Automated identification and tracking of polar-cap plasma patches at solar minimum

A method of automatically identifying and tracking polar-cap plasma patches, utilising data inversion and feature-tracking methods, is presented. A well-established and widely used 4-D ionospheric imaging algorithm, the Multi-Instrument Data Assimilation System (MIDAS), inverts slant total electron content (TEC) data from ground-based Global Navigation Satellite System (GNSS) receivers to produce images of the free electron distribution in the polar-cap ionosphere. These are integrated to form vertical TEC maps. A flexible feature-tracking algorithm, TRACK, previously used extensively in meteorological storm-tracking studies is used to identify and track maxima in the resulting 2-D data fields. Various criteria are used to discriminate between genuine patches and "false-positive" maxima such as the continuously moving day-side maximum, which results from the Earth's rotation rather than plasma motion. Results for a 12-month period at solar minimum, when extensive validation data are available, are presented. The method identifies 71 separate structures consistent with patch motion during this time. The limitations of solar minimum and the consequent small number of patches make climatological inferences difficult, but the feasibility of the method for patches larger than approximately 500 km in scale is demonstrated and a larger study incorporating other parts of the solar cycle is warranted. Possible further optimisation of discrimination criteria, particularly regarding the definition of a patch in terms of its plasma concentration enhancement over the surrounding background, may improve results.

PDFOS: PDF estimation based over-sampling for imbalanced two-class problems

This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.

Assessing pragmatic interoperability of information systems from a semiotic perspective

Most of studies on interoperability of systems integration focus on technical and semantic levels, but hardly extend investigations on pragmatic level. Our past work has addressed pragmatic interoperability, which is concerned with the relationship between signs and the potential behaviour and intention of responsible agents. We also define the pragmatic interoperability as a level concerning with the aggregation and optimisation of various business processes for achieving intended purposes of different information systems. This paper, as the extension of our previous research, is to propose an assessment method for measuring pragmatic interoperability of information systems. We firstly propose interoperability analysis framework, which is based on the concept of semiosis. We then develop pragmatic interoperability assessment process from two dimensions including six aspects (informal, formal, technical, substantive, communication, and control). We finally illustrate the assessment process in an example.

Elastic net orthogonal forward regression

An efficient two-level model identification method aiming at maximising a model׳s generalisation capability is proposed for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularisation parameters in the elastic net are optimised using a particle swarm optimisation (PSO) algorithm at the upper level by minimising the leave one out (LOO) mean square error (LOOMSE). There are two elements of original contributions. Firstly an elastic net cost function is defined and applied based on orthogonal decomposition, which facilitates the automatic model structure selection process with no need of using a predetermined error tolerance to terminate the forward selection process. Secondly it is shown that the LOOMSE based on the resultant ENOFR models can be analytically computed without actually splitting the data set, and the associate computation cost is small due to the ENOFR procedure. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Consumer acceptability and sensory profile of cooked broccoli with mustard seeds added to improve chemoprotective properties

Broccoli, a rich source of glucosinolates, is a commonly consumed vegetable of the Brassica family. Hydrolysis products of glucosinolates, isothiocyanates, have been associated with health benefits and contribute to the flavour of Brassica. However, boiling broccoli causes the myrosinase enzyme needed for hydrolysis to denature. In order to ensure hydrolysis, broccoli must either be mildly cooked or active sources of myrosinase, such as mustard seed powder, can be added post-cooking. In this study, samples of broccoli were prepared in six different ways; standard boiling with and without mustard seeds, sous-vide cooking at low temperature (70 °C) and sous-vide cooking at higher temperature (100 ºC) without mustard and with mustard at two different concentrations. The majority of consumers disliked the mildly cooked broccoli samples (70 ºC, 12 min, sous-vide) which had a hard and stringy texture. The highest mean consumer liking was for standard boiled samples (100 ºC, 7 min). Addition of 1% mustard seed powder developed sensory attributes such as pungency, burning sensation, mustard odour and flavour. One cluster of consumers (32%) found mustard seeds to be a good complement to cooked broccoli, however, the majority disliked the mustard-derived sensory attributes. Where the mustard seeds were partially processed, doubling the addition to 2% led to only the same level of mustard flavour and pungency as 1% unprocessed seeds, and mean consumer liking remained unaltered. This suggests that optimisation of the addition level of partially processed mustard seeds may be a route to enhance bioactivity of cooked broccoli without compromising consumer acceptability.

Donor-acceptor π−π stacking interactions: from small molecule complexes to healable supramolecular polymer networks

This chapter presents selected literature examples to review the development of the use of donor–acceptor π–π stacking interactions as transient cross-links in supramolecular polymer networks. The chapter examines notable examples of these highly specific and directional interactions and illustrates how they can be utilised to reliably produce functional supramolecular, self-assembled systems. Knowledge gained from these fundamental studies has enabled the design, synthesis and application of donor–acceptor stacked supramolecular motifs in non-covalent polymer networks, which is exemplified through detailing the production, physical properties and optimisation of healable materials.

Defining key structural determinants for the pro-osteogenic activity of flavonoids

Epidemiological studies suggest that fruits and vegetables may play a role in promoting bone growth and preventing age-related bone loss, attributable, at least in part, to phytochemicals such as flavonoids stimulating osteoblastogenesis. Through systematically screening the effect of flavonoids on the osteogenic differentiation of human mesenchymal stem cells in vitro, and correlating activity with chemical structure using comparative molecular field analysis, we have successfully identified important structural features which relate to their activity, as well as reliably predicting the activity of compounds with unknown activity. Contour maps emphasised the importance of electronegativity, steric bulk, and a 2-C-3-C double bond at the flavonoid C-ring, as well as overall electropositivity and reduced steric bulk at the flavonoid B-ring. These results support a role for certain flavonoids in promoting osteogenic differentiation, thus their potential for preventing skeletal deterioration, as well as providing a foundation for the lead optimisation of novel bone anabolics.

Production of acid whey hydrolysates applying an integrative process: effect of calcium on process performance

High ionic calcium concentration and the absence of caseinmacropeptides (CMP) in acid whey could influence the production of angiotensin-I-converting enzyme (ACE)-inhibitory hydrolysate and its bioactivity through the application of the integrative process. Therefore, the aim of the present study was to produce a hydrolysate from acid whey applying the integrative process. Process performance was evaluated based on protein adsorption capacity and conversion in relation to ACE-inhibitory activity (ACEi%) and ionic calcium concentration. Hydrolysates with high potency of their biological activity were produced (IC50 = 206-353 μg mL-1). High ionic calcium concentration in acid whey contributed to ACE-inhibitory activity. However, low β-lactoglobulin adsorption and conversion was observed. Optimisation of the resin volume increased the adsorption of β-lactoglobulin significantly but with lower selectivity. The changes in conversion value were not significant even at higher concentration of enzyme. Several ACE inhibitors derived from β-lactoglobulin that were identified before in sweet whey hydrolysates such as, IIAEKT, IIAE, IVTQ, LIVTQ, LIVTQT, LDAQ and LIVT were found. New peptides such as, SNICNI and ECCHGD derived from α-lactalbumin and BSA respectively were identified.