Superhydrophobic electrospun POSS-PMMA copolymer fibres with highly ordered nanofibrillar and surface structures

A POSS-PMMA copolymer has been synthesised by conventional free-radical polymerisation reaction. Uniform electrospun fibres from this copolymer showed a water contact angle as high as 1651 with a sliding angle as low as 61. For the first time, we found that the electrospun fibres had a bundled nanofibril secondary structure with an ordered POSS morphology on the fibre surface.

Reactive block copolymer modified thermosets : highly ordered nanostructures and improved properties

A highly ordered poly(dimethyl siloxane)-poly(glycidyl methacrylate) (PDMS-PGMA) reactive diblock copolymer was synthesized and used to modify bisphenol A-type epoxy resin (ER). The PDMS-PGMA block copolymer consisted of epoxy-miscible PGMA blocks and an epoxy-immiscible PDMS block. The PGMA reactive block of the block copolymer formed covalent bonds with cured epoxy and was involved in the network formation, and the PDMS block phase separated to give different ordered and disordered nanostructures at different blend compositions. The solvent cast PDMS-PGMA diblock copolymer showed ordered hexagonal cylindrical morphology. A highly ordered morphology consisting of hexagonal cylinders inside the lamellar morphology was observed in the cured PDMS-PGMA block copolymer. In the cured ER/PDMS-PGMA blends, a variety of morphologies including lamellar, cubic and worm-like and spherical nanostructures were detected depending on the blend composition. Moreover, the addition of this reactive diblock copolymer significantly increases the hydrophobicity and the glass transition temperature. It also improves the tensile strength and tensile ductility of the nanostructured thermosets at low diblock copolymer contents.

Novel B-site ordered double perovskite Ba2Bi0.1Sc0.2Co1.7O6-x for highly efficient oxygen reduction reaction

Double perovskite Ba₂Bi_0.1Sc_0.2Co_1.7O_6-x (BBSC) demonstrates low polarization resistance between 600 and 750 °C due to the high oxygen reduction rate of BBSC as reflected by its large D_V and k values, which are derived from the face centered cubic structure and high cobalt content.

Analysing ordered nanostructures in epoxy thermosets using small angle X-ray scattering techniques

Small angle X-ray scattering (SAXS) is essential for the morphological investigation of nanostructured systems as it is a bulk sampling technique and provides information about the overall distribution of the components in the system. In our study we have used SAXS to identify various ordered and disordered morphologies in block copolymer modified epoxy thermosets. We have used a reactive block copolymer and hydrogen bonding block copolymer to modify epoxy resin (ER) to see the effect of various blocks on the morphological changes.

Induced ordered weighted averaging operators

Since the introduction of the ordered weighted averaging operator [18], the OWA has received great attention with applications in fields including decision making, recommender systems [8, 21], classification [10] and data mining [16] among others. The most important step in the calculation of the OWA is the permutation of the input vector according to the size of its arguments. In some applications, it makes sense that the inputs be reordered by values different to those used in calculation. For instance, if we have a number of mobile sensor readings, we may wish to allocate more importance to the reading taken from the sensor closest to us at a given point in time, rather than the largest reading.

Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering

Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small number indicating the degree of relevance. This poses a general problem of modelling and learning rank data with ties. In this paper, we propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on two application areas: (i) document ranking with the data from the recently held Yahoo! challenge, and (ii) collaborative filtering with movie data. The results demonstrate that the models are competitive against well-known rivals.

A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm

This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning, the ordering algorithm is first used to identify a fixed order of training patterns. The main aim is to reduce and/or avoid the formation of overlapping prototypes of different classes in FAM during learning. However, the effectiveness of the ordering algorithm in resolving overlapping prototypes of different classes is compromised when dealing with complex datasets. Ordered FAMDDA not only is able to determine a fixed order of training patterns for yielding good generalization, but also is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase. To illustrate the effectiveness of Ordered FAMDDA, a total of ten benchmark datasets are experimented. The results are analyzed and compared with those from FAM and Ordered FAM. The outcomes demonstrate that Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems.

An experimental study of original and ordered fuzzy ARTMAP neural networks in pattern classification tasks

This paper investigates the effectiveness of an ordering algorithm applied to the supervised Fuzzy ARTMAP (FAM) neural network in pattern classification tasks. Before presenting the input patterns to the FAM network (known as ordered FAM), a fixed order of input patterns is first identified using the ordering algorithm. An experimental study is conducted to compare the results from ordered FAM with the average and voting results from original FAM. In the study, a pool of the original FAM networks is trained using different sequences of input patterns, and the results are averaged. Outputs from various original FAM networks can also be combined using a majority voting strategy to reach a final result. A database comprising various symptoms and measurements of patients suffering from heart attack is used to evaluate the various schemes of the FAM network in medical pattern classification tasks. The results are compared, analyzed, and discussed.

Polymeric materials with ordered nanostructures templated from hexagonal lyotropic liquid crystals

In this study, successful methods have been established to retain the ordered nanostructures in polymer materials templated from hexagonal lyotropic liquid crystals, which potentially renders broad applications as biomedical and membrane materials.

Learning from ordered sets and applications in collaborative ranking

Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!/2)6.93145N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.