Optimal multicast tree routing for cluster computing in hypercube interconnection networks

Cluster computation has been used in the applications that demand performance, reliability, and availability, such as cluster server groups, large-scale scientific computations, distributed databases, distributed media-on-demand servers and search engines etc. In those applications, multicast can play the vital roles for the information dissemination among groups of servers and users. This paper proposes a set of novel efficient fault-tolerant multicast routing algorithms on hypercube interconnection of cluster computers using multicast shared tree approach. We present some new algorithms for selecting an optimal core (root) and constructing the shared tree so as to minimize the average delay for multicast messages. Simulation results indicate that our algorithms are efficient in the senses of short end-to-end average delay, load balance and less resource utilizations over hypercube cluster interconnection networks.

Clinical measurement of muscle tone using a velocity-corrected modified ashworth scale

Objective: To develop a new form of the modified Ashworth scale (MAS) for muscle-tone assessment that combines the MAS score with the passive muscle-stretching velocity during the assessment of muscle tone, resulting in a measure that has higher intertester reliability than the MAS.

Design: Twanty-two volunteer subjects with spinal cord injuries at a tertiary care outpatient and inpatient spinal cord injury rehabilitation center affiliated with a university were recruited for this study.

Results: A decision tree in which V-MAS scores were obtained was developed. The data obtained from three independent raters, when adjusted by means of the V-MAS, showed an excellent interrater reliability.

Conclusions: Results indicated that the V-MAS is a more reliable measure. In addition, the resulting units of the V-MAS, ranging from 0 to 1, are of the same form as pendulum test data. The V-MAS method is quite simple to use because the rater need only measure the angular range and duration of the passive movement to calculate average velocity during the MAS assessment in addition to the normal MAS rating of muscle tone.

Using decision-tree to automatically construct learned-heuristics for events classification in sports video

Automatic events classification is an essential requirement for constructing an effective sports video summary. It has become a well-known theory that the high-level semantics in sport video can be “computationally interpreted” based on the occurrences of specific audio and visual features which can be extracted automatically. State-of-the-art solutions for features-based event classification have only relied on either manual-knowledge based heuristics or machine learning. To bridge the gaps, we have successfully combined the two approaches by using learning-based heuristics. The heuristics are constructed automatically using decision tree while manual supervision is only required to check the features and highlight contained in each training segment. Thus, fully automated construction of classification system for sports video events has been achieved. A comprehensive experiment on 10 hours video dataset, with five full-match soccer and five full-match basketball videos, has demonstrated the effectiveness/robustness of our algorithms.

Coastal marine habitat mapping using multi-beam acoustics, video data and a decision tree classifier on the temperate south-east Australian continental shelf

In this habitat mapping study, multi-beam acoustic data are integrated with extensive, precisely geo-referenced video validation data in a GIS environment to classify benthic substrates and biota at a 33km2 site in the near shore waters of Victoria, Australia. Using an automated decision-tree classification method, 5 representative biotic groups were identified in the Cape Nelson survey area using a combination of multi-beam bathymetry, backscatter and derivative products. Rigorous error assessment of derived, classified maps produced high overall accuracies (>85%) for all mapping products. In addition, a discrete multivariate analysis technique (kappa analysis) was used to assess classification accuracy. High-resolution (2.5m cell-size) representation of sea floor morphology and textural characteristics provided by multi-beam bathymetry and backscatter datasets, allowed the interpretation of benthic substrates of the Cape Nelson site and the communities of sessile organisms that populate them. Non-parametric multivariate statistical analysis (ANOSIM) revealed a significant difference in biotic composition between depth strata, and between substrate types. Incorporated with other descriptive measures, these results indicate that depth and substrate are important factors in the distributional ecology of the biotic communities at the Cape Nelson study site. BIOENV analysis indicates that derivatives of both multi-beam datasets (bathymetry and backscatter) are correlated with distribution and density of biotic communities. Results from this study provide new tools for research and management of the coastal zone.

