Student-teachers' perspectives on positive and negative social processes in schools

In recent years, the Standards for Qualified Teacher Status in England have placed new emphasis on student-teachers' ability to become integrated into the 'corporate life of the school' and to work with other professionals. Little research, however, has been carried out into how student-teachers perceive the social processes and interactions that are central to such integration during their initial teacher education school placements. This study aims to shed light on these perceptions. The data, gathered from 23 student-teachers through interviews and reflective writing, illustrate the extent to which the participants perceived such social processes as supporting or obstructing their development as teachers. Signals of inclusion, the degree of match or mismatch in students' and school colleagues' role expectations, and the social awareness of both school and student-teacher emerged as crucial factors in this respect. The student-teachers' accounts show their social interactions with school staff to be meaningful in developing their 'teacher self' and to be profoundly emotionally charged. The implications for mentor and student-teacher role preparation are discussed in this article.

Flavor characterization of sugar-added pennywort (Centella asiatica L.) juices treated with ultra-high pressure and thermal processes

The flavor characteristics of pennywort juices with added sugar treated by ultra-high pressure, pasteurization, and sterilization were investigated using solid phase microextraction combined with gas chromatography-mass spectrometry. It was found that sesquiterpene hydrocarbons comprised the major class of volatile components present and the juices had a characteristic aroma due to the presence of volatiles including beta-caryophyllene and humulene and alpha-copaene. In comparison with heated juices, HPP-treated samples could retain more volatile compounds such as linalool and geraniol similar to those present in fresh juice, whereas some volatiles such as alpha-terpinene and ketone class were apparently formed by thermal treatment. All processing operations produced juice that was not significantly different in the concentration of total volatiles. Practical Application: Pennywort juice is considered a nutraceutical drink for health benefits. Therefore, to preserve all aroma and active components in this juice, a nonthermal process such as ultra-high pressure should be a more appropriate technique for retention of its nutritive values than pasteurization and sterilization.

The human thalamus processes syntactic and semantic language violations

Numerous linguistic operations have been assigned to cortical brain areas, but the contributions of subcortical structures to human language processing are still being discussed. Using simultaneous EEG recordings directly from deep brain structures and the scalp, we show that the human thalamus systematically reacts to syntactic and semantic parameters of auditorily presented language in a temporally interleaved manner in coordination with cortical regions. In contrast, two key structures of the basal ganglia, the globus pallidus internus and the subthalamic nucleus, were not found to be engaged in these processes. We therefore propose that syntactic and semantic language analysis is primarily realized within cortico-thalamic networks, whereas a cohesive basal ganglia network is not involved in these essential operations of language analysis.

Intergenerational transmission of social anxiety: the role of social referencing processes in infancy

Responses to an unfamiliar adult were examined in infants of mothers with social phobia (N = 79) and infants of nonanxious comparison mothers (N = 77) at 10 and 14 months in a social referencing paradigm. On each occasion, a female stranger first interacted with the mother and then approached and interacted with the infant. Over time, infants of mothers with social phobia showed increasing avoidance of the stranger, particularly when they were behaviorally inhibited. In boys, maternal social phobia was associated with increasing fearful responses. Infant avoidance was predicted by expressed maternal anxiety and low levels of encouragement to interact with the stranger. The findings are discussed in relation to theories concerning the intergenerational transmission of social anxiety.

Attention processes in children with movement difficulties, reading difficulties or both

Reading difficulties (RD) and movement difficulties (MD) co-occur more often in clinical populations than expected for independent disorders. In this study, we investigated the pattern of association between attentional processes, RD and MD in a population of 9 year old school children. Children were screened to identify index groups with RD, MD or both, plus a control group. These groups were then tested on a battery of cognitive attention assessments (TEA-Ch). Results confirmed that the occurrence of RD and MD was greater than would be predicted for independent disorders. Additionally, children with MD, whether or not combined with RD, had poor performance on all attention measures when compared with typically developing children. Children with RD only, were no poorer on measures of attention than typical children. The results are discussed with respect to approaches proposed to account for the co-occurrence of disorders.

