Novel solid networks containing Sn-H as a greener replacement for soluble alkyl Sn-H reagents in radical assisted organic synthesis

DiGrignard reagents of the form XMg(CH2)(n)MgX, where X = Br or I and n = 6, 8, 10 or 12, were allowed to react with PhSnCl3 to produce highly cross-linked Ph-Sn polymeric networks. The Sn-H moiety was incorporated into these insoluble network polymers by treatment with Br-2 and NaBH4. Excellent accessibility of the Sn-H was displayed by these solvent penetrable but insoluble networks, giving them higher Sn-H loadings than all previously reported supported reagents. These reagents were totally regenerable in NaBH4 for radical assisted organic synthesis and no detectable leaching of the Sn into solution was observed during these reactions.

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).

New copper(I) cyanide networks: interpenetration, self-penetration and polymorphism

The structures Of four alkali-metal copper (I) cyanides, KCu2(CN)(3)(H2O)-H-.-II (I), K2Cu3(CN)(5) (II), CsCu3(CN)(4) (III) and KCu3(CN)(4) (IV) are described. Three of these, ((II)-(IV)), with previously unknown ACN:CuCN ratios have new copper-cyanide frameworks, whilst (1) is a new polymorph of KCu2(CN)(3)(H2O)-H-.. These structures are discussed in terms of assembly from the simple building units Cu(CN)(2/2), Cu(CN)(3/2), Cu(CN)(2/2)(CN)(1/1) and Cu(CN)(4/2). Compounds (I), (II) and (III) are layered materials based on (6,3) nets containing (CuCN)(6) rings (I) and (CuCN)(8) rings (II) and (III). In compound (IV), (4,4) nets containing (CuCN)(12) rings link to generate a three-dimensional network. Both (III) and (IV) are examples of interpenetrating solids in which two and four identical networks interweave, respectively. These materials illustrate the structural versatility of copper (I) in cyanide frameworks. (c) 2006 Elsevier SAS. All rights reserved.

Structure determination, magnetic and optical properties of a new chromium(II) thioantimonate, [Cr((NH2CH2CH2)(3)N)]Sb4S7

The chromium(II) antimony(III) sulphicle, [Cr((NH2CH2CH2)(3)N)]Sb4S7, was synthesised under solvothermal conditions from the reaction of Sb2S3. Cr and S dissolved in tris(2-aminoethyl)amine (tren) at 438 K. The products were characterised by single-crystal X-ray diffraction. elemental analysis, SQUID magnetometry and diffuse reflectance spectroscopy. The compound crystallises in the monoclinic space group P2(1)/n with a = 7.9756(7), b = 10.5191(9), c = 25.880(2) angstrom and beta = 90.864(5)degrees. Alternating SbS33- trigonal pyramids and Sb36 semi-cubes generate Sb4S72- chains which are directly bonded to Cr(tren pendant units. The effective magnetic moment of 4.94(6)mu(B) shows a negligible orbital contribution, in agreement with expectations for Cr(II):d(4) in a (5)A ground state. The measured band gap of 2.14(3) eV is consistent with a correlation between optical band gap and framework density that is established from analysis of a wide range of antimony sulphides. (C) 2007 Elsevier Ltd. All rights reserved.

Simulating global properties of electroencephalograms with minimal random neural networks

The human electroencephalogram (EEG) is globally characterized by a 1/f power spectrum superimposed with certain peaks, whereby the "alpha peak" in a frequency range of 8-14 Hz is the most prominent one for relaxed states of wakefulness. We present simulations of a minimal dynamical network model of leaky integrator neurons attached to the nodes of an evolving directed and weighted random graph (an Erdos-Renyi graph). We derive a model of the dendritic field potential (DFP) for the neurons leading to a simulated EEG that describes the global activity of the network. Depending on the network size, we find an oscillatory transition of the simulated EEG when the network reaches a critical connectivity. This transition, indicated by a suitably defined order parameter, is reflected by a sudden change of the network's topology when super-cycles are formed from merging isolated loops. After the oscillatory transition, the power spectra of simulated EEG time series exhibit a 1/f continuum superimposed with certain peaks. (c) 2007 Elsevier B.V. All rights reserved.

The automatic identification and prioritisation of criminal networks from police crime data

The identification of criminal networks is not a routine exploratory process within the current practice of the law enforcement authorities; rather it is triggered by specific evidence of criminal activity being investigated. A network is identified when a criminal comes to notice and any associates who could also be potentially implicated would need to be identified if only to be eliminated from the enquiries as suspects or witnesses as well as to prevent and/or detect crime. However, an identified network may not be the one causing most harm in a given area.. This paper identifies a methodology to identify all of the criminal networks that are present within a Law Enforcement Area, and, prioritises those that are causing most harm to the community. Each crime is allocated a score based on its crime type and how recently the crime was committed; the network score, which can be used as decision support to help prioritise it for law enforcement purposes, is the sum of the individual crime scores.

