In search of climate effects on Atlantic Croaker (Micropogonias undulatus) stock off the U.S. Atlantic coast with Bayesian state-space biomass dynamic models

Atlantic Croaker (Micropogonias undulatus) production dynamics along the U.S. Atlantic coast are regulated by fishing and winter water temperature. Stakeholders for this resource have recommended investigating the effects of climate covariates in assessment models. This study used state-space biomass dynamic models without (model 1) and with (model 2) the minimum winter estuarine temperature (MWET) to examine MWET effects on Atlantic Croaker population dynamics during 1972–2008. In model 2, MWET was introduced into the intrinsic rate of population increase (r). For both models, a prior probability distribution (prior) was constructed for r or a scaling parameter (r0); imputs were the fishery removals, and fall biomass indices developed by using data from the Multispecies Bottom Trawl Survey of the Northeast Fisheries Science Center, National Marine Fisheries Service, and the Coastal Trawl Survey of the Southeast Area Monitoring and Assessment Program. Model sensitivity runs incorporated a uniform (0.01,1.5) prior for r or r0 and bycatch data from the shrimp-trawl fishery. All model variants produced similar results and therefore supported the conclusion of low risk of overfishing for the Atlantic Croaker stock in the 2000s. However, the data statistically supported only model 1 and its configuration that included the shrimp-trawl fishery bycatch. The process errors of these models showed slightly positive and significant correlations with MWET, indicating that warmer winters would enhance Atlantic Croaker biomass production. Inconclusive, somewhat conflicting results indicate that biomass dynamic models should not integrate MWET, pending, perhaps, accumulation of longer time series of the variables controlling the production dynamics of Atlantic Croaker, preferably including winter-induced estimates of Atlantic Croaker kills.

Deriving acceptable biological catch from the overfishing limit: implications for assessment models

The recently revised Magnuson–Stevens Fishery Conservation and Management Act requires that U.S. fishery management councils avoid overfishing by setting annual catch limits (ACLs) not exceeding recommendations of the councils’ scientific advisers. To meet that requirement, the scientific advisers will need to know the overfishing limit (OFL) estimated in each stock assessment, with OFL being the catch available from applying the limit fishing mortality rate to current or projected stock biomass. The advisers then will derive ‘‘acceptable biological catch’’ (ABC) from OFL by reducing OFL to allow for scientific uncertainty, and ABC becomes their recommendation to the council. We suggest methodology based on simple probability theory by which scientific advisers can compute ABC from OFL and the statistical distribution of OFL as estimated by a stock assessment. Our method includes approximations to the distribution of OFL if it is not known from the assessment; however, we find it preferable to have the assessment model estimate the distribution of OFL directly. Probability-based methods such as this one provide well-defined approaches to setting ABC and may be helpful to scientific advisers as they translate the new legal requirement into concrete advice.

Estimating long-term growth-rate changes of southern bluefin tuna (Thunnus maccoyii) from two periods of tag-return data

Southern bluefin tuna (SBT) (Thunnus maccoyii) growth rates are estimated from tag-return data associated with two time periods, the 1960s and 1980s. The traditional von Bertalanffy growth model (VBG) and a two-phase VBG model were fitted to the data by maximum likelihood. The traditional VBG model did not provide an adequate representation of growth in SBT, and the two-phase VBG yielded a significantly better fit. The results indicated that significant change occurs in the pattern of growth in relation to a VBG curve during the juvenile stages of the SBT life cycle, which may be related to the transition from a tightly schooling fish that spends substantial time in near and surface shore waters to one that is found primarily in more offshore and deeper waters. The results suggest that more complex growth models should be considered for other tunas and for other species that show a marked change in habitat use with age. The likelihood surface for the two-phase VBG model was found to be bimodal and some implications of this are investigated. Significant and substantial differences were found in the growth for fish spawned in the 1960s and in the 1980s, such that after age four there is a difference of about one year in the expected age of a fish of similar length which persists over the size range for which meaningful recapture data are available. This difference may be a density-dependent response as a consequence of the marked reduction in the SBT population. Given the key role that estimates of growth have in most stock assessments, the results indicate that there is a need both for the regular monitoring of growth rates and for provisions for changes in growth over time (possibly related to changes in abundance) in the stock assessment models used for SBT and other species.

Cumulative inbreeding rate in hatchery-reared indian major carps of Karnataka and Maharashtra states

The state fisheries department hatcheries are the major suppliers of seed to the farmers in Karnataka and Maharashtra. The brood stocks of these hatcheries are genetically closed units. In the present study, effective population size and cumulative inbreeding rates were estimated. The cumulative inbreeding rates ranged from 2.69 to 13.75, 8.63 to 15.21 and 3.02 to 5.88 per cent for catla, mrigal and rohu, respectively, in Karnataka state hatcheries. In Maharashtra, the cumulative inbreeding rates for catla ranged from 7.81 to 39.34 per cent and it was 5.84 to 14.09 and 2.46 to 10.20 per cent for mrigal and rohu, respectively. To estimate the inbreeding rates in future generations, predictive models were developed using linear regression, and polynomial and power equations separately for each hatchery. Their multiple correlation and standard errors suggested that simple linear regression can predict the future inbreeding rate efficiently.

