Odour sampling. 2. Comparison of physical and aerodynamic characteristics of sampling devices: A review

Sampling devices differing greatly in shape, size and operating condition have been used to collect air samples to determine rates of emission of volatile substances, including odour. However, physical chemistry principles, in particular the partitioning of volatile substances between two phases as explained by Henrys Law and the relationship between wind velocity and emission rate, suggests that different devices cannot be expected to provide equivalent emission rate estimates. Thus several problems are associated with the use of static and dynamic emission chambers, but the more turbulent devices such as wind tunnels do not appear to be subject to these problems. In general, the ability to relate emission rate estimates obtained from wind tunnel measurements to those derived from device-independent techniques supports the use of wind tunnels to determine emission rates that can be used as input data for dispersion models.

Comparison of odour emission rates measured from various sources using two sampling devices

Two commonly used sampling devices (a wind tunnel and the US EPA dynamic emission chamber), were used to collect paired samples of odorous air from a number of agricultural odour sources. The odour samples were assessed using triangular, forced-choice dynamic olfactometry. The odour concentration data was combined with the flushing rate data to calculate odour emission rates for both devices on all sources. Odour concentrations were consistently higher in samples collected with a flux chamber (ratio ranging from 10:7 to 5:1, relative to wind tunnel samples), whereas odour emission rates were consistently larger when derived from wind tunnels (ratio ranging from 60:1 to 240:1, relative to flux chamber values). A complex relationship existed between emission rate estimates derived from each device, apparently influenced by the nature of the emitting surface. These results have great significance for users of odour dispersion models, for which an odour emission rate is a key input parameter.

A multi-hierarchical strategy for on-ramp coordination

Ramp metering (RM) is an access control for motorways, in which a traffic signal is placed at on-ramps to regulate the rate of vehicles entering the motorway and thus to preserve the motorway capacity. In general, RM algorithms fall into two categories by their effective scope: local control and coordinated control. Local control algorithm determines the metering rate based on the traffic condition on adjacent motorway mainline and the on-ramp. Conversely, coordinated RM strategies make use of measurements from the entire motorway network to operate individual ramp signals for optimal performance at the network level. This study proposes a multi-hierarchical strategy for on-ramp coordination. The strategy is structured in two layers. At the higher layer, a centralised, predictive controller plans the coordination control within a long update interval based on the location of high-risk breakdown flow. At the lower layer, reactive controllers determine the metering rates of those ramps involved in the ramp coordination with a short update interval. This strategy is modelled and applied to the northbound model of the Pacific Motorway in a micro-simulation platform (AIMSUN). The simulation results show that the proposed strategy effectively delays the onset of congestion and reduces total congestion with better managed on-ramp queues.

A Hierarchical System of Learning Automata

A learning automaton operating in a random environment updates its action probabilities on the basis of the reactions of the environment, so that asymptotically it chooses the optimal action. When the number of actions is large the automaton becomes slow because there are too many updatings to be made at each instant. A hierarchical system of such automata with assured c-optimality is suggested to overcome that problem.The learning algorithm for the hierarchical system turns out to be a simple modification of the absolutely expedient algorithm known in the literature. The parameters of the algorithm at each level in the hierarchy depend only on the parameters and the action probabilities of the previous level. It follows that to minimize the number of updatings per cycle each automaton in the hierarchy need have only two or three actions.

Spatial subdivision among assemblages of Spanish mackerel, Scomberomorus commerson (Pisces: Scombridae) across northern Australia: implications for fisheries management

Aim: This study investigated the use of stable δ13C and δ18O isotopes in the sagittal otolith carbonate of narrow-barred Spanish mackerel, Scomberomorus commerson, as indicators of population structure across Australia. Location: Samples were collected from 25 locations extending from the lower west coast of Western Australia (30°), across northern Australian waters, and to the east coast of Australia (18°) covering a coastline length of approximately 9500 km, including samples from Indonesia. Methods: The stable δ13C and δ18O isotopes in the sagittal otolith carbonate of S. commerson were analysed using standard mass spectrometric techniques. The isotope ratios across northern Australian subregions were subjected to an agglomerative hierarchical cluster analysis to define subregions. Isotope ratios within each of the subregions were compared to assess population structure across Australia. Results: Cluster analysis separated samples into four subregions: central Western Australia, north Western Australia, northern Australia and the Gulf of Carpentaria and eastern Australia. Isotope signatures for fish from a number of sampling sites from across Australia and Indonesia were significantly different, indicating population separation. No significant differences were found in otolith isotope ratios between sampling times (no temporal variation). Main conclusions: Significant differences in the isotopic signatures of S. commerson demonstrate that there is unlikely to be any substantial movement of fish among these spatially discrete adult assemblages. The lack of temporal variation among otolith isotope ratios indicates that S. commerson populations do not undergo longshore spatial shifts in distribution during their life history. The temporal persistence of spatially explicit stable isotopic signatures indicates that, at these spatial scales, the population units sampled comprise functionally distinct management units or separate ‘stocks’ for many of the purposes of fisheries management. The spatial subdivision evident among populations of S. commerson across northern and western Australia indicates that it may be advantageous to consider S. commerson population dynamics and fisheries management from a metapopulation perspective (at least at the regional level).