There's SEM and 'SEM' : a critique of the use of PLS regression in information systems research

In disciplines other than IS, the use of covariance-based structural equation modelling (SEM) is the mainstream method for SEM analysis, and for confirmatory factor analysis (CFA). Yet a body of IS literature has developed arguing that PLS regression is a superior tool for these analyses, and for establishing reliability and validity. Despite these claims, the views underlying this PLS literature are not universally shared. In this paper the authors review the PLS and mainstream SEM literatures, and describe the key differences between the two classes of tools. The paper also canvasses why PLS regression is rarely used in management, marketing, organizational behaviour, and that branch of psychology concerned with good measurement – psychometrics. The paper offers some practical options to Australasian researchers seeking greater mastery of SEM, and also acts as a roadmap for readers who want to check for themselves what the mainstream SEM literature has to say.

Publication selection bias in minimum-wage research? A meta-regression analysis

Card and Krueger's meta-analysis of the employment effects of minimum wages challenged existing theory. Unfortunately, their meta-analysis confused publication selection with the absence of a genuine empirical effect. We apply recently developed meta-analysis methods to 64 US minimum-wage studies and corroborate that Card and Krueger's findings were nevertheless correct. The minimum-wage effects literature is contaminated by publication selection bias, which we estimate to be slightly larger than the average reported minimum-wage effect. Once this publication selection is corrected, little or no evidence of a negative association between minimum wages and employment remains.

Unions and profits : a meta-regression analysis

The effect of unions on profits continues to be an unresolved theoretical and empirical issue. In this paper, clustered data analysis and hierarchical linear meta-regression models are applied to the population of forty-five econometric studies that report 532 estimates of the direct effect of unions on profits. Unions have a significant negative effect on profits in the United States, and this effect is larger when market-based measures of profits are used. Separate meta-regression analyses are used to identify the effects of market power and long-lived assets on profits, as well as the sources of union-profit effects. The accumulated evidence rejects market power as a source of union-profit effects. While the case is not yet proven, there is some evidence in support of the appropriation of quasi-rent hypothesis. There is a clear need for further American and non-American primary research in this area.

Venn diagrams, Carroll Diagrams, tree sorts - activities

As early as Level 3 in VELS [Victorian Essential Learning Standards], the topics of Venn diagrams, Carroll Diagrams (which are identical to Karnaugh Maps) and Trees Sorts, are introduced as powerful visual organisers of categories. A selection of challenging tasks for these tools at various levels, including Level 9, are provided.

IgE cross-reactivity between the major peanut allergen Ara h 2 and tree nut allergens

Allergy to peanut and tree nuts is characterised by a high frequency of life-threatening anaphylactic reactions and typically lifelong persistence. Although peanut is the most common cause of nut allergy, peanut allergic patients are frequently also sensitive to tree nuts. It is not known if this is due to cross-reactivity between peanut and tree nut allergens. In this study, the major peanut allergen Ara h 2 was cloned from peanut cDNA, expressed in E. coli cells as a His-tag fusion protein and purified using a Ni-NTA column. Immunoblotting, ELISA and basophil activation indicated by CD63 expression all confirmed the IgE reactivity and biological activity of rAra h 2. To determine whether or not this allergen plays a role in IgE cross-reactivity between peanut and tree nuts, inhibition ELISA was performed. Pre-incubation of serum from peanut allergic patients with increasing concentrations of almond or Brazil nut extract inhibited IgE binding to rAra h 2. Purified rAra h 2-specific serum IgE antibodies also bound to proteins present in almond and Brazil nut extracts by immunoblotting. This indicates that the major peanut allergen, Ara h 2, shares common IgE-binding epitopes with almond and Brazil nut allergens, which may contribute to the high incidence of tree nut sensitisation in peanut allergic individuals.

Estimating the total energy demand for supra-maximal exercise using the VO2-power regression from an incremental exercise test

The VO_2-power regression and estimated total energy demand for a 6-minute supramaximal exercise test was predicted from a continuous incremental exercise test. Sub-maximal VO_2- power co-ordinates were established from the last 40 seconds(s) of 150-second exercise stages. The precision of the estimated total energy demand was determined using the 95% confidence interval (95% CI) of the estimated total energy demand. The linearity of the individual VO_2-power regression equations was determined using Pearson's correlation coefficient. The mean 95% CI of the estimated total energy demand was 5.9±2.5 mL O₂ Eq•^-1kg•min^-1, and the mean correlation coefficient was 0.9942±0.0042. The current study contends that the sub-maximal VO_2-power co-ordinates from a continuous incremental exercise test can be used to estimate supra-maximal energy demand without compromising the precision of the accumulated oxygen deficit (AOD) method.