Assistive classification for improving the efficiency of avian species richness surveys

Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution and used them to classify one-minute recordings into five classes. By filtering out the non-avian minutes, we can reduce the amount of data by about 50% and improve the efficiency of determining avian species richness. The experimental results show that, given 60 one-minute samples, our approach enables to direct ecologists to find about 10% more avian species.

Computer-assisted sampling of acoustic data for more efficient determination of bird species richness

Bird species richness survey is one of the most intriguing ecological topics for evaluating environmental health. Here, bird species richness denotes the number of unique bird species in a particular area. Factors affecting the investigation of bird species richness include weather, observation bias, and most importantly, the prohibitive costs of conducting surveys at large spatiotemporal scales. Thanks to advances in recording techniques, these problems have been alleviated by deploying sensors for acoustic data collection. Although automated detection techniques have been introduced to identify various bird species, the innate complexity of bird vocalizations, the background noise present in the recording and the escalating volumes of acoustic data pose a challenging task on determination of bird species richness. In this paper we proposed a two-step computer-assisted sampling approach for determining bird species richness in one-day acoustic data. First, a classification model is built based on acoustic indices for filtering out minutes that contain few bird species. Then the classified bird minutes are ordered by an acoustic index and the redundant temporal minutes are removed from the ranked minute sequence. The experimental results show that our method is more efficient in directing experts for determination of bird species compared with the previous methods.

Growth hormone resistance and somatomedins in children with end-stage liver disease awaiting transplantation

Background: The success of orthotopic liver transplantation as treatment for end-stage liver disease has prompted investigation of strategies to maintain or improve nutrition and growth in children awaiting transplantation, because malnutrition is an adverse prognostic factor. The purpose of this study was to evaluate the effect of recombinant human growth hormone therapy on body composition and indices of liver function in patients awaiting transplant. Methods: The study was designed as a placebo- controlled, double-blind, crossover trial. Patients received 0.2 U/kg growth hormone, subcutaneously, or placebo daily for 28 days during two treatment periods, separated by a 2-week washout period. Ten patients (mean age, 3.06 ± 1.15 years; range, 0.51-11.65 years, five men), with extrahepatic biliary atresia (n = 8) or two with Alagille's syndrome (n = 2), with end-stage liver disease, completed the trial while awaiting orthotopic liver transplantation. Height, weight, total body potassium, total body fat, resting energy expenditure, respiratory quotient, hematologic and multiple biochemical profile, number of albumin infusions, insulin-like growth factor-1 and 1, growth hormone binding protein (GHBP), and insulin-like growth factor binding protein-1 (IGFBP-1) and insulin-like growth factor binding protein (IGFBP-3) were measured at the beginning and end of each treatment period. Results: Growth hormone treatment was associated with a significant decline in serum bilirubin (-34.6 ± 16.5 μmol/l vs. 18.2 ± 11.59 μmol/l; p < 0.02) but there was no significant effect on any anthropometric or body composition measurements, or on any biochemical or hematologic parameters. Conclusions: These children with end-stage liver disease displayed growth hormone resistance, particularly in relation to the somatomedin axis. Exogenous growth hormone administration may be of limited value in these patients

Malnutrition, gastroenteritis and trypsinogen concentration in hospitalised aboriginal children

Objective: To explore relationships between malnutrition and pancreatic damage in hospitalised aboriginal children. Methods: Immunoreactive trypsinogen (IRT) concentrations were measured in two populations of hospitalised aboriginal children in Australia; 472 children aged 0-3 years, in Alice Springs (Northern Territory); and 187 children aged 0-16 years in Mount Isa (Queensland). Correlation of whole blood IRT with height and weight z-scores, four-site skinfold thickness and upper arm circumference was sought. Results: In Mount Isa, the geometric mean IRT concentration rose with decreasing weight z-score. The IRT concentration was otherwise unrelated to nutritional indices. Sixty percent of the 39 Mount Isa patients with gastroenteritis and 24.5% of the 358 Alice Springs patients with gastroenteritis had an IRT concentration in the upper quartile for their population, compared with 16% for patients with other diagnoses in both populations. Conclusions: A high IRT concentration in patients with low weight z-scores is a confounding effect of gastroenteritis, and may result from subclinical pancreatic disease in gastroenteritis.

