Organisational Self-Evaluation and Teacher Education for Community Relations in a Transforming Society?

During 2004, the School of Education at the University of Ulster embarked on an innovative three-year project designed to embed community relations objectives within initial teacher education. With the advent of more peaceful times in Northern Ireland, this was a precipitous time for initial teacher educators to review the preparation given to beginner teachers for teaching in an increasingly pluralist society emerging from conflict. The present paper reports on one very specific and time-limited element of the broader project. That is, development work designed to investigate the possibilities of using processes of self-review and evaluation as a lever for improvements in initial teacher education for community relations. Following a brief contextualisation, the background to, and the development of, a set of materials designed to support rigorous and systematic self-review of all aspects of provision in a university-based initial teacher education department is described. The Community Relations Index for Initial Teacher Education (Cr-ITE) was envisaged as being of use to initial teacher education establishments in order to help teacher educators take responsibility for rigorous learning from their practice, whilst placing inclusive values at the centre of organisational development. The final section includes further critical reflection on the role of organisational self-review in transforming teacher education for inclusion in a society emerging from longstanding communal conflict.

Social categorizations, social comparisons and stigma: Presentations of self in people with learning difficulties

Self-categorization theory stresses the importance of the context in which the metacontrast principle is proposed to operate. This study is concerned with how 'the pool of psychologically relevant stimuli' (Turner, Hogg, Oakes, Reicher & Wetherell, 1987, p. 47) comprising the context is determined. Data from interviews with 33 people with learning difficulties were used to show how a positive sense of self might be constructed by members of a stigmatized social category through the social worlds that they describe, and therefore the social comparisons and categorizations that are made possible. Participants made downward comparisons which focused on people with learning difficulties who were less able or who displayed challenging behaviour, and with people who did not have learning difficulties but who, according to the participants, behaved badly, such as beggars, drunks and thieves. By selection of dimensions and comparison others, a positive sense of self and a particular set of social categorizations were presented. It is suggested that when using self-categorization theory to study real-world social categories, more attention needs to be paid to the involvement of the perceiver in determining which stimuli are psychologically relevant since this is a crucial determinant of category salience.

Position control of shape memory alloy actuators using self tuning fuzzy PID controller

Shape Memory Alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

A novel shrinkage technique based on the normal inverse Gaussian density model

This paper proposes a novel image denoising technique based on the normal inverse Gaussian (NIG) density model using an extended non-negative sparse coding (NNSC) algorithm proposed by us. This algorithm can converge to feature basis vectors, which behave in the locality and orientation in spatial and frequency domain. Here, we demonstrate that the NIG density provides a very good fitness to the non-negative sparse data. In the denoising process, by exploiting a NIG-based maximum a posteriori estimator (MAP) of an image corrupted by additive Gaussian noise, the noise can be reduced successfully. This shrinkage technique, also referred to as the NNSC shrinkage technique, is self-adaptive to the statistical properties of image data. This denoising method is evaluated by values of the normalized signal to noise rate (SNR). Experimental results show that the NNSC shrinkage approach is indeed efficient and effective in denoising. Otherwise, we also compare the effectiveness of the NNSC shrinkage method with methods of standard sparse coding shrinkage, wavelet-based shrinkage and the Wiener filter. The simulation results show that our method outperforms the three kinds of denoising approaches mentioned above.

Trajectories of Learning Dispositions and Attitudes in Upper Primary Schools in Northern Ireland: What exactly is going on?

There is increasing research and policy interest in the importance of attitudes to learning, learning orientations and learning dispositions (however they are labelled), not only because they influence traditional measures of school achievement but also because they facilitate how well children function at school, with implications for their future learning. This paper reports the findings on pupils’ learning dispositions and attitudes from two separate cohorts of pupils as they progress through upper primary school (Key Stage 2) in 50 schools in Northern Ireland. (These data are drawn from two different longitudinal studies and the data collection period predates the introduction of the new Northern Ireland Curriculum.) Approximately 1200 pupils completed seven scales from the Assessment of Learner-Centred Practices, ALCPs (McCombs and Lauer, 1997) at three time points, at the end of P5 (9 year olds), at the end of P6 (10 years olds) and at the end of P7 (11 year olds). ALCPs draws on an extensive research base that has identified cognitive and motivational dispositions and attitudes that are associated with a positive orientation to learning, and ultimately with positive progress in school (Alexander and Murphy, 1998). Although each scale can be considered separately, the seven scales cluster into two groups: self-efficacy, mastery orientation, active learning strategies and curiosity are all predicted to be pro-learning; and challenge avoidance, work avoidance, and – to a lesser extent – performance orientation, are predicted to be negatively associated with learning. The general trajectory in the children’s self-evaluations shows that they are becoming less pro-learning over time, with significant decreases in their self-ratings of active learning, curiosity, mastery orientation and self-efficacy. At the same time, there is some evidence that they work harder and put more effort into their work but this is not accompanied by maintaining their previous pro-learning motivations and strategies. The pattern is consistently more negative for boys than for girls. There are very few differences between the two cohorts indicating that the pattern is not confined to a specific cohort. These findings are challenging and will be interrogated with regard to two questions – are the changes related to the influence of the children’s school experiences per se or are they more related to developmental differences as children adopt more critical appraisals of their personal attributes and efforts as they get older? Whatever the reason, these learning dispositions and attitudes are important as they contribute significantly to school achievement even when the more traditional predictors like gender and ability are taken into account.