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process.

Statistics and modelling of turbulent kinetic energy transport in different regimes of premixed combustion

The statistical behaviour of turbulent kinetic energy transport in turbulent premixed flames is analysed using data from three-dimensional Direct Numerical Simulation (DNS) of freely propagating turbulent premixed flames under decaying turbulence. For flames within the corrugated flamelets regime, it is observed that turbulent kinetic energy is generated within the flame brush. By contrast, for flames within the thin reaction zones regime it has been found that the turbulent kinetic energy decays monotonically through the flame brush. Similar trends are observed also for the dissipation rate of turbulent kinetic energy. Within the corrugated flamelets regime, it is demonstrated that the effects of the mean pressure gradient and pressure dilatation within the flame are sufficient to overcome the effects of viscous dissipation and are responsible for the observed augmentation of turbulent kinetic energy in the flame brush. In the thin reaction zones regime, the effects of the mean pressure gradient and pressure dilatation terms are relatively much weaker than those of viscous dissipation, resulting in a monotonic decay of turbulent kinetic energy across the flame brush. The modelling of the various unclosed terms of the turbulent kinetic energy transport equation has been analysed in detail. The predictions of existing models are compared with corresponding quantities extracted from DNS data. Based on this a-priori DNS assessment, either appropriate models are identified or new models are proposed where necessary. It is shown that the turbulent flux of turbulent kinetic energy exhibits counter-gradient (gradient) transport wherever the turbulent scalar flux is counter-gradient (gradient) in nature. A new model has been proposed for the turbulent flux of turbulent kinetic energy, and is found to capture the qualitative and quantitative behaviour obtained from DNS data for both the corrugated flamelets and thin reaction zones regimes without the need to adjust any of the model constants. © 2010 Springer Science+Business Media B.V.

Effect of particle concentration on erosion rate of mild steel bends in a pneumatic conveyor

Particle concentration is known as a main factor that affects erosion rate of pipe bends in pneumatic conveyors. With consideration of different bend radii, the effect of particle concentration on weight loss of mild steel bends has been investigated in an industrial scale test rig. Experimental results show that there was a significant reduction of the specific erosion rate for high particle concentrations. This reduction was considered to be as a result of the shielding effect during the particle impacts. An empirical model is given. Also a theoretical study of scaling on the shielding effect, and comparisons with some existing models, are presented. It is found that the reduction in specific erosion rate (relative to particle concentration) has a stronger relationship in conveying pipelines than has been found in the erosion tester. © 2004 Elsevier B.V. All rights reserved.

Use of automatic sand pourers for loose sand models

The consistency of laboratory sand model preparation for physical testing is a fundamental criterion in representing identical geotechnical issues at prototype scale. This objective led to the development of robotic apparatus to eliminate the non-uniformity in manual pouring. Previous studies have shown consistent sand models with high relative density between 50 to 90% produced by the automatic moving-hopper sand pourer at the University of Cambridge, based primarily on a linear correlation to flow rate. However, in the case of loose samples, the influence of other parameters, particularly the drop height, becomes more apparent. In this paper, findings on the effect of flow rate and drop height are discussed in relation to the layer thickness and relative density of loose sand samples. Design charts are presented to illustrate their relationships. The effect of these factors on different sand types is also covered to extend the use of the equipment. © 2010 Taylor & Francis Group, London.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.

Reynolds number effects on scalar dissipation rate transport and its modeling in turbulent premixed combustion

The effects of turbulent Reynolds number, Ret, on the transport of scalar dissipation rate of reaction progress variable in the context of Reynolds averaged Navier-Stokes simulations have been analyzed using three-dimensional simplified chemistry-based direct numerical simulation (DNS) data of freely propagating turbulent premixed flames with different values of Ret. Scaling arguments have been used to explain the effects of Ret on the turbulent transport, scalar-turbulence interaction, and the combined reaction and molecular dissipation terms. Suitable modifications to the models for these terms have been proposed to account for Ret effects, and the model parameters include explicit Ret dependence. These expressions approach expected asymptotic limits for large values of Ret. However, turbulent Reynolds number Ret does not seem to have any major effects on the modeling of the term arising from density variation. Copyright © Taylor and Francis Group, LLC.

A Balance Equation Determines a Switch in Neuronal Excitability

We use the qualitative insight of a planar neuronal phase portrait to detect an excitability switch in arbitrary conductance-based models from a simple mathematical condition. The condition expresses a balance between ion channels that provide a negative feedback at resting potential (restorative channels) and those that provide a positive feedback at resting potential (regenerative channels). Geometrically, the condition imposes a transcritical bifurcation that rules the switch of excitability through the variation of a single physiological parameter. Our analysis of six different published conductance based models always finds the transcritical bifurcation and the associated switch in excitability, which suggests that the mathematical predictions have a physiological relevance and that a same regulatory mechanism is potentially involved in the excitability and signaling of many neurons. © 2013 Franci et al.