Predicting fault inflow in iterative software development processes: an industrial evaluation

This paper addresses the need for accurate predictions on the fault inflow, i.e. the number of faults found in the consecutive project weeks, in highly iterative processes. In such processes, in contrast to waterfall-like processes, fault repair and development of new features run almost in parallel. Given accurate predictions on fault inflow, managers could dynamically re-allocate resources between these different tasks in a more adequate way. Furthermore, managers could react with process improvements when the expected fault inflow is higher than desired. This study suggests software reliability growth models (SRGMs) for predicting fault inflow. Originally developed for traditional processes, the performance of these models in highly iterative processes is investigated. Additionally, a simple linear model is developed and compared to the SRGMs. The paper provides results from applying these models on fault data from three different industrial projects. One of the key findings of this study is that some SRGMs are applicable for predicting fault inflow in highly iterative processes. Moreover, the results show that the simple linear model represents a valid alternative to the SRGMs, as it provides reasonably accurate predictions and performs better in many cases.

Estimation of Hidden Markov Models parameters using differential evolution

Hidden Markov Models (HMMs) have been successfully applied to different modelling and classification problems from different areas over the recent years. An important step in using HMMs is the initialisation of the parameters of the model as the subsequent learning of HMM’s parameters will be dependent on these values. This initialisation should take into account the knowledge about the addressed problem and also optimisation techniques to estimate the best initial parameters given a cost function, and consequently, to estimate the best log-likelihood. This paper proposes the initialisation of Hidden Markov Models parameters using the optimisation algorithm Differential Evolution with the aim to obtain the best log-likelihood.

Enhancing change detection in low-quality surveillance footage using markov random fields

Urban surveillance footage can be of poor quality, partly due to the low quality of the camera and partly due to harsh lighting and heavily reflective scenes. For some computer surveillance tasks very simple change detection is adequate, but sometimes a more detailed change detection mask is desirable, eg, for accurately tracking identity when faced with multiple interacting individuals and in pose-based behaviour recognition. We present a novel technique for enhancing a low-quality change detection into a better segmentation using an image combing estimator in an MRF based model.

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D – C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D –1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Further we run the algorithms on a mini-Grid and investigate their efficiency depending on the granularity. Corresponding experimental results are presented.

Robustness and applicability of Markov chain Monte Carlo algorithms for eigenvalue problems

In this paper we analyse applicability and robustness of Markov chain Monte Carlo algorithms for eigenvalue problems. We restrict our consideration to real symmetric matrices. Almost Optimal Monte Carlo (MAO) algorithms for solving eigenvalue problems are formulated. Results for the structure of both - systematic and probability error are presented. It is shown that the values of both errors can be controlled independently by different algorithmic parameters. The results present how the systematic error depends on the matrix spectrum. The analysis of the probability error is presented. It shows that the close (in some sense) the matrix under consideration is to the stochastic matrix the smaller is this error. Sufficient conditions for constructing robust and interpolation Monte Carlo algorithms are obtained. For stochastic matrices an interpolation Monte Carlo algorithm is constructed. A number of numerical tests for large symmetric dense matrices are performed in order to study experimentally the dependence of the systematic error from the structure of matrix spectrum. We also study how the probability error depends on the balancing of the matrix. (c) 2007 Elsevier Inc. All rights reserved.

High signal to noise ratio THz spectroscopy with ASOPS and signal processing schemes for mapping and controlling molecular and bulk relaxation processes

Asynchronous Optical Sampling has the potential to improve signal to noise ratio in THz transient sperctrometry. The design of an inexpensive control scheme for synchronising two femtosecond pulse frequency comb generators at an offset frequency of 20 kHz is discussed. The suitability of a range of signal processing schemes adopted from the Systems Identification and Control Theory community for further processing recorded THz transients in the time and frequency domain are outlined. Finally, possibilities for femtosecond pulse shaping using genetic algorithms are mentioned.