Estimates of body sizes at maturation and at sex change, and the spawning seasonality and sex ratio of the endemic Hawaiian grouper (Hyporthodus quernus, F. Epinephelidae)

A case study of the reproductive biology of the endemic Hawaiian grouper or hapu’upu’u (Hyporthodus quernus) is presented as a model for comprehensive future studies of economically important epinephelid groupers. Specimens were collected throughout multiple years (1978–81, 1992–93, and 2005–08) from most reefs and banks of the Northwestern Hawaiian Islands. The absence of small males, presence of atretic oocytes and brown bodies in testes of mature males, and both developed ovarian and testicular tissues in the gonads of five transitional fish provided evidence of protogynous hermaphroditism. No small mature males were collected, indicating that Hawaiian grouper are monandrous (all males are sex-changed females). Complementary microscopic criteria also were used to assign reproductive stage and estimate median body sizes (L50) at female sexual maturity and at adult sex change from female to male. The L50 at maturation and at sex change was 580 ±8 (95% confidence interval [CI]) mm total length (TL) and 895 ±20 mm TL, respectively. The adult sex ratio was strongly female biased (6:1). Spawning seasonality was described by using gonadosomatic indices. Females began ripening in the fall and remained ripe through April. A February–June main spawning period that followed peak ripening was deduced from the proportion of females whose ovaries contained hydrated oocytes, postovulatory follicles, or both. Testes weights were not affected by season; average testes weight was only about 0.2% of body weight—an order of magnitude smaller than that for ovaries that peaked at 1–3% of body weight. The species’ reproductive life history is discussed in relation to its management

South Dakota\u27s Big Sioux and Vermillion River Basins: Economic Value of Irrigation Water

The primary objective of this study was to estimate the value of water used for irrigation in the Big Sioux and Vermillion river basins of eastern South Dakota. These estimates were made by imputing a residual value of water. Data were acquired through personal interviews with irrigators in the study area which was partitioned into two rainfall regions and two soil areas per rainfall region. Crop enterprise budgets were derived from the data and used to calculate net returns to management and water. These figures were compared with net returns to management from dryland farming obtained from secondary sources to arrive at the final water value estimates

Minkowski compactness measure

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Published in: Computational Intelligence (UKCI), 2013, 13th UK Workshop, Guildford UK. Date of Conference: 9-11 Sept. 2013Many compactness measures are available in the literature. In this paper we present a generalised compactness measure Cq(S) which unifies previously existing definitions of compactness. The new measure is based on Minkowski distances and incorporates a parameter q which modifies the behaviour of the compactness measure. Different shapes are considered to be most compact depending on the value of q: for q = 2, the most compact shape in 2D (3D) is a circle (a sphere); for q → ∞, the most compact shape is a square (a cube); and for q = 1, the most compact shape is a square (a octahedron). For a given shape S, measure Cq(S) can be understood as a function of q and as such it is possible to calculate a spectum of Cq(S) for a range of q. This produces a particular compactness signature for the shape S, which provides additional shape information. The experiments section of this paper provides illustrative examples where measure Cq(S) is applied to various shapes and describes how measure and its spectrum can be used for image processing applications

A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data

Over the next few years new spectroscopic surveys (from the optical surveys of the Sloan Digital Sky Survey and the 2 degree Field survey through to space-based ultraviolet satellites such as GALEX) will provide the opportunity and challenge of understanding how galaxies of different spectral type evolve with redshift. Techniques have been developed to classify galaxies based on their continuum and line spectra. Some of the most promising of these have used the Karhunen and Loeve transform (or Principal Component Analysis) to separate galaxies into distinct classes. Their limitation has been that they assume that the spectral coverage and quality of the spectra are constant for all galaxies within a given sample. In this paper we develop a general formalism that accounts for the missing data within the observed spectra (such as the removal of sky lines or the effect of sampling different intrinsic rest wavelength ranges due to the redshift of a galaxy). We demonstrate that by correcting for these gaps we can recover an almost redshift independent classification scheme. From this classification we can derive an optimal interpolation that reconstructs the underlying galaxy spectral energy distributions in the regions of missing data. This provides a simple and effective mechanism for building galaxy spectral energy distributions directly from data that may be noisy, incomplete or drawn from a number of different sources.Comment: 20 pages, 8 figures. Accepted for publication in A