Paraphrastic language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, this paper presents a novel form of language model, the paraphrastic LM. A phrase level transduction model that is statistically learned from standard text data is used to generate paraphrase variants. LM probabilities are then estimated by maximizing their marginal probability. Significant error rate reductions of 0.5%-0.6% absolute were obtained on a state-ofthe-art conversational telephone speech recognition task using a paraphrastic multi-level LM modelling both word and phrase sequences.

Application of scalar dissipation rate model to siemens DLE combustors

The standard design process for the Siemens Industrial Turbomachinery, Lincoln, Dry Low Emissions combustion systems has adopted the Eddy Dissipation Model with Finite Rate Chemistry for reacting computational fluid dynamics simulations. The major drawbacks of this model have been the over-prediction of temperature and lack of species data limiting the applicability of the model. A novel combustion model referred to as the Scalar Dissipation Rate Model has been developed recently based on a flamelet type assumption. Previous attempts to adopt the flamelet philosophy with alternative closure models have failed, with the prediction of unphysical phenomenon. The Scalar Dissipation Rate Model (SDRM) was developed from a physical understanding of scalar dissipation rate, signifying the rate of mixing of hot and cold fluids at scales relevant to sustain combustion, in flames and was validated using direct numerical simulations data and experimental measurements. This paper reports on the first industrial application of the SDRM to SITL DLE combustion system. Previous applications have considered ideally premixed laboratory scale flames. The industrial application differs significantly in the complexity of the geometry, unmixedness and operating pressures. The model was implemented into ANSYS-CFX using their inbuilt command language. Simulations were run transiently using Scale Adaptive Simulation turbulence model, which switches between Large Eddy Simulation and Unsteady Reynolds Averaged Navier Stokes using a blending function. The model was validated in a research SITL DLE combustion system prior to being applied to the actual industrial geometry at real operating conditions. This system consists of the SGT-100 burner with a glass square-sectioned combustor allowing for detailed diagnostics. This paper shows the successful validation of the SDRM against time averaged temperature and velocity within measurement errors. The successful validation allowed application of the SDRM to the SGT-100 twin shaft at the relevant full load conditions. Limited validation data was available due to the complexity of measurement in the real geometry. Comparison of surface temperatures and combustor exit temperature profiles showed an improvement compared to EDM/FRC model. Furthermore, no unphysical phenomena were predicted. This paper presents the successful application of the SDRM to the industrial combustion system. The model shows a marked improvement in the prediction of temperature over the EDM/FRC model previously used. This is of significant importance in the future applications of combustion CFD for understanding of hardware mechanical integrity, combustion emissions and dynamics of the flame. Copyright © 2012 by ASME.

Paraphrastic language models and combination with neural network language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, paraphrastic LMs were proposed in previous research and successfully applied to a US English conversational telephone speech transcription task. In order to exploit the complementary characteristics of paraphrastic LMs and neural network LMs (NNLM), the combination between the two is investigated in this paper. To investigate paraphrastic LMs' generalization ability to other languages, experiments are conducted on a Mandarin Chinese broadcast speech transcription task. Using a paraphrastic multi-level LM modelling both word and phrase sequences, significant error rate reductions of 0.9% absolute (9% relative) and 0.5% absolute (5% relative) were obtained over the baseline n-gram and NNLM systems respectively, after a combination with word and phrase level NNLMs. © 2013 IEEE.

Differential equation based length scales to improve DES and RANS simulations

Surprisingly expensive to compute wall distances are still used in a range of key turbulence and peripheral physics models. Potentially economical, accuracy improving differential equation based distance algorithms are considered. These involve elliptic Poisson and hyperbolic natured Eikonal equation approaches. Numerical issues relating to non-orthogonal curvilinear grid solution of the latter are addressed. Eikonal extension to a Hamilton-Jacobi (HJ) equation is discussed. Use of this extension to improve turbulence model accuracy and, along with the Eikonal, enhance Detached Eddy Simulation (DES) techniques is considered. Application of the distance approaches is studied for various geometries. These include a plane channel flow with a wire at the centre, a wing-flap system, a jet with co-flow and a supersonic double-delta configuration. Although less accurate than the Eikonal, Poisson method based flow solutions are extremely close to those using a search procedure. For a moving grid case the Poisson method is found especially efficient. Results show the Eikonal equation can be solved on highly stretched, non-orthogonal, curvilinear grids. A key accuracy aspect is that metrics must be upwinded in the propagating front direction. The HJ equation is found to have qualitative turbulence model improving properties. © 2003 by P. G. Tucker.