An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Multivariate analysis of the dielectric response of materials modeled using networks of resistors and capacitors

We discuss the modeling of dielectric responses of electromagnetically excited networks which are composed of a mixture of capacitors and resistors. Such networks can be employed as lumped-parameter circuits to model the response of composite materials containing conductive and insulating grains. The dynamics of the excited network systems are studied using a state space model derived from a randomized incidence matrix. Time and frequency domain responses from synthetic data sets generated from state space models are analyzed for the purpose of estimating the fraction of capacitors in the network. Good results were obtained by using either the time-domain response to a pulse excitation or impedance data at selected frequencies. A chemometric framework based on a Successive Projections Algorithm (SPA) enables the construction of multiple linear regression (MLR) models which can efficiently determine the ratio of conductive to insulating components in composite material samples. The proposed method avoids restrictions commonly associated with Archie’s law, the application of percolation theory or Kohlrausch-Williams-Watts models and is applicable to experimental results generated by either time domain transient spectrometers or continuous-wave instruments. Furthermore, it is quite generic and applicable to tomography, acoustics as well as other spectroscopies such as nuclear magnetic resonance, electron paramagnetic resonance and, therefore, should be of general interest across the dielectrics community.

Real estate markets and the macroeconomy: a dynamic coherence framework

This paper analyzes the dynamic interactions between real estate markets, in the US and the UK and their macroeconomic environments. We apply a new approach based on a dynamic coherence function (DCF) to study these interactions bringing together different real estate markets (the securitized market, the commercial market and the residential market). The results suggest that there is a common trend that drives the different real estate markets in the UK and the US, particularly in the long run, since they have a similar shape of the DCF. We also find that, in the US, wealth and housing expenditure channels are very conductive during real estate crises. However, in the UK, only the wealth effect is significant as a transmission channel during real estate market downturns. In addition, real estate markets in the UK and the US react differently to institutional shocks. This brings some insights on the conduct of monetary policy in order to avoid disturbances in real estate markets.

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models.

Helium ion microscopy visualizes lipid nanodomains in mammalian cells

Cell membranes are composed of two-dimensional bilayers of amphipathic lipids, which allow a lateral movement of the respective membrane components. These components are arranged in an inhomogeneous manner as transient micro- and nanodomains, which are believed to be crucially involved in the regulation of signal transduction pathways in mammalian cells. Because of their small size (diameter 10-200 nm), membrane nanodomains cannot be directly imaged using conventional light microscopy. Here, we present direct visualization of cell membrane nanodomains by helium ion microscopy (HIM). We show that HIM is capable to image biological specimens without any conductive coating, and that HIM images clearly allow the identification of nanodomains in the ultrastructure of membranes with 1.5 nm resolution. The shape of these nanodomains is preserved by fixation of the surrounding unsaturated fatty acids while saturated fatty acids inside the nanodomains are selectively removed. Atomic force microscopy, fluorescence microscopy, 3D structured illumination microscopy and direct stochastic optical reconstruction microscopy provide additional evidence that the structures in the HIM images of cell membranes originate from membrane nanodomains. The nanodomains observed by HIM have an average diameter of 20 nm and are densely arranged with a minimal nearest neighbor distance of ~15 nm.

What is Carbon? Conceptualising carbon and capabilities in the context of community sequestration projects in the global South

Carbon has been described as a ‘surreal commodity’. Whilst carbon trading, storage, sequestration and emissions have become a part of the contemporary climate lexicon, how carbon is understood, valued and interpreted by actors responsible for implementing carbon sequestration projects is still unclear. In this review paper, we are concerned with how carbon has come to take on a range of meanings, and in particular, we appraise what is known about the situated meanings that people involved in delivering, and participating in, carbon sequestration projects in the global South assign to this complex element. Whilst there has been some reflection on the new meanings conferred on carbon via the neoliberal processes of marketisation, and how these processes interact with historical and contemporary narratives of environmental change, less is known about how these meanings are (re)produced and (re)interpreted locally. We review how carbon has been defined both as a chemical element and as a tradable, marketable commodity, and discuss the implications these global meanings might have for situated understandings, particularly linked to climate change narratives, amongst communities in the global South. We consider how the concept of carbon capabilities, alongside theoretical notions of networks, assemblages and local knowledges of the environment and nature, might be useful in beginning to understand how communities engage with abstract notions of carbon. We discuss the implications of specific values attributed to carbon, and therefore to different ecologies, for wider conceptualisations of how nature is valued, and climate is understood, and particularly how this may impact on community interactions with carbon sequestration projects. Knowing more about how people understand, value and know carbon allows policies to be better informed and practices more effectively targeted at engaging local populations meaningfully in carbon-related projects.

Carbon nanotubes in electrospun polyethylene oxide nanofibres: a potential route to conducting nanofibres

Polyethylene oxide solution containing multi-walled carbon nanotubes have been electrospun onto a rotating collector to produce highly aligned arrays of electrospun nanofibers ranging in diameters from (200 – 360) nanometres. The addition of a surfactant (Triton X-100)is highly effective in dispersing carbon nanotube within an aqueous solution of polyethylene oxide and the resulting mixture can be electrospun without excessive clumping to produce nanofibers containing high loadings of nanotubes; in this case up to 5% wt thereby providing an effective route to electrically conductive nanofibres.

The impact of charge transfer and structural disorder on the thermoelectric properties of cobalt intercalated TiS2

A family of phases, CoxTiS2 (0 ≤ x ≤ 0.75) has been prepared and characterised by powder X-ray and neutron diffraction, electrical and thermal transport property measurements, thermal analysis and SQUID magnetometry. With increasing cobalt content, the structure evolves from a disordered arrangement of cobalt ions in octahedral sites located in the van der Waals’ gap (x ≤ 0.2), through three different ordered vacancy phases, to a second disordered phase at x ≥ 0.67. Powder neutron diffraction reveals that both octahedral and tetrahedral inter-layer sites are occupied in Co0.67TiS2. Charge transfer from the cobalt guest to the TiS2 host affords a systematic tuning of the electrical and thermal transport properties. At low levels of cobalt intercalation (x < 0.1), the charge transfer increases the electrical conductivity sufficiently to offset the concomitant reduction in |S|. This, together with a reduction in the overall thermal conductivity leads to thermoelectric figures of merit that are 25 % higher than that of TiS2, ZT reaching 0.30 at 573 K for CoxTiS2 with 0.04 ≤ x ≤ 0.08. Whilst the electrical conductivity is further increased at higher cobalt contents, the reduction in |S| is more marked due to the higher charge carrier concentration. Furthermore both the charge carrier and lattice contributions to the thermal conductivity are increased in the electrically conductive ordered-vacancy phases, with the result that the thermoelectric performance is significantly degraded. These results illustrate the competition between the effects of charge transfer from guest to host and the disorder generated when cobalt cations are incorporated in the inter-layer space.