Rational modification of the hierarchy of intermolecular interactions in molecular crystal structures by using tunable halogen bonds

A family of 16 isomolecular salts (3-XpyH)(2)[MX'(4)] (3-XpyH=3-halopyridinium; M=Co, Zn; X=(F), Cl, Br, (I); X'=Cl, Br, I) each containing rigid organic cations and tetrahedral halometallate anions has been prepared and characterized by X-ray single crystal and/or powder diffraction. Their crystal structures reflect the competition and cooperation between non-covalent interactions: N-H center dot center dot center dot X'-M hydrogen bonds, C-X center dot center dot center dot X'-M halogen bonds and pi-pi stacking. The latter are essentially unchanged in strength across the series, but both halogen bonds and hydrogen bonds are modified in strength upon changing the halogens involved. Changing the organic halogen (X) from F to I strengthens the C-X center dot center dot center dot X'-M halogen bonds, whereas an analogous change of the inorganic halogen (X') weakens both halogen bonds and N-H center dot center dot center dot X'-M hydrogen bonds. By so tuning the strength of the putative halogen bonds from repulsive to weak to moderately strong attractive interactions, the hierarchy of the interactions has been modified rationally leading to systematic changes in crystal packing. Three classes of crystal structure are obtained. In type A (C-F center dot center dot center dot X'-M) halogen bonds are absent. The structure is directed by N-H center dot center dot center dot X'-M hydrogen bonds and pi-stacking interactions. In type B structures, involving small organic halogens (X) and large inorganic halogens (X'), long (weak) C-X center dot center dot center dot X'-M interactions are observed with type I halogen-halogen interaction geometries (C-X center dot center dot center dot X' approximate to X center dot center dot center dot X'-M approximate to 155 degrees), but hydrogen bonds still dominate. Thus, minor but quite significant perturbations from the type A structure arise. In type C, involving larger organic halogens (X) and smaller inorganic halogens (X'), stronger halogen bonds are formed with a type II halogen-halogen interaction geometry (C-X center dot center dot center dot X' approximate to 180 degrees; X center dot center dot center dot X'-M approximate to 110 degrees) that is electrostatically attractive. The halogen bonds play a major role alongside hydrogen bonds in directing the type C structures, which as a result are quite different from type A and B.

A platform for supporting micro-collaborations in a diverse device environment

Collaborative software is usually thought of as providing audio-video conferencing services, application/desktop sharing, and access to large content repositories. However mobile device usage is characterized by users carrying out short and intermittent tasks sometimes referred to as 'micro-tasking'. Micro-collaborations are not well supported by traditional groupware systems and the work in this paper seeks out to address this. Mico is a system that provides a set of application level peer-to-peer services for the ad-hoc formation and facilitation of collaborative groups across a diverse mobile device domain. The system builds on the Java ME bindings of the JXTA P2P protocols, and is designed with an approach to use the lowest common denominators that are required for collaboration between varying degrees of mobile device capability. To demonstrate how our platform facilitates application development, we built an exemplary set of demonstration applications and include code examples here to illustrate the ease and speed afforded when developing collaborative software with Mico.

ENABLE: a wrist worn device with integrated accessible services to support old people living independently and safely at home

This RTD project, 2007-2009, is partly funded by the European Commission, in Framework Programme 6. It aims to assist elderly people for living well, independently and at case. ENABLE will provide a number of services for elderly people based on the new technology provided by mobile phones. The project is developing a Wrist unit with both integrated and external sensors, and with a radio frequency link to a mobile phone. Dedicated ENABLE software running on the wrist unit and mobile phone makes these services fully accessible for the elderly users. This paper outlines the fundamental motivation and the approach which currently is undertaken in order to collect the more detailed user needs and requirements. The general architecture and the design of the ENABLE system are outlined.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Orthogonal forward selection for constructing the radial basis function network with tunable nodes

An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.

A simple device for multiplex ELISA made from melt-extruded plastic microcapillary film

We present a simple device for multiplex quantitative enzyme-linked immunosorbant assays (ELISA) made from a novel melt-extruded microcapillary film (MCF) containing a parallel array of 200µm capillaries along its length. To make ELISA devices different protein antigens or antibodies were immobilised inside individual microcapillaries within long reels of MCF extruded from fluorinated ethylene propylene (FEP). Short pieces of coated film were cut and interfaced with a pipette, allowing sequential uptake of samples and detection solutions into all capillaries from a reagent well. As well as being simple to produce, these FEP MCF devices have excellent light transmittance allowing direct optical interrogation of the capillaries for simple signal quantification. Proof of concept experiments demonstrate both quantitative and multiplex assays in FEP MCF devices using a standard direct ELISA procedure and read using a flatbed scanner. This new multiplex immunoassay platform should find applications ranging from lab detection to point-of-care and field diagnostics.

pH-tunable hydrogelators for water purification: structural optimisation and evaluation

A focused library of potential hydrogelators each containing two substituted aromatic residues separated by a urea or thiourea linkage have been synthesised and characterized. Six of these novel compounds are highly efficient hydrogelators, forming gels in aqueous solution at low concentrations (0.03–0.60 wt %). Gels were formed through a pH switching methodology, by acidification of a basic solution (pH 14 to ≈4) either by addition of HCl or via the slow hydrolysis of glucono-δ-lactone. Frequently, gelation was accompanied by a dramatic switch in the absorption spectra of the gelators, resulting in a significant change in colour, typically from a vibrant orange to pale yellow. Each of the gels was capable of sequestering significant quantities of the aromatic cationic dye, methylene blue, from aqueous solution (up to 1.02 g of dye per gram of dry gelator). Cryo-transmission electron microscopy of two of the gels revealed an extensive network of high aspect ratio fibers. The structure of the fibers altered dramatically upon addition of 20 wt % of the dye, resulting in aggregation and significant shortening of the fibrils. This study demonstrates the feasibility for these novel gels finding application as inexpensive and effective water purification platforms.

Adaptive noise cancellation with fast tunable RBF network

This paper describes a novel adaptive noise cancellation system with fast tunable radial basis function (RBF). The weight coefficients of the RBF network are adapted by the multi-innovation recursive least square (MRLS) algorithm. If the RBF network performs poorly despite of the weight adaptation, an insignificant node with little contribution to the overall performance is replaced with a new node without changing the model size. Otherwise, the RBF network structure remains unchanged and only the weight vector is adapted. The simulation results show that the proposed approach can well cancel the noise in both stationary and nonstationary ANC systems.

Online modeling with tunable RBF network

In this paper, we propose a novel online modeling algorithm for nonlinear and nonstationary systems using a radial basis function (RBF) neural network with a fixed number of hidden nodes. Each of the RBF basis functions has a tunable center vector and an adjustable diagonal covariance matrix. A multi-innovation recursive least square (MRLS) algorithm is applied to update the weights of RBF online, while the modeling performance is monitored. When the modeling residual of the RBF network becomes large in spite of the weight adaptation, a node identified as insignificant is replaced with a new node, for which the tunable center vector and diagonal covariance matrix are optimized using the quantum particle swarm optimization (QPSO) algorithm. The major contribution is to combine the MRLS weight adaptation and QPSO node structure optimization in an innovative way so that it can track well the local characteristic in the nonstationary system with a very sparse model. Simulation results show that the proposed algorithm has significantly better performance than existing approaches.