Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Engineering hierarchical nanostructures by elastocapillary self-assembly

Surfaces coated with nanoscale filaments such as silicon nanowires and carbon nanotubes are potentially compelling for high-performance battery and capacitor electrodes, photovoltaics, electrical interconnects, substrates for engineered cell growth, dry adhesives, and other smart materials. However, many of these applications require a wet environment or involve wet processing during their synthesis. The capillary forces introduced by these wet environments can lead to undesirable aggregation of nanoscale filaments, but control of capillary forces can enable manipulation of the filaments into discrete aggregates and novel hierarchical structures. Recent studies suggest that the elastocapillary self-assembly of nanofilaments can be a versatile and scalable means to build complex and robust surface architectures. To enable a wider understanding and use of elastocapillary self-assembly as a fabrication technology, we give an overview of the underlying fundamentals and classify typical implementations and surface designs for nanowires, nanotubes, and nanopillars made from a wide variety of materials. Finally, we discuss exemplary applications and future opportunities to realize new engineered surfaces by the elastocapillary self-assembly of nanofilaments. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative approaches for characterising fibrillar protein nanostructures

Polypeptide sequences have an inherent tendency to self-assemble into filamentous nanostructures commonly known as amyloid fibrils. Such self-assembly is used in nature to generate a variety of functional materials ranging from protective coatings in bacteria to catalytic scaffolds in mammals. The aberrant self-assembly of misfolded peptides and proteins is also, however, implicated in a range of disease states including neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. It is increasingly evident that the intrinsic material properties of these structures are crucial for understanding the thermodynamics and kinetics of the pathological deposition of proteins, particularly as the mechanical fragmentation of aggregates enhances the rate of protein deposition by exposing new fibril ends which can promote further growth. We discuss here recent advances in physical techniques that are able to characterise the hierarchical self-assembly of misfolded protein molecnles and define their properties. © 2010 Materials Research Society.

Wet-spinning of amyloid protein nanofibers into multifunctional high-performance biofibers.

Amyloid nanofibers derived from hen egg white lysozyme were processed into macroscopic fibers in a wet-spinning process based on interfacial polyion complexation using a polyanionic polysaccharide as cross-linker. As a result of their amyloid nanostructure, the hierarchically self-assembled protein fibers have a stiffness of up to 14 GPa and a tensile strength of up to 326 MPa. Fine-tuning of the polyelectrolytic interactions via pH allows to trigger the release of small molecules, as demonstrated with riboflavin-5'-phophate. The amyloid fibrils, highly oriented within the gellan gum matrix, were mineralized with calcium phosphate, mimicking the fibrolamellar structure of bone. The formed mineral crystals are highly oriented along the nanofibers, thus resulting in a 9-fold increase in fiber stiffness.

Demonstration of multi wavelength picosecond pulse generation with resonant RF drive using an monolithically integrated multi-grating cavity (MGC) laser

Multiwavelength pulses were generated using a monolithically integrated device. The device used is an integrated InGaAs/InGaAsP/InP multi-wavelength laser fabricated by selective area regrowth. The device self pulsated on all of the four wavelength channels. 48 ps pulses were obtained which were measured by a 50GHz oscilloscope and 32GHz photodiode which was not bandwidth limited. Simultaneous multi-wavelength pulse generation was also achieved.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Effect of surface roughness on rarefied-gas heat transfer in microbearings

In this Letter, the rarefaction and roughness effects on the heat transfer process in gas microbearings are investigated. A heat transfer model is developed by introducing two-variable Weierstrass-Mandelbrot (W-M) function with fractal geometry. The heat transfer problem in the multiscale self-affine rough microbearings at slip flow regime is analyzed and discussed. The results show that rarefaction has more significant effect on heat transfer in rough microbearings with lower fractal dimension. The negative influence of roughness on heat transfer found to be the Nusselt number reduction. The heat transfer performance can be optimized with increasing fractal dimension of the rough surface. © 2012 Elsevier B.V. All rights reserved.

Slip flow and heat transfer in microbearings with fractal surface topographies

The effects of random surface roughness on slip flow and heat transfer in microbearings are investigated. A three-dimensional random surface roughness model characterized by fractal geometry is used to describe the multiscale self-affine roughness, which is represented by the modified two-variable Weierstrass- Mandelbrot (W-M) functions, at micro-scale. Based on this fractal characterization, the roles of rarefaction and roughness on the thermal and flow properties in microbearings are predicted and evaluated using numerical analyses and simulations. The results show that the boundary conditions of velocity slip and temperature jump depend not only on the Knudsen number but also on the surface roughness. It is found that the effects of the gas rarefaction and surface roughness on flow behavior and heat transfer in the microbearing are strongly coupled. The negative influence of roughness on heat transfer found to be the Nusselt number reduction. In addition, the effects of temperature difference and relative roughness on the heat transfer in the bearing are also analyzed and discussed. © 2012 Elsevier Ltd. All rights reserved.