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal

Hierarchical Bayesian models can assimilate surveillance and ecological information to estimate both invasion extent and model parameters for invading plant pests spread by people. A reliability analysis framework that can accommodate multiple dispersal modes is developed to estimate human-mediated dispersal parameters for an invasive species. Uncertainty in the observation process is modelled by accounting for local natural spread and population growth within spatial units. Broad scale incursion dynamics are based on a mechanistic gravity model with a Weibull distribution modification to incorporate a local pest build-up phase. The model uses Markov chain Monte Carlo simulations to infer the probability of colonisation times for discrete spatial units and to estimate connectivity parameters between these units. The hierarchical Bayesian model with observational and ecological components is applied to a surveillance dataset for a spiralling whitefly (Aleurodicus dispersus) invasion in Queensland, Australia. The model structure provides a useful application that draws on surveillance data and ecological knowledge that can be used to manage the risk of pest movement.

Telomere dynamics in the Sydney rock oyster (Saccostrea glomerata): an investigation into the effects of age, tissue type, location and time of sampling.

Telomere length has been purported as a biomarker for age and could offer a non-lethal method for determining the age of wild-caught individuals. Molluscs, including oysters and abalone, are the basis of important fisheries globally and have been problematic to accurately age. To determine whether telomere length could provide an alternative means of ageing molluscs, we evaluated the relationship between telomere length and age using the commercially important Sydney rock oyster (Saccostrea glomerata). Telomere lengths were estimated from tissues of known age individuals from different age classes, locations and at different sampling times. Telomere length tended to decrease with age only in young oysters less than 18 months old, but no decrease was observed in older oysters aged 2-4 years. Regional and temporal differences in telomere attrition rates were also observed. The relationship between telomere length and age was weak, however, with individuals of identical age varying significantly in their telomere length making it an imprecise age biomarker in oysters.

Soil carbon stock in the tropical rangelands of Australia: Effects of soil type and grazing pressure, and determination of sampling requirement.

On-going, high-profile public debate about climate change has focussed attention on how to monitor the soil organic carbon stock (C(s)) of rangelands (savannas). Unfortunately, optimal sampling of the rangelands for baseline C(s) - the critical first step towards efficient monitoring - has received relatively little attention to date. Moreover, in the rangelands of tropical Australia relatively little is known about how C(s) is influenced by the practice of cattle grazing. To address these issues we used linear mixed models to: (i) unravel how grazing pressure (over a 12-year period) and soil type have affected C(s) and the stable carbon isotope ratio of soil organic carbon (delta(13)C) (a measure of the relative contributions of C(3) and C(4) vegetation to C(s)); (ii) examine the spatial covariation of C(s) and delta(13)C; and, (iii) explore the amount of soil sampling required to adequately determine baseline C(s). Modelling was done in the context of the material coordinate system for the soil profile, therefore the depths reported, while conventional, are only nominal. Linear mixed models revealed that soil type and grazing pressure interacted to influence C(s) to a depth of 0.3 m in the profile. At a depth of 0.5 m there was no effect of grazing on C(s), but the soil type effect on C(s) was significant. Soil type influenced delta(13)C to a soil depth of 0.5 m but there was no effect of grazing at any depth examined. The linear mixed model also revealed the strong negative correlation of C(s) with delta(13)C, particularly to a depth of 0.1 m in the soil profile. This suggested that increased C(s) at the study site was associated with increased input of C from C(3) trees and shrubs relative to the C(4) perennial grasses; as the latter form the bulk of the cattle diet, we contend that C sequestration may be negatively correlated with forage production. Our baseline C(s) sampling recommendation for cattle-grazing properties of the tropical rangelands of Australia is to: (i) divide the property into units of apparently uniform soil type and grazing management; (ii) use stratified simple random sampling to spread at least 25 soil sampling locations about each unit, with at least two samples collected per stratum. This will be adequate to accurately estimate baseline mean C(s) to within 20% of the true mean, to a nominal depth of 0.3 m in the profile.

