic interaction in all these complexes. ((c) Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2006).

An Efficient Parameterization of Dynamic Neural Networks for Nonlinear System Identification

Dynamic neural networks (DNNs), which are also known as recurrent neural networks, are often used for nonlinear system identification. The main contribution of this letter is the introduction of an efficient parameterization of a class of DNNs. Having to adjust less parameters simplifies the training problem and leads to more parsimonious models. The parameterization is based on approximation theory dealing with the ability of a class of DNNs to approximate finite trajectories of nonautonomous systems. The use of the proposed parameterization is illustrated through a numerical example, using data from a nonlinear model of a magnetic levitation system.