The Bayesian Decision Tree Technique with a Sweeping Strategy

The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models used within such a technique gives experts additional information by making this classification scheme observable. The use of the Markov Chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. However, in practice, the MCMC technique may become stuck in a particular DT which is far away from a region with a maximal posterior. Sampling such DTs causes bias in the posterior estimates, and as a result the evaluation of classification uncertainty may be incorrect. In a particular case, the negative effect of such sampling may be reduced by giving additional prior information on the shape of DTs. In this paper we describe a new approach based on sweeping the DTs without additional priors on the favorite shape of DTs. The performances of Bayesian DT techniques with the standard and sweeping strategies are compared on a synthetic data as well as on real datasets. Quantitatively evaluating the uncertainty in terms of entropy of class posterior probabilities, we found that the sweeping strategy is superior to the standard strategy

Integrating the promotion of physical activity within a smoking cessation programme: Findings from collaborative action research in UK Stop Smoking Services

Background: Within the framework of collaborative action research, the aim was to explore the feasibility of developing and embedding physical activity promotion as a smoking cessation aid within UK 6/7-week National Health Service (NHS) Stop Smoking Services. Methods: In Phase 1 three initial cycles of collaborative action research (observation, reflection, planning, implementation and re-evaluation), in an urban Stop Smoking Service, led to the development of an integrated intervention in which physical activity was promoted as a cessation aid, with the support of a theoretically based self-help guide, and self monitoring using pedometers. In Phase 2 advisors underwent training and offered the intervention, and changes in physical activity promoting behaviour and beliefs were monitored. Also, changes in clients’ stage of readiness to use physical activity as a cessation aid, physical activity beliefs and behaviour and physical activity levels were assessed, among those who attended the clinic at 4-week post-quit. Qualitative data were collected, in the form of clinic observation, informal interviews with advisors and field notes. Results: The integrated intervention emerged through cycles of collaboration as something quite different to previous practice. Based on field notes, there were many positive elements associated with the integrated intervention in Phase 2. Self-reported advisors’ physical activity promoting behaviour increased as a result of training and adapting to the intervention. There was a significant advancement in clients’ stage of readiness to use physical activity as a smoking cessation aid. Conclusions: Collaboration with advisors was key in ensuring that a feasible intervention was developed as an aid to smoking cessation. There is scope to further develop tailored support to increasing physical activity and smoking cessation, mediated through changes in perceptions about the benefits of, and confidence to do physical activity

Optimising decision trees using multi-objective particle swarm optimisation

Copyright © 2009 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Swarm Intelligence for Multi-objective Problems in Data MiningSummary. Although conceptually quite simple, decision trees are still among the most popular classifiers applied to real-world problems. Their popularity is due to a number of factors – core among these is their ease of comprehension, robust performance and fast data processing capabilities. Additionally feature selection is implicit within the decision tree structure. This chapter introduces the basic ideas behind decision trees, focusing on decision trees which only consider a rule relating to a single feature at a node (therefore making recursive axis-parallel slices in feature space to form their classification boundaries). The use of particle swarm optimization (PSO) to train near optimal decision trees is discussed, and PSO is applied both in a single objective formulation (minimizing misclassification cost), and multi-objective formulation (trading off misclassification rates across classes). Empirical results are presented on popular classification data sets from the well-known UCI machine learning repository, and PSO is demonstrated as being fully capable of acting as an optimizer for trees on these problems. Results additionally support the argument that multi-objectification of a problem can improve uni-objective search in classification problems

Variational Bayesian tracking: whole track convergence for large scale ecological video monitoring

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Conference paper: IEEE International Joint Conference on Neural Networks (IJCNN), 4-9 Aug. 2013, Dallas, Texas, USA.Variational Bayesian approximations offer a computationally fast alternative to numerical approximations for Bayesian inference. We examine variational Bayesian methods for filtering and smoothing continuous hidden Markov models, in particular those with sharply-peaked, nonlinear observations densities. We show that, by making variational updates in the correct order, robust convergence to the tracked state may be achieved. We apply the whole track convergence algorithm to tracking wild crickets in video streams and describe how animals may be identified from the characteristics of their tracks. We also show how identifying alphanumeric tags may be read under poor lighting conditions