Predictors of clinical improvement in children with recurrent abdominal pain

Forty-three children with recurrent abdominal pain who had received treatment from a paediatric gastroenterology clinic were reassessed 6 and 12 months after initial presentation. Measures of children's pain included a pain diary (PD) which measured pain intensity, a parent observation record (POR) which assessed pain behaviour and a structured interview to assess the degree to which pain interferes with the child's activities. Pretreatment measures of the child's history of pain, coping strategies in dealing with pain, and their mother's caregiving strategies were examined as predictors of two indices of clinical improvement: the extent of change in pain on the child's pain diary from pre-test to 6 months follow-up, and the degree of interference to the child's activities. All children had shown significant improvement in the level of pain at follow up, with 74.4% being pain free at 12 month follow-up on the PD and 83.7% being pain free on the POR. The amount of change they showed varied, with some showing residual impairment even though they were significantly improved. Regression analyses showed that children with greatest reductions on the child's pain diary at the 6 month follow-up were those with a stress-related mode of onset, whose mothers used more adaptive caregiving strategies, and who received cognitive behavioural family intervention. There was also a non significant trend for younger children to fare better. These data suggest the importance of early diagnosis and routinely assessing parental caregiving behaviour and beliefs about the origins of pain in planning treatment for children with RAP.

Detection of anuran calling activity in long field recordings for bio-acoustic monitoring

This paper presents a system to analyze long field recordings with low signal-to-noise ratio (SNR) for bio-acoustic monitoring. A method based on spectral peak track, Shannon entropy, harmonic structure and oscillation structure is proposed to automatically detect anuran (frog) calling activity. Gaussian mixture model (GMM) is introduced for modelling those features. Four anuran species widespread in Queensland, Australia, are selected to evaluate the proposed system. A visualization method based on extracted indices is employed for detection of anuran calling activity which achieves high accuracy.

Regolith mass balance inferred from combined mineralogical, geochemical and geophysical studies: Mule Hole gneissic watershed, South India

The aim of this study is to propose a method to assess the long-term chemical weathering mass balance for a regolith developed on a heterogeneous silicate substratum at the small experimental watershed scale by adopting a combined approach of geophysics, geochemistry and mineralogy. We initiated in 2003 a study of the steep climatic gradient and associated geomorphologic features of the edge of the rifted continental passive margin of the Karnataka Plateau, Peninsular India. In the transition sub-humid zone of this climatic gradient we have studied the pristine forested small watershed of Mule Hole (4.3 km(2)) mainly developed on gneissic substratum. Mineralogical, geochemical and geophysical investigations were carried out (i) in characteristic red soil profiles and (ii) in boreholes up to 60 m deep in order to take into account the effect of the weathering mantle roots. In addition, 12 Electrical Resistivity Tomography profiles (ERT), with an investigation depth of 30 m, were generated at the watershed scale to spatially characterize the information gathered in boreholes and soil profiles. The location of the ERT profiles is based on a previous electromagnetic survey, with an investigation depth of about 6 m. The soil cover thickness was inferred from the electromagnetic survey combined with a geological/pedological survey. Taking into account the parent rock heterogeneity, the degree of weathering of each of the regolith samples has been defined using both the mineralogical composition and the geochemical indices (Loss on Ignition, Weathering Index of Parker, Chemical Index of Alteration). Comparing these indices with electrical resistivity logs, it has been found that a value of 400 Ohm m delineates clearly the parent rocks and the weathered materials, Then the 12 inverted ERT profiles were constrained with this value after verifying the uncertainty due to the inversion procedure. Synthetic models based on the field data were used for this purpose. The estimated average regolith thickness at the watershed scale is 17.2 m, including 15.2 m of saprolite and 2 m of soil cover. Finally, using these estimations of the thicknesses, the long-term mass balance is calculated for the average gneiss-derived saprolite and red soil. In the saprolite, the open-system mass-transport function T indicates that all the major elements except Ca are depleted. The chlorite and biotite crystals, the chief sources for Mg (95%), Fe (84%), Mn (86%) and K (57%, biotite only), are the first to undergo weathering and the oligoclase crystals are relatively intact within the saprolite with a loss of only 18%. The Ca accumulation can be attributed to the precipitation of CaCO3 from the percolating solution due to the current and/or the paleoclimatic conditions. Overall, the most important losses occur for Si, Mg and Na with -286 x 10(6) mol/ha (62% of the total mass loss), -67 x 10(6) mol/ha (15% of the total mass loss) and -39 x 10(6) mol/ha (9% of the total mass loss), respectively. Al, Fe and K account for 7%, 4% and 3% of the total mass loss, respectively. In the red soil profiles, the open-system mass-transport functions point out that all major elements except Mn are depleted. Most of the oligoclase crystals have broken down with a loss of 90%. The most important losses occur for Si, Na and Mg with -55 x 10(6) mol/ha (47% of the total mass loss), -22 x 10(6) mol/ha (19% of the total mass loss) and -16 x 10(6) mol/ha (14% of the total mass loss), respectively. Ca, Al, K and Fe account for 8%, 6%, 4% and 2% of the total mass loss, respectively. Overall these findings confirm the immaturity of the saprolite at the watershed scale. The soil profiles are more evolved than saprolite but still contain primary minerals that can further undergo weathering and hence consume atmospheric CO2.