Fabrication, densification, and replica molding of 3D carbon nanotube microstructures

The introduction of new materials and processes to microfabrication has, in large part, enabled many important advances in microsystems, labon- a-chip devices, and their applications. In particular, capabilities for cost-effective fabrication of polymer microstructures were transformed by the advent of soft lithography and other micromolding techniques 1,2, and this led a revolution in applications of microfabrication to biomedical engineering and biology. Nevertheless, it remains challenging to fabricate microstructures with well-defined nanoscale surface textures, and to fabricate arbitrary 3D shapes at the micro-scale. Robustness of master molds and maintenance of shape integrity is especially important to achieve high fidelity replication of complex structures and preserving their nanoscale surface texture. The combination of hierarchical textures, and heterogeneous shapes, is a profound challenge to existing microfabrication methods that largely rely upon top-down etching using fixed mask templates. On the other hand, the bottom-up synthesis of nanostructures such as nanotubes and nanowires can offer new capabilities to microfabrication, in particular by taking advantage of the collective self-organization of nanostructures, and local control of their growth behavior with respect to microfabricated patterns. Our goal is to introduce vertically aligned carbon nanotubes (CNTs), which we refer to as CNT "forests", as a new microfabrication material. We present details of a suite of related methods recently developed by our group: fabrication of CNT forest microstructures by thermal CVD from lithographically patterned catalyst thin films; self-directed elastocapillary densification of CNT microstructures; and replica molding of polymer microstructures using CNT composite master molds. In particular, our work shows that self-directed capillary densification ("capillary forming"), which is performed by condensation of a solvent onto the substrate with CNT microstructures, significantly increases the packing density of CNTs. This process enables directed transformation of vertical CNT microstructures into straight, inclined, and twisted shapes, which have robust mechanical properties exceeding those of typical microfabrication polymers. This in turn enables formation of nanocomposite CNT master molds by capillary-driven infiltration of polymers. The replica structures exhibit the anisotropic nanoscale texture of the aligned CNTs, and can have walls with sub-micron thickness and aspect ratios exceeding 50:1. Integration of CNT microstructures in fabrication offers further opportunity to exploit the electrical and thermal properties of CNTs, and diverse capabilities for chemical and biochemical functionalization 3. © 2012 Journal of Visualized Experiments.

Non-destructive characterization of structural hierarchy within aligned carbon nanotube assemblies

Understanding and controlling the hierarchical self-assembly of carbon nanotubes (CNTs) is vital for designing materials such as transparent conductors, chemical sensors, high-performance composites, and microelectronic interconnects. In particular, many applications require high-density CNT assemblies that cannot currently be made directly by low-density CNT growth, and therefore require post-processing by methods such as elastocapillary densification. We characterize the hierarchical structure of pristine and densified vertically aligned multi-wall CNT forests, by combining small-angle and ultra-small-angle x-ray scattering (USAXS) techniques. This enables the nondestructive measurement of both the individual CNT diameter and CNT bundle diameter within CNT forests, which are otherwise quantified only by delicate and often destructive microscopy techniques. Our measurements show that multi-wall CNT forests grown by chemical vapor deposition consist of isolated and bundled CNTs, with an average bundle diameter of 16 nm. After capillary densification of the CNT forest, USAXS reveals bundles with a diameter 4 m, in addition to the small bundles observed in the as-grown forests. Combining these characterization methods with new CNT processing methods could enable the engineering of macro-scale CNT assemblies that exhibit significantly improved bulk properties. © 2011 American Institute of Physics.

Leadership of healthcare commissioning networks in England: a mixed-methods study on clinical commissioning groups.

To explore the relational challenges for general practitioner (GP) leaders setting up new network-centric commissioning organisations in the recent health policy reform in England, we use innovation network theory to identify key network leadership practices that facilitate healthcare innovation.

Statistics anxiety, state anxiety during an examination, and academic achievement.

BACKGROUND: A large proportion of students identify statistics courses as the most anxiety-inducing courses in their curriculum. Many students feel impaired by feelings of state anxiety in the examination and therefore probably show lower achievements. AIMS: The study investigates how statistics anxiety, attitudes (e.g., interest, mathematical self-concept) and trait anxiety, as a general disposition to anxiety, influence experiences of anxiety as well as achievement in an examination. SAMPLE: Participants were 284 undergraduate psychology students, 225 females and 59 males. METHODS: Two weeks prior to the examination, participants completed a demographic questionnaire and measures of the STARS, the STAI, self-concept in mathematics, and interest in statistics. At the beginning of the statistics examination, students assessed their present state anxiety by the KUSTA scale. After 25 min, all examination participants gave another assessment of their anxiety at that moment. Students' examination scores were recorded. Structural equation modelling techniques were used to test relationships between the variables in a multivariate context. RESULTS: Statistics anxiety was the only variable related to state anxiety in the examination. Via state anxiety experienced before and during the examination, statistics anxiety had a negative influence on achievement. However, statistics anxiety also had a direct positive influence on achievement. This result may be explained by students' motivational goals in the specific educational setting. CONCLUSIONS: The results provide insight into the relationship between students' attitudes, dispositions, experiences of anxiety in the examination, and academic achievement, and give recommendations to instructors on how to support students prior to and in the examination.

Manufacturing excellent engineers: Skill development in a Masters programme

An MPhil programme, delivered by the Engineering Department at the University of Cambridge, claims to be excellent at preparing graduates for manufacturing industry careers. The course uses a combination of different educational experiences, including industry-based assignments, industrial visits and practical exercises. This research explores how problem solving skills are developed during the first module, Induction, which is designed to enable students to undertake their first industrial assignment. From the literature, four conditions necessary for skill development were identified: Provision of a skill description, making explicit key components A number of different experiences with a range of contextual variables A teaching process which includes regular feedback and student reflection Students motivated to learn. These were used to construct a skill development framework (SDF). Using a case study research design, multiple types of evidence were collected to test for the above conditions using both classroom observation and questionnaire methods. The results confirmed the presence of the SDF conditions at different levels, with reflection aspects considered the weakest. Conflicting results were obtained regarding the students' self-awareness of skill levels. A plausible explanation is a change in the students' frame of reference. This initial study set out to develop a better understanding of the process of skill development. Whilst the SDF appears reasonable, there is a need for further work in three broad areas of defining skills, assessing skills and developing reflection skills.