Host and Habitat Use by Parasitoids (Hymenoptera: Pteromalidae) of House Fly and Stable Fly (Diptera: Muscidae) Pupae

House fly and stable fly pupae were collected during the summer from a dairy farm in northern Illinois. Spalangia nigroaenea accounted for most of the parasitoids recovered from house flies. Spalangia nigra, S. endius, Muscidifurax spp., and S. nigroaenea accounted for most of the parasitoids from stable flies. The majority of flies were house flies late in the summer and stable flies early in the summer. Higher percentages of house flies tended to be in samples containing lower substrate moisture and higher substrate temperature. Parasitism of stable flies started earlier and peaked weeks before that of house flies, with overall parasitism highest from mid-to late-summer. Parasitism of house flies, but not stable flies, differed significantly among habitats, being greater in calf hutches than in edge samples. Hymenopterous parasitoids from house flies tended to include a greater percentage of S. nigroaenea (and a lower percentage of Muscidifurax spp.) in calf hutches versus drainage or edge habitats and in sub- strates consisting of mostly wood shavings versus mostly manure. Within samples, differential parasitism of fly species was not detected for S. nigroaenea, S. endius, or Muscidifurax spp.; but S. nigra preferentially parasitized stable flies

Transonic separated flow predictions based on a mathematically simple, nonequilibrium turbulence closure model

A mathematically simple, turbulence closure model designed to treat transonic airfoil flows even with massive separation is described. Numerical solutions of the Reynolds-averaged, Navier-Stokes equations obtained with this closure model are shown to agree well with experiments over a broad range of test conditions

A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation.

Multivariate kernel regression is an important tool for investigating the relationship between a response and a set of explanatory variables. It is generally accepted that the performance of a kernel regression estimator largely depends on the choice of bandwidth rather than the kernel function. This nonparametric technique has been employed in a number of empirical studies including the state-price density estimation pioneered by Aït-Sahalia and Lo (1998). However, the widespread usefulness of multivariate kernel regression has been limited by the difficulty in computing a data-driven bandwidth. In this paper, we present a Bayesian approach to bandwidth selection for multivariate kernel regression. A Markov chain Monte Carlo algorithm is presented to sample the bandwidth vector and other parameters in a multivariate kernel regression model. A Monte Carlo study shows that the proposed bandwidth selector is more accurate than the rule-of-thumb bandwidth selector known as the normal reference rule according to Scott (1992) and Bowman and Azzalini (1997). The proposed bandwidth selection algorithm is applied to a multivariate kernel regression model that is often used to estimate the state-price density of Arrow-Debreu securities. When applying the proposed method to the S&P 500 index options and the DAX index options, we find that for short-maturity options, the proposed Bayesian bandwidth selector produces an obviously different state-price density from the one produced by using a subjective bandwidth selector discussed in Aït-Sahalia and Lo (1998).Black-Scholes formula, Likelihood, Markov chain Monte Carlo, Posterior density.

Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions

We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood-based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework). In particular we consider so-called hidden semi-Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi-Markov model to multiple bison movement paths

Separated transonic airfoil flow calculations with a nonequilibrium turbulence model

Navier-Stokes transonic airfoil calculations based on a recently developed nonequilibrium, turbulence closure model are presented for a supercritical airfoil section at transonic cruise conditions and for a conventional airfoil section at shock-induced stall conditions. Comparisons with experimental data are presented which show that this nonequilibrium closure model performs significantly better than the popular Baldwin-Lomax and Cebeci-Smith equilibrium algebraic models when there is boundary-layer separation that results from the inviscid-viscous interactions

Case study of a female ocean racer: prerace preparation and nutritional intake during the Vendee Globe 2008.

The Vendée Globe is a solo round-the-world sailing race without stopovers or assistance, a physically demanding challenge for which appropriate nutrition should maintain energy balance and ensure optimum performance. This is an account of prerace nutritional preparation with a professional and experienced female racer and assessment of daily nutritional intake (NI) during the race using a multimethod approach. A daily energy intake (EI) of 15.1 MJ/day was recommended for the race and negotiated down by the racer to 12.7 MJ/day, with carbohydrate and fluid intake goals of 480 g/day and 3,020 ml/day, respectively. Throughout the 99-day voyage, daily NI was recorded using electronic food diaries and inventories piloted during training races. NI was assessed and a postrace interview and questionnaire were used to evaluate the intervention. Fat mass (FM) and fat-free mass (FFM) were assessed pre- (37 days) and postrace (11 days) using dual-energy X-ray absorptiometry, and body mass was measured before the racer stepped on the yacht and immediately postrace. Mean EI was 9.2 MJ/day (2.4-14.3 MJ/day), representing a negative energy balance of 3.5 MJ/day under the negotiated EI goal, evidenced by a 7.9-kg loss of body mass (FM -7.5 kg, FFM -0.4 kg) during the voyage, with consequent underconsumption of carbohydrate by ~130 g/day. According to the postrace yacht food inventory, self-reported EI was underreported by 7%. This intervention demonstrates the practicality of the NI approach and assessment, but the racer's nutrition strategy can be further improved to facilitate meeting more optimal NI goals for performance and health. It also shows that evaluation of NI is possible in this environment over prolonged periods, which can provide important information for optimizing nutritional strategies for ocean racing

Coal thickness gauge using RRAS techniques, part 1

A noncontacting sensor having a measurement range of 0 to 6 in or more, and with an accuracy of 0.5 in or better is needed to control the machinery used in modern coal mining so that the thickness of the coal layer remaining over the rock is maintained within selected bounds. The feasibility of using the radiofrequency resonance absorption (RRAS) techniques of electron magnetic resonance (EMR) and nuclear magnetic resonance (NMR) as the basis of a coal thickness gauge is discussed. The EMR technique was found, by analysis and experiments, to be well suited for this application