Species-area and species-individual relationships for tropical trees: A comparison of three 50-ha plots

1 Species-accumulation curves for woody plants were calculated in three tropical forests, based on fully mapped 50-ha plots in wet, old-growth forest in Peninsular Malaysia, in moist, old-growth forest in central Panama, and in dry, previously logged forest in southern India. A total of 610 000 stems were identified to species and mapped to < Im accuracy. Mean species number and stem number were calculated in quadrats as small as 5 m x 5 m to as large as 1000 m x 500 m, for a variety of stem sizes above 10 mm in diameter. Species-area curves were generated by plotting species number as a function of quadrat size; species-individual curves were generated from the same data, but using stem number as the independent variable rather than area. 2 Species-area curves had different forms for stems of different diameters, but species-individual curves were nearly independent of diameter class. With < 10(4) stems, species-individual curves were concave downward on log-log plots, with curves from different forests diverging, but beyond about 104 stems, the log-log curves became nearly linear, with all three sites having a similar slope. This indicates an asymptotic difference in richness between forests: the Malaysian site had 2.7 times as many species as Panama, which in turn was 3.3 times as rich as India. 3 Other details of the species-accumulation relationship were remarkably similar between the three sites. Rectangular quadrats had 5-27% more species than square quadrats of the same area, with longer and narrower quadrats increasingly diverse. Random samples of stems drawn from the entire 50 ha had 10-30% more species than square quadrats with the same number of stems. At both Pasoh and BCI, but not Mudumalai. species richness was slightly higher among intermediate-sized stems (50-100mm in diameter) than in either smaller or larger sizes, These patterns reflect aggregated distributions of individual species, plus weak density-dependent forces that tend to smooth the species abundance distribution and 'loosen' aggregations as stems grow. 4 The results provide support for the view that within each tree community, many species have their abundance and distribution guided more by random drift than deterministic interactions. The drift model predicts that the species-accumulation curve will have a declining slope on a log-log plot, reaching a slope of O.1 in about 50 ha. No other model of community structure can make such a precise prediction. 5 The results demonstrate that diversity studies based on different stem diameters can be compared by sampling identical numbers of stems. Moreover, they indicate that stem counts < 1000 in tropical forests will underestimate the percentage difference in species richness between two diverse sites. Fortunately, standard diversity indices (Fisher's sc, Shannon-Wiener) captured diversity differences in small stem samples more effectively than raw species richness, but both were sample size dependent. Two nonparametric richness estimators (Chao. jackknife) performed poorly, greatly underestimating true species richness.

