Enhancing students' self-concepts and related constructs: A critical longitudinal analysis capitalising on and combining promising enhancement techniques for educational settings

Enhancing children's self-concepts is widely accepted as a critical educational outcome of schooling and is postulated as a mediating variable that facilitates the attainment of other desired outcomes such as improved academic achievement. Despite considerable advances in self-concept research, there has been limited progress in devising teacher-administered enhancement interventions. This is unfortunate as teachers are crucial change agents during important developmental periods when self-concept is formed. The primary aim of the present investigation is to build on the promising features of previous self-concept enhancement studies by: (a) combining two exciting research directions developed by Burnett and Craven to develop a potentially powerful cognitive-based intervention; (b) incorporating recent developments in theory and measurement to ensure that the multidimensionality of self-concept is accounted for in the research design; (c) fully investigating the effects of a potentially strong cognitive intervention on reading, mathematics, school and learning self-concepts by using a large sample size and a sophisticated research design; (d) evaluating the effects of the intervention on affective and cognitive subcomponents of reading, mathematics, school and learning self-concepts over time to test for differential effects of the intervention; (e) modifying and extending current procedures to maximise the successful implementation of a teacher-mediated intervention in a naturalistic setting by incorporating sophisticated teacher training as suggested by Hattie (1992) and including an assessment of the efficacy of implementation; and (f) examining the durability of effects associated with the intervention.

Orientation robust transmit polarization diversity techniques

Polarisation diversity is a technique to improve the quality of mobile communications, but its reliability is suboptimal because it depends on the mobile channel and the antenna orientations at both ends of the mobile link. A method to optimise the reliability is established by minimising the dependency on antenna orientations. While the mobile base station can have fixed antenna orientation, the mobile terminal is typically a handheld device with random orientations. This means orientation invariance needs to be established at the receiver in the downlink, and at the transmitter in the uplink. This research presents separate solutions for both cases, and is based on the transmission of an elliptically polarised signal synthesised from the channel statistics. Complete receiver orientation invariance is achieved in the downlink. Effects of the transmitter orientation are minimised in the uplink.

Combining ability, genetic diversity and heterosis in relation to F1 performance of tropically adapted shrunken (sh2) sweet corn lines

With 6 tables Abstract The objectives of this study were to evaluate the importance of heterosis for agronomic and quality traits in shrunken (sh2) sweet corn, assess the usefulness of combining ability to predict the value of parents and their crosses for further genetic improvement and examine whether genetic divergence can predict heterosis or F1 performance. Ten genetically diverse shrunken (sh2) sweet corn inbred lines were used to generate 45 F1s. F1s and parents were evaluated for agronomic and quality traits across environments. Heterosis was more important for yield-related traits than it was for ear aspects and eating quality. Heterosis for most traits was mostly dependent on dominance genetic effects of parental lines. Parents and F1per se performance were highly correlated with general combining ability effects and mid-parent values, respectively, for most traits. Hybrid performance for flavour and plant height was significantly but weakly related to simple sequence repeat (SSR)-based genetic distance (GD). Phenotypic distance (PD), estimated from phenotypic traits was correlated with heterosis for total soluble solids, ear length and flavour. © 2012 State of Queensland.

Combining indentation and diffusion couple techniques for combinatorial discovery of high temperature shape memory alloys

We demonstrate the possibility of accelerated identification of potential compositions for high-temperature shape memory alloys (SMAs) through a combinatorial material synthesis and analysis approach, wherein we employ the combination of diffusion couple and indentation techniques. The former was utilized to generate smooth and compositionally graded inter-diffusion zones (IDZs) in the Ni-Ti-Pd ternary alloy system of varying IDZ thickness, depending on the annealing time at high temperature. The IDZs thus produced were then impressed with an indenter with a spherical tip so as to inscribe a predetermined indentation strain. Subsequent annealing of the indented samples at various elevated temperatures, T-a, ranging between 150 and 550 degrees C allows for partial to full relaxation of the strain imposed due to the shape memory effect. If T-a is above the austenite finish temperature, A(f), the relaxation will be complete. By measuring the depth recovery, which serves as a proxy for the shape recovery characteristic of the SMA, a three-dimensional map in the recovery temperature composition space is constructed. A comparison of the published Af data for different compositions with the Ta data shows good agreement when the depth recovery is between 70% and 80%, indicating that the methodology proposed in this paper can be utilized for the identification of promising compositions. Advantages and further possibilities of this methodology are discussed.

