Utilising Creative Methodologies to Elicit the Views of Young Learners with Additional Needs in Literacy

The research described in this article aimed to explore and examine the dominant ‘assessment’ and ‘participation’ stories of upper-primary pupils with long-standing and marked literacy learning needs, their views on how their level of participation in the assessment and remediation of their additional needs might be increased and also how they perceive themselves as literacy learners. This qualitative small-scale study adopted a case study approach and utilised creative methodologies in the context of focus groups to investigate sensitively theviews and experiences of Key Stage 2 pupils with additional needs in literacy. The findings discussed here are based on the outcomes from the four Northern Irish schools that participated in the original cross-border (Northern Ireland/Republic of Ireland) study. Findings are discussed in the context of strategies for promotingholistic and empowering pathways for learners with additional needs in literacy.

Glycosylation of low density lipoprotein in patients with type 1 (insulin-dependent) diabetes:correlations with other parameters of glycaemic control

Glycosylation of low density lipoproteins obtained from 16 patients with Type 1 (insulin-dependent) diabetes and from 16 age-, sex-, and race-matched controls, was determined. The diabetic patients were normolipaemic and were in good or fair glycaemic control. Eleven patients performed home blood glucose monitoring. Glycosylation of low density lipoproteins in the diabetic patients was significantly higher (p less than 0.001) than in the control subjects, and was significantly correlated with haemoglobin A1c, (p less than 0.01), glycosylation of plasma proteins, (p less than 0.001), and mean home blood glucose, (p less than 0.01). This study confirms that, in diabetic patients, increased glycosylation of low density lipoprotein occurs to an extent which correlates closely with other commonly used indices of glycaemic control.

Orthogonal space-time block coded rate-adaptive modulation with outdated feedbasck

Abstract-Channel state information (CSI) at the transmitter can be used to adapt transmission rate or antenna gains in multi-antenna systems. We propose a rate-adaptive M-QAM scheme equipped with orthogonal space-time block coding with simple outdated, finite-rate feedback over independent flat fading channels. We obtain closed-form expressions for the average BER and throughput for our scheme, and analyze the effects of possibly delayed feedback on the performance gains. We derive optimal switching thresholds maximizing the average throughput under average and outage BER constraints with outdated feedback. Our numerical results illustrate the immunity of our optimal thresholds to delayed feedback.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Experimenting with the Coxian Phase-Type Distribution to Uncover Suitable Fits

In the past few decades, Coxian phase-type distributions have become increasingly more popular as a means of representing survival times. In healthcare, they are considered suitable for modelling the length of stay of patients in hospital and more recently for modelling the patient waiting times in Accident and Emergency Departments. The Coxian phase-type distribution has not only been shown to provide a good representation of real survival data, but its interpretation seems reasonably initiative to the medical experts. The drawback, however, is fitting the distribution to the data. There have been many attempts at accurately estimating the Coxian phase-type parameters. This paper wishes to examine the most promising of the approaches reported in the literature to determine the most accurate. Three performance measures are introduced to assess the fitting process of the algorithms along with the likelihood values and AIC to examine the goodness of fit and complexity of the model. Previous research suggests that the fitting process is strongly influenced by the initial parameter estimates and the data itself being quite variable. To overcome this, one experiment in this research paper will use the same initial parameter values for each estimation and perform the fits on the data simulated from a Coxian phase-type distribution with known parameters.

Dynamic pricing model and algorithm for perishable products with fuzzy demand (ABS 2* Journal)

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, a-optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real-world example is presented to highlight the effectiveness of the developed model and algorithm.

Predictions of large events on a spring-block model

We study the predictability of a theoretical model for earthquakes, using a pattern recognition algorithm similar to the CN and M8 algorithms known in seismology. The model, which is a stochastic spring-block model with both global correlation and local interaction, becomes more predictable as the strength of the global correlation or the local interaction is increased.

Reduction of the temperature sensitivity of Halomonas hydrothermalis by iron starvation combined with microaerobic conditions

The limits to biological processes on Earth are determined by physicochemical parameters, such as extremes of temperature and low water availability. Research into microbial extremophiles has enhanced our understanding of the biophysical boundaries which define the biosphere. However, there remains a paucity of information on the degree to which rates of microbial multiplication within extreme environments are determined by the availability of specific chemical elements. Here, we show that iron availability and composition of the gaseous phase (aerobic vs. microaerobic) determine susceptibility of a marine bacterium, Halomonas hydrothermalis, to sub-optimal and elevated temperature and salinity by impacting rates of cell division (but not viability). In particular, iron starvation combined with microaerobic conditions (5 % v/v of O2, 10 % v/v of CO2, reduced pH) reduced sensitivity to temperature across the 13 °C range tested. These data demonstrate that nutrient limitation interacts with physicochemical parameters to determine biological permissiveness for extreme environments. The interplay between resource availability and stress tolerance, therefore, may shape the distribution and ecology of microorganisms within Earth's biosphere.

More nonlocality with less entanglement in a tripartite atom-optomechanical system

Quantum effects in hybrid atomic optomechanics in a system comprising a cloud of atoms and a mobile mirror mediated by a single-mode cavity are studied. Tripartite non-locality is observed in the atom-light-mirror system, as demonstrated by the violation of the Mermin-Klyshko (MK) inequality. It has been shown [C. Genes, et al., PRA 77, 050307 (R) (2008)] that tripartite entanglement is optimized when the cavity is resonant with the anti-Stokes sideband of the driving laser and the atomic frequency matches the Stokes one. However, we show that this is not the case for the nonlocality. The MK function achieves minima when the atoms are resonant with both the Stokes and anti-Stokes sidebands, and unexpectedly, we find violation of the MK inequality only in a parameter region where entanglement is far from being maximum. A negative relation exists between nonlocality and entanglement with consideration of the possibility of bipartite nonlocality in the violation of the MK inequality. We also study the non-classicality of the mirror by post-selected measurements, e.g. Geiger-like detection, on the cavity and/or the atoms. We show that with feasible parameters Geiger-like detection on the atoms can effectively induce mechanical non-classicality.

Genomic lesions associated with a different clinical outcome in diffuse large B-Cell lymphoma treated with R-CHOP-21

Despite recent therapeutic improvements, the clinical course of diffuse large B-cell lymphoma (DLBCL) still differs considerably among patients. We conducted this retrospective multi-centre study to evaluate the impact of genomic aberrations detected using a high-density genome wide-single nucleotide polymorphism-based array on clinical outcome in a population of DLBCL patients treated with R-CHOP-21 (rituximab, cyclophosphamide, doxorubicine, vincristine and prednisone repeated every 21_d). 166 DNA samples were analysed using the GeneChip Human Mapping 250K NspI. Genomic anomalies were analysed regarding their impact on the clinical course of 124 patients treated with R-CHOP-21. Unsupervised clustering was performed to identify genetically related subgroups of patients with different clinical outcomes. Twenty recurrent genetic lesions showed an impact on the clinical course. Loss of genomic material at 8p23.1 showed the strongest statistical significance and was associated with additional aberrations, such as 17p- and 15q-. Unsupervised clustering identified five DLBCL clusters with distinct genetic profiles, clinical characteristics and outcomes. Genetic features and clusters, associated with a different outcome in patients treated with R-CHOP, have been identified by arrayCGH.