Flow behavior of LDPE and its blends with LLDPE I and II: A comparative study

Studies on melt rheological properties of blends of low density polyethylene (LDPE) with selected grades of linear low density polyethylene (LLDPE), which differ widely in their melt flow indices, are reported, The data obtained in a capillary rheometer are presented to describe the effects of blend composition and shear rate on flow behavior index, melt viscosity, and melt elasticity. In general, blending of LLDPE I that has a low melt flow index (2 g/10 min) with LDPE results in a decrease of its melt viscosity, processing temperature, and the tendency of extrudate distortion, depending on blending ratio. A blending ratio around 20-30% LLDPE I seems optimum from the point of view of desirable improvement in processability behavior. On the other hand, blending of LLDPE II that has a high melt flow index (10 g/10 min) with LDPE offers a distinct advantage in increasing the pseudoplasticity of LDPE/LLDPE II blends.

A new intelligent algorithm for online voltage stability assessment and monitoring

This paper presents a new approach for assessing power system voltage stability based on artificial feed forward neural network (FFNN). The approach uses real and reactive power, as well as voltage vectors for generators and load buses to train the neural net (NN). The input properties of the NN are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The performance of the trained NN is investigated on two systems under various voltage stability assessment conditions. Main advantage is that the proposed approach is fast, robust, accurate and can be used online for predicting the L-indices of all the power system buses simultaneously. The method can also be effectively used to determining local and global stability margin for further improvement measures.

Hydroclimatic influence of large-scale circulation on the variability of reservoir inflow

In this study, the nature of basin-scale hydroclimatic association for Indian subcontinent is investigated. It is found that, the large-scale circulation information from Indian Ocean is also equally important in addition to the El Nino-Southern Oscillation (ENSO), owing to the geographical location of Indian subcontinent. The hydroclimatic association of the variation of monsoon inflow into the Hirakud reservoir in India is investigated using ENSO and EQUatorial INdian Ocean Oscillation (EQUINOO, the atmospheric part of Indian Ocean Dipole mode) as the large-scale circulation information from tropical Pacific Ocean and Indian Ocean regions respectively. Individual associations of ENSO & EQUINOO indices with inflow into Hirakud reservoir are also assessed and found to be weak. However, the association of inflows into Hirakud reservoir with the composite index (CI) of ENSO and EQUINOO is quite strong. Thus, the large-scale circulation information from Indian Ocean is also important apart form the ENSO. The potential of the combined information of ENSO and EQUINOO for predicting the inflows during monsoon is also investigated with promising results. The results of this study will be helpful to water resources managers due to fact that the nature of monsoon inflow is becoming available as an early prediction.

A Proposal for Concurrent Estimation of Static and Dynamic Nonlinearity of ADC

Despite great advances in very large scale integrated-circuit design and manufacturing, performance of even the best available high-speed, high-resolution analog-to-digital converter (ADC) is known to deteriorate while acquiring fast-rising, high-frequency, and nonrepetitive waveforms. Waveform digitizers (ADCs) used in high-voltage impulse recordings and measurements are invariably subjected to such waveforms. Errors resulting from a lowered ADC performance can be unacceptably high, especially when higher accuracies have to be achieved (e.g., when part of a reference measuring system). Static and dynamic nonlinearities (estimated independently) are vital indices for evaluating performance and suitability of ADCs to be used in such environments. Typically, the estimation of static nonlinearity involves 10-12 h of time or more (for a 12-b ADC) and the acquisition of millions of samples at high input frequencies for dynamic characterization. ADCs with even higher resolution and faster sampling speeds will soon become available. So, there is a need to reduce testing time for evaluating these parameters. This paper proposes a novel and time-efficient method for the simultaneous estimation of static and dynamic nonlinearity from a single test. This is achieved by conceiving a test signal, comprised of a high-frequency sinusoid (which addresses dynamic assessment) modulated by a low-frequency ramp (relevant to the static part). Details of implementation and results on two digitizers are presented and compared with nonlinearities determined by the existing standardized approaches. Good agreement in results and time savings achievable indicates its suitability.

