Enhancing English language learners’ text understanding through metaphor awareness

 The study examined awareness of metaphor as a tool to enhance English language learners’ understanding of texts with embedded metaphors. Findings revealed that an enhanced awareness of metaphor, as indicated by greater use of the metalanguage of metaphor, longer turns conversation and reflective journals, helped them get deeper text meaning.

Application of identification techniques to determine effect of carbon content in steels on rheological parameters during hot deformation

The objective of the present work is searching for the correlation between the carbon content in steels and the parameters of the rheological models, which are used to describe the materials behavior during hot plastic deformation. This correlation can be expected in the internal variable models, which are based on physical phenomena occurring in the material. Such a model, based on the dislocation density as the internal variable, is investigated in this work. The experiments including hot torsion tests are used for the analysis.
The procedure is composed of three parts. Plastometric tests were performed for steels with various carbon content. Optimization techniques were applied next to determine the coefficients in the internal variable rheological model for these steels. Two versions of the model are considered. One is based on the average dislocation density and the second accounts for the distribution of dislocation densities. Evaluation of correlation between carbon content and such coefficients in the models as activation energy for self diffusion, activation energy for recrystallization, grain boundary mobility, recovery coefficient etc. was the main objective of the work. In consequence, the model which may be used for simulation of hot forming processes for steels with various chemical compositions, is proposed.

Client identification and money laundering control : perspectives on the Financial Intelligence Centre Act 38 of 2001

The Financial Intelligence Centre Act 38 of 2001 (FICA) compels certain persons and institutions (defined as "accountable institutions'') to identify and verify the identity of a new client before any transaction may be concluded or any business relationship is established.1 Accountable institutions are listed in schedule ¹ to FICA and include banks, brokers, financial advisers, insurance companies, attorneys and estate agents. This duty to identify new clients came into effect on 30 June 2003. However, FICA also requires a similar procedure to be followed in respect of all current clients. Current clients are those with whom an accountable institution had business relationships on 30 June 2003.² After 30 June 2004 an institution may not conclude a transaction in the course of its business relationship with an unidentified current client, until it has established and verified that client's identity as prescribed. An institution that concludes any transaction in contravention of this prohibition, commits an offence and is liable to a fine not exceeding R10 million or to imprisonment of up to 15 years.³

The majority of accountable institutions and their clients failed to meet the June 2004 current client identification deadline.4 This failure posed serious economic and legal risks. With a few days to spare, the minister of finance granted a partial and temporary exemption in respect of these requirements. This article explores the statutory scheme for identification and re-identification of clients and some of the practical problems that were encountered. The June 2004 exemptions from these requirements are also considered and proposals for law reform are made.

The discussion of the FICA identification scheme necessitates the following brief overview of the international and South African money laundering control framework.

1 s 21(1) of FICA.
2 s 21(2) of FICA. See also s 82(2)(b).
3 s 46(2) of FICA read with s 68(1) of FICA.

Exploring metaphor as an alternative marketing language

Purpose: – The main aim of this paper is to stimulate more relevant and critical ideas about marketing and the wider management field by exploring the actual and potential contribution of metaphor to marketing theory and practice. The subsequent connections made can help contribute towards understanding and coping with the theory/practice gap.

Design/methodology/approach: – To date, the majority of metaphor application has tended to be literal and surface-level rather than theoretically grounded. This paper interrogates the literature surrounding metaphor in marketing and management fields, while also examining the contribution of other areas such as art. The paper constructs and debates the conceptual notion of the marketer as an artist.

Findings: – Incorporation of theoretically grounded metaphors into marketing theory can help develop a form of marketing which is capable of dealing with ambiguity, chaotic market conditions, creative thinking and practice.

Originality/value: – Adoption of a metaphorical approach to marketing research helps to instil a critical and creative ethos in the research process. Marketers are concerned with identification and exploitation of opportunities. Metaphor assists in the process by enhancing visualisation of these future directions. We live out our lives to a large degree through the making of metaphorical connections. We should therefore embrace more qualitative, creative associations in marketing theory, as well as practice.

Identification of implicit technical relationships in overlapping networks

Overlapping network techniques to expand the single sequential finish-to-start relationship between preceding and succeeding activities in scheduling construction projects have been developed for decades. The hidden logic relationships between activities and two virtual activities, Start and Finish, however limit the applications of these techniques in practice as they may lead to incorrect time parameters of some activities. In this research, a novel approach has been developed to identify those concealed relationships in a structured procedure by a two-dimensional nested diagram. An empirical study was carried out to demonstrate the network modification approach.

Spectral element model updating for damage identification using clonal selection algorithm

A spectral element model updating procedure is presented to identify damage in a structure using Guided wave propagation results. Two damage spectral elements (DSE1 and DSE2) are developed to model the local (cracks in reinforcement bar) and global (debonding between reinforcement bar and concrete) damage in one-dimensional homogeneous and composite waveguide, respectively. Transfer matrix method is adopted to assemble the stiffness matrix of multiple spectral elements. In order to solve the inverse problem, clonal selection algorithm is used for the optimization calculations. Two displacement-based functions and two frequency-based functions are used as objective functions in this study. Numerical simulations of wave propagation in a bare steel bar and in a reinforcement bar without and with various assumed damage scenarios are carried out. Numerically simulated data are then used to identify local and global damage of the steel rebar and the concrete-steel interface using the proposed method. Results show that local damage is easy to be identified by using any considered objective function with the proposed method while only using the wavelet energy-based objective function gives reliable identification of global damage. The method is then extended to identify multiple damages in a structure. To further verify the proposed method, experiments of wave propagation in a rectangular steel bar before and after damage are conducted. The proposed method is used to update the structural model for damage identification. The results demonstrate the capability of the proposed method in identifying cracks in steel bars based on measured wave propagation data.

