Method of identifying clusters representing statistical dependencies in multivariate data

Approach is first to cluster and then to compute spatial boundaries for resulting clusters. Next step is to compute, from set of Monte Carlo samples obtained from scrambled data, estimates of probabilities of obtaining at least as many points within boundaries as were actually observed in original data

Toughening of BIS maleimide resins: Synthesis and characterization of maleimide terminated poly(arylene ether) oligomers and polymers

Amine functional poly(arylene ether) sulfones were previously reported. Herein, the chemistry was extended to amorphous poly(arylene ether) ketones because of their higher fracture toughness values, relative to the polysulfones. It was demonstrated that the amino functional oligomers undergo a self-crosslinking reaction at temperatures above about 220 C. This produces an insoluble, but ductile network that has excellent resistance. A ketamine structure hypothesis was proposed and verified using solid state magic angle NMR. In most cases, the water generated upon ketamine formation is too low to produce porosity and solid networks are obtained. The stability of the ketamine networks towards hydrolysis is excellent. The chemistry was further demonstrated to be able to crosslink preformed nonfunctional poly(arylene ether) ketones if a difunctional amine was utilized. This concept has the possibility of greatly improving the creep resistance of thermoplastics. Also, a new technique was developed for converting the amine functional oligomers cleanly into maleimide structures. This method involves reacting maleic anhydride with monomeric aminophenols in the presence of solvent mixtures

Regional engagement and spatial modelling for natural resource management planning

Changing unsustainable natural resource use in agricultural landscapes is a complex social–ecological challenge that cannot be addressed through traditional reductionist science. More holistic and inclusive (or transdisciplinary) processes are needed. This paper describes a transdisciplinary project for natural resource management planning in two regions (Eyre Peninsula and South Australian Murray-Darling Basin) of southern Australia. With regional staff, we reviewed previous planning to gain an understanding of the processes used and to identify possible improvement in plan development and its operation. We then used an envisioning process to develop a value-rich narrative of regional aspirations to assist stakeholder engagement and inform the development of a land use management option assessment tool called the landscape futures analysis tool (LFAT). Finally, we undertook an assessment of the effectiveness of the process through semi-structured stakeholder interviews. The planning process review highlighted the opinion that the regional plans were not well informed by available science, that they lacked flexibility, and were only intermittently used after publication. The envisioning process identified shared values—generally described as a trust, language that is easily understood, wise use of resources, collaboration and inclusiveness. LFAT was designed to bring the best available science together in a form that would have use in planning, during community consultation and in assessing regional management operations. The LFAT provided spatially detailed but simple models of agricultural yields and incomes, plant biodiversity, weed distribution, and carbon sequestration associated with future combinations of climate, commodity and carbon prices, and costs of production. Stakeholders were impressed by the presentation and demonstration results of the software. While there was anecdotal evidence that the project provided learning opportunities and increased understanding of potential land use change associated with management options under global change, the direct evidence of influence in the updated regional plan was limited. This project had elements required for success in transdisciplinary research, but penetration seems limited. Contributing factors appear to be a complexity of climate effects with economic uncertainty, lack of having the project embedded in the plan revision process, limited continuity and capacity of end users and limited after project support and promotion. Strategies are required to minimise the controlling influence that these limitations can have.Wayne S. Meyer, Brett A. Bryan, David M. Summers, Greg Lyle, Sam Wells, Josie McLean, Mark Siebentrit

Neutral Hydrogen Mapping of Virgo Cluster Blue Compact Dwarf Galaxies

A new installment of neutral hydrogen mappings of Blue Compact Dwarf galaxies, as defined by optical morphology, in and near the Virgo cluster is presented. The primary motivation was to search for outlying clouds of HI as potential interactive triggers of the enhanced star formation, and therefore the mapped galaxies were selected for large HI} mass, large optical diameter, and large velocity profile width. Approximately half the sample proved to have one or more small, low column density star-free companion clouds, either detached or appearing as an appendage in our maps, at resolution of order 4 kpc. Comparison is made to a sample of similarly mapped field BCD galaxies drawn from the literature; however, the Virgo cluster sample of mapped BCDs is still too small for conclusive comparisons to be made. We found, on the one hand, little or no evidence for ram pressure stripping nor, on the other, for extremely extended low column density HI envelopes. The HI rotation curves in most cases rise approximately linearly, and slowly, as far out as we can trace the gas.Comment: To appear in AJ, Dec. 200

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

MCG+00-32-16: An Irregular Galaxy Close to the Lowest Redshift Absorber on the 3C 273 Line of Sight

MCG+00-32-16 is the galaxy closest in position-velocity space to the lowest redshift Ly$\alpha$ absorber along the line-of-sight to the quasar 3C 273. Its projected separation is 204 (d/19 Mpc) kpc, where d is the distance from the Milky Way to the galaxy, and the redshift difference is only 94 km/s; HI 1225+01 is slightly closer in projected separation to the absorber, but has a greater redshift difference. We present HI synthesis array mapping and CCD photometry in B and R for MCG+00-32-16. The HI disk is rotating in such a way that the side of the galaxy closer to the sight-line to the quasar has the larger velocity difference from the absorber. The absorber may be a ``failed dwarf'' member of a poor galaxy group of which MCG+00-32-16 and HI 1225+01 are the only members to have formed stars.Comment: 14 pages, 9 figures, accepted by Astrophysical Journa

Ohio Economic Insects and Related Anthropods

PDF pages: 3

Decision making and risk management in adventure sports coaching

Adventure sport coaches practice in environments that are dynamic and high in risk, both perceived and actual. The inherent risks associated with these activities, individuals’ responses and the optimal exploitation of both combine to make the processes of risk management more complex and hazardous than the traditional sports where risk management is focused almost exclusively on minimization. Pivotal to this process is the adventure sports coaches’ ability to make effective judgments regarding levels of risk, potential benefits and possible consequences. The exact nature of this decision making process should form the basis of coaching practice and coach education in this complex and dynamic field. This positional paper examines decision making by the adventure sports coach in these complex, challenging environments and seeks to stimulate debate whilst offering a basis for future research into this topic

Formal vs. informal coach education

The training of coaches is considered central to sustaining and improving the quality of sports coaching and the ongoing process of professionalisation. Sports coaches participate in a range of learning opportunities (informal to formal) that contribute to their development to varying degrees. In this article, we present our collective understanding on the varying types of learning opportunities and their contribution to coach accreditation and development. The authors presented these views (from a sports pedagogy perspective) as part of a workshop entitled "Formal vs. Informal Coach Education" at the 2007 International Council of Coach Education Master Class in Beijing. These reflections seek to stimulate the on-going, and often sterile, debate about formal versus informal coach education/learning in order to progress scholarship in coaching

Integrated information increases with fitness in the evolution of animats

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.Comment: 27 pages, 8 figures, one supplementary figure. Three supplementary video files available on request. Version commensurate with published text in PLoS Comput. Bio