Framing as status or benefits? Consumers’ reactions to hierarchical loyalty program communication

Purpose A fundamental aspect of hierarchical loyalty programs is that some consumers get rewards that others do not. Despite the widespread use of such programs, academics have long debated whether these benefits are outweighed by the potential negative impact of the differential treatment of customers. This study extends our understanding, examining the impact of message framing on consumers’ reactions to hierarchical loyalty structures. Design/methodology/approach Three online studies were conducted. Study 1 uses advertisements to manipulate the message frame’s emphasis (benefits vs. status). Study 2 manipulates consumers’ frame of thought by directing their attention to either changes in benefits or status. Finally, Study 3 uses the proposed framework to reconcile contradictory findings from past research. Findings Low-frequency customers who do not expect to qualify for a superior customer tier tend to reject hierarchical programs when thinking about status. In contrast, when these customers think about concrete rewards, loyalty program messages produce no negative reactions. High-frequency customers are positively affected by communication regardless of the type of benefits framed. Research limitations/implications All studies were done online potentially limiting the external validity of the results. Nevertheless, the impact of message framing on perceptions about the loyalty program seems to be quite robust across different studies and manipulations. Practical implications When communicating with low-frequency customers managers should avoid promising status; customers should instead be motivated based on concrete rewards. High-frequency customers are indifferent to alternative emphasis of communication frames. Originality/value Marketing academics have acknowledged the importance of being able to reward top customers without demotivating light and moderate users. Our research is the first to provide a solution to this issue.

Sequence generator for periodic sampling of transient phenomena

A simple instrument that can provide a sequence of timed pulses for first initiating a transient process and then enabling sampling and recording periodically during the course of the transient event is described. The time delay between the first of these sampling pulses and the start of the transient event is adjustable. This sequence generator has additional features that make it ideal for use in acquiring the waveforms when a storage oscilloscope is used as the recording device. For avoiding the clutter caused by many waveforms being recorded at the same place on an oscilloscope screen such features as displacements of waveforms in the X and Y directions and trace blanking at places where the waveform is not required, have been incorporated. This sequence generator has been employed to acquire a sequence of Raman scattered radiation signals from an adiabatically expanding saturated vapour probed by a flashlamp-pumped dye laser.

Estimation of local forest attributes, utilizing two-phase sampling and auxiliary data

This thesis examines the feasibility of a forest inventory method based on two-phase sampling in estimating forest attributes at the stand or substand levels for forest management purposes. The method is based on multi-source forest inventory combining auxiliary data consisting of remote sensing imagery or other geographic information and field measurements. Auxiliary data are utilized as first-phase data for covering all inventory units. Various methods were examined for improving the accuracy of the forest estimates. Pre-processing of auxiliary data in the form of correcting the spectral properties of aerial imagery was examined (I), as was the selection of aerial image features for estimating forest attributes (II). Various spatial units were compared for extracting image features in a remote sensing aided forest inventory utilizing very high resolution imagery (III). A number of data sources were combined and different weighting procedures were tested in estimating forest attributes (IV, V). Correction of the spectral properties of aerial images proved to be a straightforward and advantageous method for improving the correlation between the image features and the measured forest attributes. Testing different image features that can be extracted from aerial photographs (and other very high resolution images) showed that the images contain a wealth of relevant information that can be extracted only by utilizing the spatial organization of the image pixel values. Furthermore, careful selection of image features for the inventory task generally gives better results than inputting all extractable features to the estimation procedure. When the spatial units for extracting very high resolution image features were examined, an approach based on image segmentation generally showed advantages compared with a traditional sample plot-based approach. Combining several data sources resulted in more accurate estimates than any of the individual data sources alone. The best combined estimate can be derived by weighting the estimates produced by the individual data sources by the inverse values of their mean square errors. Despite the fact that the plot-level estimation accuracy in two-phase sampling inventory can be improved in many ways, the accuracy of forest estimates based mainly on single-view satellite and aerial imagery is a relatively poor basis for making stand-level management decisions.

A flexible hierarchical approach for facial age estimation based on multiple features

Age estimation from facial images is increasingly receiving attention to solve age-based access control, age-adaptive targeted marketing, amongst other applications. Since even humans can be induced in error due to the complex biological processes involved, finding a robust method remains a research challenge today. In this paper, we propose a new framework for the integration of Active Appearance Models (AAM), Local Binary Patterns (LBP), Gabor wavelets (GW) and Local Phase Quantization (LPQ) in order to obtain a highly discriminative feature representation which is able to model shape, appearance, wrinkles and skin spots. In addition, this paper proposes a novel flexible hierarchical age estimation approach consisting of a multi-class Support Vector Machine (SVM) to classify a subject into an age group followed by a Support Vector Regression (SVR) to estimate a specific age. The errors that may happen in the classification step, caused by the hard boundaries between age classes, are compensated in the specific age estimation by a flexible overlapping of the age ranges. The performance of the proposed approach was evaluated on FG-NET Aging and MORPH Album 2 datasets and a mean absolute error (MAE) of 4.50 and 5.86 years was achieved respectively. The robustness of the proposed approach was also evaluated on a merge of both datasets and a MAE of 5.20 years was achieved. Furthermore, we have also compared the age estimation made by humans with the proposed approach and it has shown that the machine outperforms humans. The proposed approach is competitive with current state-of-the-art and it provides an additional robustness to blur, lighting and expression variance brought about by the local phase features.