Brand salience for fast-moving consumer goods : an empirically based model

Brand salience, or the prominence of a brand in memory, has been linked to brand choice and purchase by consumers. The research reported in this paper proposed and tested a model of brand salience for fast-moving consumer goods, which incorporates knowledge, media consumption, and brand image as antecedents. A quasi-experimental method was utilised, where 270 respondents undertook a free recall exercise using category cues, and then completed multiitem measures of brand knowledge, brand associations, and purchase likelihood. Analysis of the data using SEM found support for an empirical model of brand salience where there was a relationship between brand salience and purchase likelihood. The empirical evidence supports building a brand in a primary category, in order to build the depth and breadth of the brand’s associations in consumer memory.

Altered fast- and slow-twitch muscle fibre characteristics in female mice with a (S248F) knock-in mutation of the brain neuronal nicotinic acetylcholine receptor

We generated a mouse line with a missense mutation (S248F) in the gene (CHRNA4) encoding the α4 subunit of neuronal nicotinic acetylcholine receptor (nAChR). Mutant mice demonstrate brief nicotine induced dystonia that resembles the clinical events seen in patients with the same mutation. Drug-induced dystonia is more pronounced in female mice, thus our aim was to determine if the S248F mutation changed the properties of fast- and slow-twitch muscle fibres from female mutant mice. Reverse transcriptase-PCR confirmed CHRNA4 gene expression in the brain but not skeletal muscles in normal and mutant mice. Ca²⁺ and Sr²⁺ force activation curves were obtained using skinned muscle fibres prepared from slow-twitch (soleus) and fast-twitch (EDL) muscles. Two significant results were found: (1) the (pCa₅₀ - pSr₅₀) value from EDL fibres was smaller in mutant mice than in wild type (1.01 vs. 1.30), (2) the percentage force produced at pSr 5.5 was larger in mutants than in wild type (5.76 vs. 0.24%). Both results indicate a shift to slow-twitch characteristics in the mutant. This conclusion is supported by the identification of the myosin heavy chain (MHC) isoforms. Mutant EDL fibres expressed MHC I (usually only found in slow-twitch fibres) as well as MHC IIa. Despite the lack of spontaneous dystonic events, our findings suggest that mutant mice may be having subclinical events or the mutation results in a chronic alteration to muscle neural input.

Fast finite-time consensus of a class of high-order uncertain nonlinear systems

This paper poses and solves a new problem of consensus control where the task is to make the fixed-topology multi-agent network, with each agent described by an uncertain nonlinear system in chained form, to reach consensus in a fast finite time. Our development starts with a set of new sliding mode surfaces. It is proven that, on these sliding mode surfaces, consensus can be achieved if the communication graph has the proposed directed spanning tree. Next, we introduce the multi-surface sliding mode control to drive the sliding variables to the sliding mode surfaces in a fast finite time. The control Lyapunov function for fast finite time stability, motivated by the fast terminal sliding mode control, is used to prove the reachability of the sliding mode surface. A recursive design procedure is provided, which guarantees the boundedness of the control input.

Fast responsive and morphologically robust thermo-responsive hydrogel nanofibres from poly(N-isopropylacrylamide) and POSS crosslinker

Stable thermo-responsive hydrogel nanofibres have been prepared by electrospinning of commercial poly(N-isopropylacrylamide) (PNIPAM) in the presence of a polyhedral oligomeric silsesquioxane (POSS) possessing eight epoxide groups and of an organic-base catalyst, followed by a heat curing treatment. The nanofibres showed excellent hydrogel characteristics with fast swelling and de-swelling responses triggered by temperature changes. They were also morphologically robust as their physical integrity was preserved upon repeated hydration/dehydration cycles and exposure to solvents.

Fast colorimetric detection of copper ions using L-Cysteine functionalized gold nanoparticles

This communication reports an efficient visual detection method of Cu²⁺ by L-cysteine functionalized gold nanoparticles in aqueous solution. Upon exposure to Cu²⁺, the gold nanoparticle solution changed from red to blue, in response to surface plasmon absorption of dispersed and aggregated nanoparticles. This colorimetric sensor allows a rapid quantitative assay of Cu²⁺ down to the concentration range of 10⁻⁵ M. Recognition of Cu²⁺ and formation of the aggregates are proposed to occur via a 2 : 1 sandwich complex between L-cysteine and Cu²⁺.

Fast computation of trimmed means

We present two methods of calculating trimmed means without sorting the data in O(n) time. The existing method implemented in major statistical packages relies on sorting, which takes O(n log n) time. The proposed algorithm is based on the quickselect algorithm for calculating order statistics with O(n) expected running time. It is an order of magnitude faster than the existing method for large data sets.

Fast mining of non-derivable episode rules in complex sequences

Researchers have been endeavoring to discover concise sets of episode rules instead of complete sets in sequences. Existing approaches, however, are not able to process complex sequences and can not guarantee the accuracy of resulting sets due to the violation of anti-monotonicity of the frequency metric. In some real applications, episode rules need to be extracted from complex sequences in which multiple items may appear in a time slot. This paper investigates the discovery of concise episode rules in complex sequences. We define a concise representation called non-derivable episode rules and formularize the mining problem. Adopting a novel anti-monotonic frequency metric, we then develop a fast approach to discover non-derivable episode rules in complex sequences. Experimental results demonstrate that the utility of the proposed approach substantially reduces the number of rules and achieves fast processing.

Fast cross-validation of kernel fisher discriminant classifiers

Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (^4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.

A fast kernel dimension reduction algorithm with applications to face recognition

This paper presents a novel dimensionality reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-class distance and the sum of the within-class variances of the training samples for a given reduced dimension. This algorithm has lower complexity than the recently reported kernel dimension reduction(KDR) for supervised learning. We conducted several simulations with large training datasets, which demonstrate that the proposed algorithm has similar performance or is marginally better compared with KDR whilst having the advantage of computational efficiency. Further, we applied the proposed dimension reduction algorithm to face recognition in which the number of training samples is very small. This proposed face recognition approach based on the new algorithm outperforms the eigenface approach based on the principle component analysis (PCA), when the training data is complete, that is, representative of the whole dataset.