Comparison of techniques to examine the diversity of fungi in adult patients with cystic fibrosis

This study compares conventional and molecular techniques for the detection of fungi in 77 adult cystic fibrosis (CF) patients. Three different methods were investigated, i.e., (1) conventional microbiological culture (including yeasts and filamentous fungi), (2) mycological culture with CF-derived fungal specific culture media, and (3) Non-culture and direct DNA extraction from patient sputa. Fungi isolated from environmental air samples of the CF unit were compared to fungi in sputa from CF patients. Fungi (n = 107) were detected in 14/77(18%) of patients by method 1, in 60/77 (78%) of patients by method 2 and with method 3, in 77/77(100%) of the patients. The majority of yeasts isolated were Candida albicans and C. dubliniensis. Exophiala (Wangiella) dermatitidis, Scedosporiumapiospermum, Penicillium spp., Aspergillus fumigatus, and Aspergillus versicolor were also identified by sequence analysis of the rDNA short internal transcribed spacer (ITS2) region. Conventional laboratory analysis failed to detect fungi in 63 patients mainly due to overgrowth by Gram-negative organisms. Mycological culture with antibiotics dramatically increased the number of fungi that could be detected. Molecular techniques detected fungi such as Saccharomyces cerevisiae, Malassezia spp., Fuscoporia ferrea, Fusarium culmorum, Acremonium strictum, Thanatephorus cucumeris and Cladosporium spp. which were not found with other methods. This study demonstrates that several potentially important fungi may not be detected if mycological culture methods alone are used. A polyphasic approach employing both enhanced mycological culture with molecular detection will help determine the presence of fungi in the sputa of patients with CF and their healthcare environment.

Combining multi-scale geophysical techniques for robust hydro-structural characterisation in catchments underlain by hard rock in post-glacial regions

Accurate conceptual models of groundwater systems are essential for correct interpretation of monitoring data in catchment studies. In surface-water dominated hard rock regions, modern ground and surface water monitoring programmes often have very high resolution chemical, meteorological and hydrological observations but lack an equivalent emphasis on the subsurface environment, the properties of which exert a strong control on flow pathways and interactions with surface waters. The reasons for this disparity are the complexity of the system and the difficulty in accurately characterising the subsurface, except locally at outcrops or in boreholes. This is particularly the case in maritime north-western Europe, where a legacy of glacial activity, combined with large areas underlain by heterogeneous igneous and metamorphic bedrock, make the structure and weathering of bedrock difficult to map or model. Traditional approaches which seek to extrapolate information from borehole to field-scale are of limited application in these environments due to the high degree of spatial heterogeneity. Here we apply an integrative and multi-scale approach, optimising and combining standard geophysical techniques to generate a three-dimensional geological conceptual model of the subsurface in a catchment in NE Ireland. Available airborne LiDAR, electromagnetic and magnetic data sets were analysed for the region. At field-scale surface geophysical methods, including electrical resistivity tomography, seismic refraction, ground penetrating radar and magnetic surveys, were used and combined with field mapping of outcrops and borehole testing. The study demonstrates how combined interpretation of multiple methods at a range of scales produces robust three-dimensional conceptual models and a stronger basis for interpreting groundwater and surface water monitoring data.

Combining multi-band and frequency-filtering techniques for speech recognition in noisy environments

While current speech recognisers give acceptable performance in carefully controlled environments, their performance degrades rapidly when they are applied in more realistic situations. Generally, the environmental noise may be classified into two classes: the wide-band noise and narrow band noise. While the multi-band model has been shown to be capable of dealing with speech corrupted by narrow-band noise, it is ineffective for wide-band noise. In this paper, we suggest a combination of the frequency-filtering technique with the probabilistic union model in the multi-band approach. The new system has been tested on the TIDIGITS database, corrupted by white noise, noise collected from a railway station, and narrow-band noise, respectively. The results have shown that this approach is capable of dealing with noise of narrow-band or wide-band characteristics, assuming no knowledge about the noisy environment.

Signal Reliability Improvement Using Selection Combining Based Macro-Diversity for Off-Body Communications At 868 MHz

This paper investigates the potential improvement in signal reliability for outdoor short-range off-body communications channels at 868 MHz using the macro-diversity offered by multiple co-located base stations. In this study, ten identical hypothetical base stations were positioned equidistantly around the perimeter of a rectangle of length 6.67 m and width 3.3 m. A body worn node was placed on the central chest region of an adult male. Five scenarios, each considering different user trajectories, were then analyzed to test the efficacy of using macro-diversity when the desired link is subject to shadowing caused by the human body. A number of selection combining based macro-diversity configurations consisting of four and then ten base stations were considered. It was found that using a macro-diversity system consisting of four base stations (or equivalently signal branches), a maximum diversity gain of 22.5 dB could be obtained while implementing a 10-base station setup this figure could be improved to 25.2 dB.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.