Load estimation with uncertainties from opportunistic sampling data: A semiparametric approach

We consider estimating the total load from frequent flow data but less frequent concentration data. There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates that minimizes the biases and makes use of informative predictive variables. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized rating-curve approach with additional predictors that capture unique features in the flow data, such as the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and the discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. Forming this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach for two rivers delivering to the Great Barrier Reef, Queensland, Australia. One is a data set from the Burdekin River, and consists of the total suspended sediment (TSS) and nitrogen oxide (NO(x)) and gauged flow for 1997. The other dataset is from the Tully River, for the period of July 2000 to June 2008. For NO(x) Burdekin, the new estimates are very similar to the ratio estimates even when there is no relationship between the concentration and the flow. However, for the Tully dataset, by incorporating the additional predictive variables namely the discounted flow and flow phases (rising or recessing), we substantially improved the model fit, and thus the certainty with which the load is estimated.

An extended regression approach to estimating loads and their uncertainties in Great Barrier Reef catchments

There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates by minimizing the biases and making use of possible predictive variables. The load estimation procedure can be summarized by the following four steps: - (i) output the flow rates at regular time intervals (e.g. 10 minutes) using a time series model that captures all the peak flows; - (ii) output the predicted flow rates as in (i) at the concentration sampling times, if the corresponding flow rates are not collected; - (iii) establish a predictive model for the concentration data, which incorporates all possible predictor variables and output the predicted concentrations at the regular time intervals as in (i), and; - (iv) obtain the sum of all the products of the predicted flow and the predicted concentration over the regular time intervals to represent an estimate of the load. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized regression (rating-curve) approach with additional predictors that capture unique features in the flow data, namely the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and cumulative discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. The model also has the capacity to accommodate autocorrelation in model errors which are the result of intensive sampling during floods. Incorporating this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach using the concentrations of total suspended sediment (TSS) and nitrogen oxide (NOx) and gauged flow data from the Burdekin River, a catchment delivering to the Great Barrier Reef. The sampling biases for NOx concentrations range from 2 to 10 times indicating severe biases. As we expect, the traditional average and extrapolation methods produce much higher estimates than those when bias in sampling is taken into account.

A Proposal for Concurrent Estimation of Static and Dynamic Nonlinearity of ADC

Despite great advances in very large scale integrated-circuit design and manufacturing, performance of even the best available high-speed, high-resolution analog-to-digital converter (ADC) is known to deteriorate while acquiring fast-rising, high-frequency, and nonrepetitive waveforms. Waveform digitizers (ADCs) used in high-voltage impulse recordings and measurements are invariably subjected to such waveforms. Errors resulting from a lowered ADC performance can be unacceptably high, especially when higher accuracies have to be achieved (e.g., when part of a reference measuring system). Static and dynamic nonlinearities (estimated independently) are vital indices for evaluating performance and suitability of ADCs to be used in such environments. Typically, the estimation of static nonlinearity involves 10-12 h of time or more (for a 12-b ADC) and the acquisition of millions of samples at high input frequencies for dynamic characterization. ADCs with even higher resolution and faster sampling speeds will soon become available. So, there is a need to reduce testing time for evaluating these parameters. This paper proposes a novel and time-efficient method for the simultaneous estimation of static and dynamic nonlinearity from a single test. This is achieved by conceiving a test signal, comprised of a high-frequency sinusoid (which addresses dynamic assessment) modulated by a low-frequency ramp (relevant to the static part). Details of implementation and results on two digitizers are presented and compared with nonlinearities determined by the existing standardized approaches. Good agreement in results and time savings achievable indicates its suitability.