Wireless sensor networks

This paper provides an introduction to Wireless Sensor Networks (WSN), their applications in the field of control engineering and elsewhere and gives pointers to future research needs. WSN are collections of stand-alone devices which, typically, have one or more sensors (e.g. temperature, light level), some limited processing capability and a wireless interface allowing communication with a base station. As they are usually battery powered, the biggest challenge is to achieve the necessary monitoring whilst using the least amount of power.

Opportunistic cooperative diversity protocols for wireless networks

In this paper we consider a cooperative communication system where some a priori information of wireless channels is available at the transmitter. Several opportunistic relaying strategies are developed to fully utilize the available channel information. Then an explicit expression of the outage probability is developed for each proposed cooperative scheme as well as the diversity-multiplexing tradeoff by using order statistics. Our analytical results show that the more channel information available at the transmitter, the better performance a cooperative system can achieve. When the exact values of the source-relay channels are available, the performance loss at low SNR can be effectively suppressed. When the source node has the access to the source-relay and relay-destination channels, the full diversity can be achieved by costing only one extra channel used for relaying transmission, and an optimal diversity-multiplexing tradeoff can be achieved d(r) = (N + 1)(1 - 2r), where N is the number of all possible relaying nodes.

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme. (C) 2007 Elsevier B.V. All rights reserved.

Global exponential periodicity of a class of bidirectional associative memory networks with finite distributed delays

In this paper, we study the periodic oscillatory behavior of a class of bidirectional associative memory (BAM) networks with finite distributed delays. A set of criteria are proposed for determining global exponential periodicity of the proposed BAM networks, which assume neither differentiability nor monotonicity of the activation function of each neuron. In addition, our criteria are easily checkable. (c) 2005 Elsevier Inc. All rights reserved.

Global exponential periodicity of a class of neural networks with recent-history distributed delays

In this paper, we propose to study a class of neural networks with recent-history distributed delays. A sufficient condition is derived for the global exponential periodicity of the proposed neural networks, which has the advantage that it assumes neither the differentiability nor monotonicity of the activation function of each neuron nor the symmetry of the feedback matrix or delayed feedback matrix. Our criterion is shown to be valid by applying it to an illustrative system. (c) 2005 Elsevier Ltd. All rights reserved.

An oblivious shortest-path routing algorithm for fully connected cubic networks

Fully connected cubic networks (FCCNs) are a class of newly proposed hierarchical interconnection networks for multicomputer systems, which enjoy the strengths of constant node degree and good expandability. The shortest path routing in FCCNs is an open problem. In this paper, we present an oblivious routing algorithm for n-level FCCN with N = 8(n) nodes, and prove that this algorithm creates a shortest path from the source to the destination. At the costs of both an O(N)-parallel-step off-line preprocessing phase and a list of size N stored at each node, the proposed algorithm is carried out at each related node in O(n) time. In some cases the proposed algorithm is superior to the one proposed by Chang and Wang in terms of the length of the routing path. This justifies the utility of our routing strategy. (C) 2006 Elsevier Inc. All rights reserved.

Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations

Cloud optical depth is one of the most poorly observed climate variables. The new “cloud mode” capability in the Aerosol Robotic Network (AERONET) will inexpensively yet dramatically increase cloud optical depth observations in both number and accuracy. Cloud mode optical depth retrievals from AERONET were evaluated at the Atmospheric Radiation Measurement program’s Oklahoma site in sky conditions ranging from broken clouds to overcast. For overcast cases, the 1.5 min average AERONET cloud mode optical depths agreed to within 15% of those from a standard ground‐based flux method. For broken cloud cases, AERONET retrievals also captured rapid variations detected by the microwave radiometer. For 3 year climatology derived from all nonprecipitating clouds, AERONET monthly mean cloud optical depths are generally larger than cloud radar retrievals because of the current cloud mode observation strategy that is biased toward measurements of optically thick clouds. This study has demonstrated a new way to enhance the existing AERONET infrastructure to observe cloud optical properties on a global scale.

Design and disposition of infrared optical multilayer coatings for the Improved Stratospheric and Mesospheric Sounder (ISAMS)

The Improved Stratospheric and Mesospheric Sounder (ISAMS) is designed to measure the Earths middle atmosphere in the range of 4.6 to 16.6 micorns. This paper considers all the coated optical elements in two radiometric test channels. (Analysis of the spectral response will be presented as a seperate paper at this symposium, see Sheppard et al). Comparisons between the compued spectral performance and measurements from actual coatings will be discussed: These will include substrate absorption simulations. The results of environmental testing (durability and stability) are included, together with details of coating deposition and monitoring conditions.

International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.