Detecting K-complexes for sleep stage identification using nonsmooth optimisation

The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features.

Human identification from ECG signals via sparse representation of local segments

This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments. Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data. A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal. Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points. The method achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals.

Vibration based damage identification of a scale-model steel frame structure subjected to bolt connection failures

Large-span steel frame structures prove to be an ideal choice for their speed of construction, relatively low cost, strength, durability and structural design flexibility. For this type of structure, the beam-column connections are critical for its structural integrity and overall stability. This is because a steel frame generally fails first at its connectors, due to the change in stress redistribution with adjacent members and material related failures, caused by various factors such as fire, seismic activity or material deterioration. Since particular attention is required at a steel frame’s connection points, this study explores the applicability of a comprehensive structural health monitoring (SHM) method to identify early damage and prolong the lifespan of connection points of steel frames. An impact hammer test was performed on a scale-model steel frame structure, recording its dynamic response to the hammer strike via an accelerometer. The testing procedure included an intact scenario and two damage scenarios by unfastening four bolt connections in an accumulating order. Based entirely on time-domain experimental data for its calibration, an Auto Regressive Average Exogenous (ARMAX) model is used to create a simple and accurate model for vibration simulation. The calibrated ARMAX model is then used to identify various bolt-connection related damage scenarios via R² value. The findings in this study suggest that the proposed time-domain approach is capable of identifying structural damage in a parsimonious manner and can be used as a quick or initial solution.

The Integrating Theory, Evidence and Action (ITEA) method: a procedure for helping practitioners translate theory and research into action

Introduction: The aim of this article is to describe and explain a new method for integrating theory and evidence that enables practitioners to translate evidence into action applied in their practice. Method: A new multiple methods procedure called the Integrating Theory, Evidence and Action method is described. It is a mixed method that progresses through seven distinct steps: clinical question, framework, identification, deconstruction, analysis, reconstruction, and transfer/utilization. An example of using this method to review evidence around occupational therapy with people recovering from alcohol misuse and/or abuse is provided. Findings: This method highlights the importance of theory, tests the empirical strength of theories, includes diverse forms of evidence, and encourages the integration of knowledge within clinical practice. Conclusion: The Integrating Theory, Evidence and Action method is accessible and useful to practitioners and will support their efforts to make their practice evidence based. Current methods of evidence-based practice focus mostly on research evidence (particularly quantitative evidence); however, research is only one of the ways of knowing that practitioners draw upon to guide their practice. This method enables occupational therapists to integrate theory, evidence, and practice in a coherent and translatable way.

Swine influenza virus PA and neuraminidase gene reassortment into human H1N1iInfluenza Virus is associated with an altered pathogenic phenotype linked to increased MIP-2 expression

Swine are susceptible to infection by both avian and human influenza viruses, and this feature is thought to contribute to novel reassortant influenza viruses. In this study, the influenza virus reassortment rate in swine and human cells was determined. Coinfection of swine cells with 2009 pandemic H1N1 virus (huH1N1) and an endemic swine H1N2 (A/swine/Illinois/02860/09) virus (swH1N2) resulted in a 23% reassortment rate that was independent of α2,3- or α2,6-sialic acid distribution on the cells. The reassortants had altered pathogenic phenotypes linked to introduction of the swine virus PA and neuraminidase (NA) into huH1N1. In mice, the huH1N1 PA and NA mediated increased MIP-2 expression early postinfection, resulting in substantial pulmonary neutrophilia with enhanced lung pathology and disease. The findings support the notion that swine are a mixing vessel for influenza virus reassortants independent of sialic acid distribution. These results show the potential for continued reassortment of the 2009 pandemic H1N1 virus with endemic swine viruses and for reassortants to have increased pathogenicity linked to the swine virus NA and PA genes which are associated with increased pulmonary neutrophil trafficking that is related to MIP-2 expression. IMPORTANCE: Influenza A viruses can change rapidly via reassortment to create a novel virus, and reassortment can result in possible pandemics. Reassortments among subtypes from avian and human viruses led to the 1957 (H2N2 subtype) and 1968 (H3N2 subtype) human influenza pandemics. Recent analyses of circulating isolates have shown that multiple genes can be recombined from human, avian, and swine influenza viruses, leading to triple reassortants. Understanding the factors that can affect influenza A virus reassortment is needed for the establishment of disease intervention strategies that may reduce or preclude pandemics. The findings from this study show that swine cells provide a mixing vessel for influenza virus reassortment independent of differential sialic acid distribution. The findings also establish that circulating neuraminidase (NA) and PA genes could alter the pathogenic phenotype of the pandemic H1N1 virus, resulting in enhanced disease. The identification of such factors provides a framework for pandemic modeling and surveillance.