Advanced design optimization of cold-formed steel portal frame buildings

The design optimization of cold-formed steel portal frame buildings is considered in this paper. The objective function is based on the cost of the members for the main frame and secondary members (i.e., purlins, girts, and cladding for walls and roofs) per unit area on the plan of the building. A real-coded niching genetic algorithm is used to minimize the cost of the frame and secondary members that are designed on the basis of ultimate limit state. It iis shown that the proposed algorithm shows effective and robust capacity in generating the optimal solution, owing to the population's diversity being maintained by applying the niching method. In the optimal design, the cost of purlins and side rails are shown to account for 25% of the total cost; the main frame members account for 27% of the total cost, claddings for the walls and roofs accounted for 27% of the total cost.

Building information modeling based building design optimization for sustainability

Environmental problems, especially climate change, have become a serious global issue waiting for people to solve. In the construction industry, the concept of sustainable building is developing to reduce greenhouse gas emissions. In this study, a building information modeling (BIM) based building design optimization method is proposed to facilitate designers to optimize their designs and improve buildings’ sustainability. A revised particle swarm optimization (PSO) algorithm is applied to search for the trade-off between life cycle costs (LCC) and life cycle carbon emissions (LCCE) of building designs. In order tovalidate the effectiveness and efficiency of this method, a case study of an office building is conducted in Hong Kong. The result of the case study shows that this method can enlarge the searching space for optimal design solutions and shorten the processing time for optimal design results, which is really helpful for designers to deliver an economic and environmental friendly design scheme.

A strategy for designing inhibitors of alpha-synuclein aggregation and toxicity as a novel treatment for Parkinson's disease and related disorders.

Convergent biochemical and genetic evidence suggests that the formation of alpha-synuclein (alpha-syn) protein deposits is an important and, probably, seminal step in the development of Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). It has been reported that transgenic animals overexpressing human alpha-syn develop lesions similar to those found in the brain in PD, together with a progressive loss of dopaminergic cells and associated abnormalities of motor function. Inhibiting and/or reversing alpha-syn self-aggregation could, therefore, provide a novel approach to treating the underlying cause of these diseases. We synthesized a library of overlapping 7-mer peptides spanning the entire alpha-syn sequence, and identified amino acid residues 64-100 of alpha-syn as the binding region responsible for its self-association. Modified short peptides containing alpha-syn amino acid sequences from part of this binding region (residues 69-72), named alpha-syn inhibitors (ASI), were found to interact with full-length alpha-syn and block its assembly into both early oligomers and mature amyloid-like fibrils. We also developed a cell-permeable inhibitor of alpha-syn aggregation (ASID), using the polyarginine peptide delivery system. This ASID peptide was able to inhibit the DNA damage induced by Fe(II) in neuronal cells transfected with alpha-syn(A53T), a familial PD-associated mutation. ASI peptides without this delivery system did not reverse levels of Fe(II)-induced DNA damage. Furthermore, the ASID peptide increased (P

SuperWASP-North extrasolar planet candidates: candidates from fields 17h

We have performed photometric observations of nearly seven million stars with 8 <V <15 with the SuperWASP-North instrument from La Palma between 2004 May to September. Fields in the right ascension range 17-18h, yielding over 185000 stars with sufficient quality data, have been searched for transits using a modified box least-squares (BLS) algorithm. We find a total of 58 initial transiting candidates which have high signal-to-noise ratio in the BLS, show multiple transit-like dips and have passed visual inspection. Analysis of the blending and the inferred planetary radii for these candidates leave, a total of seven transiting planet candidates which pass all the tests plus four which pass the majority. We discuss the derived parameters for these candidates and their properties and comment on the implications for future transit searches.

MSGR-based Low Latency Complex Matrix Inversion Architecture

This paper presents a matrix inversion architecture based on the novel Modified Squared Givens Rotations (MSGR) algorithm, which extends the original SGR method to complex valued data, and also corrects erroneous results in the original SGR method when zeros occur on the diagonal of the matrix either initially or during processing. The MSGR algorithm also avoids complex dividers in the matrix inversion, thus minimising the complexity of potential real-time implementations. A systolic array architecture is implemented and FPGA synthesis results indicate a high-throughput low-latency complex matrix inversion solution. © 2008 IEEE.

Local Conventions in Game Play in an Evolving Dual Social Network Framework

People usually perform economic interactions within the social setting of a small group, while they obtain relevant information from a broader source. We capture this feature with a dynamic interaction model based on two separate social networks. Individuals play a coordination game in an interaction network, while updating their strategies using information from a separate influence network through which information is disseminated. In each time period, the interaction and influence networks co-evolve, and the individuals’ strategies are updated through a modified naive learning process. We show that both network structures and players’ strategies always reach a steady state, in which players form fully connected groups and converge to local conventions. We also analyze the influence exerted by a minority group of strongly opinionated players on these outcomes.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.