eModeration: Contextualising online learning in undergraduate nurse education

The wide availability of flexible, mixed-mode methods of course delivery to nursing students places increased demands on the skills and adaptability of nurse educators. The rapid uptake of computer-moderated learning, in particular, has required educators to reconsider some of their long-established pedagogical beliefs and practices which, over time, have faithfully sustained face-to-face teaching in classrooms. Inevitably, a certain degree of pedagogical adjustment is required when teaching online to ensure that the qualities of educational processes are consonant with expected learning outcomes. This paper discusses these important aspects, together with strategies that can help optimise educational practice, with a view to improve the delivery of Web-based courses

The Effect of Post-Purchase Perceived-Value Towards the Relationship Quality of Hajj and Umrah Travel Agencies in Indonesia

One out of four people in the world is a Moslem, and Indonesia ranks first on the biggest Moslem population in the world. Hundreds of thousands of people go to Makkah each year to make Hajj (pilgrimage). Hajj and Umrah travel agencies as providers of Hajj and Umrah packages are becoming important in Indonesia, as their number is about two hundred agencies. However, there has been little discussion about Hajj and Umrah, especially on Hajj and Umrah travel agencies. The purpose of this paper is to identify the relationships of post-purchase perceived-value to relationship quality, which consists of satisfaction, commitment, and trust, on Hajj and Umrah travel agencies. This study finds that the post-purchase perceived-value significantly affects satisfaction and trust but does not affect commitment. Moreover, satisfaction significantly affects trust and commitment, while trust does not affect commitment

Generalized constructive tree weights

The Loop Vertex Expansion (LVE) is a quantum field theory (QFT) method which explicitly computes the Borel sum of Feynman perturbation series. This LVE relies in a crucial way on symmetric tree weights which define a measure on the set of spanning trees of any connected graph. In this paper we generalize this method by defining new tree weights. They depend on the choice of a partition of a set of vertices of the graph, and when the partition is non-trivial, they are no longer symmetric under permutation of vertices. Nevertheless we prove they have the required positivity property to lead to a convergent LVE; in fact, we formulate this positivity property precisely for the first time. Our generalized tree weights are inspired by the Brydges-Battle-Federbush work on cluster expansions and could be particularly suited to the computation of connected functions in QFT. Several concrete examples are explicitly given.Comment: 22 pages, 2 figure

Basic studies in microwave remote sensing

Scattering models were developed in support of microwave remote sensing of earth terrains with particular emphasis on model applications to airborne Synthetic Aperture Radar measurements of forest. Practically useful surface scattering models based on a solution of a pair of integral equations including multiple scattering effects were developed. Comparisons of these models with controlled scattering measurements from statistically known random surfaces indicate that they are valid over a wide range of frequencies. Scattering models treating a forest environment as a two and three layered media were also developed. Extensive testing and comparisons were carried out with the two layered model. Further studies with the three layered model are being carried out. A volume scattering model valid for dense media such as a snow layer was also developed that shows the appropriate trend dependence with the volume fraction of scatterers

Flux Qubit Readout in the Persistent Current Basis at arbitrary Bias Points

Common flux qubit readout schemes are qubit dominated, meaning they measure in the energy eigenbasis of the qubit. For various applications meausrements in a basis different from the energy eigenbasis are required. Here we present an indirect measurement protocol, which is detector dominated instead of qubit dominated, yielding a projective measurements in the persistent current basis for arbitrary bias points. We show that with our setup it is possible to perform a quantum nondemolition measurement (QND) in the persistent current basis at all flux bias points with fidelities reaching almost 100%.Comment: 5 pages, 3 figures + 5 pages Supplementa

EFICAz²: enzyme function inference by a combined approach enhanced by machine learning

©2009 Arakaki et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/107doi:10.1186/1471-2105-10-107Background: We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. Results: We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz², exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz² and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz² generates considerably more unique assignments than KEGG. Conclusion: Performance benchmarks and the comparison with KEGG demonstrate that EFICAz² is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz² web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.htm