Effect of sampling density on the measurement of stream condition indicators in two lowland Australian streams

There is widespread application of indicators to the assessment of environmental condition of streams. These indicators are intended for use by managers in making various comparative and absolute assessments and often have a role in resource allocation and performance assessment. Therefore, the problem of formally defining confidence in the results is important but difficult because the sampling strategies used are commonly based on a compromise between the requirements of statistical rigour and the pragmatic issues of access and resources. It is rare to see this compromise explicitly considered and consequently there is seldom quantification of the uncertainty that could affect the confidence a manager has in an indicator. In this paper, we present a method for quantitatively assessing the tradeoffs between sampling density and uncertainty in meeting various monitoring objectives. Assessments using judgement‐based representative reaches are shown to be unreliable; instead a sampling approach is recommended based on the random selection of measuring sites. A detailed dataset was collected along two streams in Victoria, Australia, and the effect of sampling density was assessed by subsampling from this dataset with precision related to the number of sites assessed per reach length and the intensity of the sampling at each site. The sampling scheme to achieve a given precision is shown to depend on the monitoring objective. In particular, three objectives were considered: (1) making a baseline assessment of current condition; (2) change detection; and (3) detection of a critical threshold in condition. Change detection is shown to be more demanding than assessing baseline condition with additional sampling effort required to achieve the same precision. Sampling to detect a critical threshold depends on nominating acceptable values of Type I and II error and the size of the effect to be detected. Copyright © 2006 John Wiley & Sons, Ltd.     

Organic matter release in low temperature thermal treatment of biological sludge for reduction of excess sludge production.

Thermal treatment applied in association with a biological system allows for a significant reduction in excess sludge production (approximately 50%). In general, heat treatment is described as a sludge disintegration technique. This paper offers a thorough study on the impact of heat treatment, at temperatures below 100 degrees C, on the solubilisation of the sludge COD and its biodegradability. Discontinuous heating experiments were performed on activated and digested sludge. At all temperatures tested the released COD for digested sludge was systematically higher than that for activated sludge (15 and 40%, respectively, at 95 degrees C for 40 min of contact time). For the first 30 min, a 1st order kinetic, with respect to the residual COD, was systematically found. In the range of 40-95 degrees C, digested sludge had a lower activation energy than activated sludge (26 kcal/mol compared to 70-160 kcal/mol). COD solubilisation is thus more positively influenced by temperature in the case of activated sludge. This may be due to the significant difference in the ratio of protein/carbohydrate in digested and activated sludge (1-5 and 0.2-0.7, respectively). The increase in the COD/TKN ratio in the solubilised fraction after thermal treatment of activated sludge suggests a preferential solubilisation of proteins over carbohydrates. Respirometric tests performed on the solubilised COD showed that whatever the sludge origin, only 40-50% of released COD is biodegradable at a conventional hydraulic retention time (i.e., 24 h). Hence, heat treatment would act more through organic matter solubilisation rather than by a biodegradability increase.     

Novel tiO2 nanocatalysts for wastewater purification: tapping energy from the sun.

Water treatment using TiO2 semiconductor as a durable heterogeneous photocatalyst has been the focus of environmentalists in recent years. Currently, we developed an inexpensive and highly efficient approach for synthesizing nitrogen-doped TiO2 with lower band-gap energy that can respond to visible light. Doping on the molecular scale led to an enhanced nitrogen concentration of up to 21.8%. Reflectance measurements showed the synthesized N-doped TiO2 nanoparticles are catalytically active with the absorbance that extends into the visible region up to 600 nm. The water purification potential of this new class of compound was evaluated by studying the photodegradation of Acid Orange 7 (AO7) and E. coli. Experiments were conducted to compare the photocatalytic activities of N-doped TiO2 nanocatalysts and commercially available Degussa P25 power under identical solar light exposure. N-doped TiO2 demonstrated superior photocatalytic activities in both chemical compound degradation and bactericidal reactions. The result of this study shows the potential of applying new generations of catalyst for wastewater purification and disinfection.     

Sampling and the discretization theorem

An algorithm was obtained for restoration of a continuous process from a finite number of its equispaced discrete samples. The necessary and sufficient sampling conditions were formulated.     

Learning with belief levels

We study learning of predicate logics formulas from “elementary facts,” i.e. from the values of the predicates in the given model. Several models of learning are considered, but most of our attention is paid to learning with belief levels. We propose an axiom system which describes what we consider to be a human scientist's natural behavior when trying to explore these elementary facts. It is proved that no such system can be complete. However we believe that our axiom system is “practically” complete. Theorems presented in the paper in some sense confirm our hypothesis.     

Results of a User Study on 2D Hurricane Visualization

We present the results from a user study looking at the ability of observers to mentally integrate wind direction and magnitude over a vector field. The data set chosen for the study is an MM5 (PSU/NCAR Mesoscale Model) simulation of Hurricane Lili over the Gulf of Mexico as it approaches the southeastern United States. Nine observers participated in the study. This study investigates the effect of layering on the observer's ability to determine the magnitude and direction of a vector field. We found a tendency for observers to underestimate the magnitude of the vectors and a counter‐clockwise bias when determining the average direction of a vector field. We completed an additional study with two observers to try to uncover the source of the counter‐clockwise bias. These results have direct implications to atmospheric scientists, but may also be able to be applied to other fields that use 2D vector fields.     

Assessing continuous bivariate effects among different groups through nonparametric regression models: An application to breast cancer detection

In many applications, the joint effect of two continuous covariates on the target binary response may vary across groups defined by levels of a given factor. A testing procedure that would enable this type of surface-by-factor interactions to be detected has been designed. To accomplish this goal, a logistic generalized additive model (GAM) with bivariate continuous interactions varying across groups defined by levels of a factor is considered. A local scoring algorithm based on local linear kernel smoothers was implemented to estimate the proposed logistic GAM. Bootstrap resampling techniques were used for the purpose of testing for factor-by-surface interactions. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. The adequacy of the bootstrap-based test was assessed by means of a simulation study. If a factor-by-surface interaction is detected in the model, it is then established that the use of the odds-ratio curves is very useful in obtaining a direct interpretation of the fitted model. The benefits of using this methodology when analyzing real data are illustrated by applying the technique to the outputs produced by a computerized system dedicated to the early detection of breast cancer.     

Driving Mechanisms of Delamination Related Reliability Problems in Exposed Pad Packages

Exposed pad packages were introduced in the late 1980s and early 1990s because of their excellent thermal and electrical performance. Despite these advantages, the exposed pad packages experience a lot of thermo-hygro-mechanical related reliability problems during qualification and testing. Examples are die lift, which occurs predominantly after moisture sensitivity level conditions, and die-attach to leadframe delamination leading to downbond stitch breaks during temperature cycling. In this chapter, nonlinear finite element (FE) models using fracture mechanics based J-integral calculations are used to assess the reliability problems of the exposed pad package family. Using the parametric FE models any geometrical and material effects can be explored to their impact on the occurrence diepad delamination, and dielift. For instance the impact of diepad size is found to be of much less importance as the impact of die thickness is. Using the fracture mechanics approach, the starting location for the delamination from thermo-hygro-mechanical point of view is deducted. The results indicate that when diepad delamination is present, cracks are likely to grow beneath the die and dielift will occur. The interaction between dielift and other failure modes, such as lifted ball bonds, are not found to be very significant. The FE models are combined with simulation-based optimization methods to deduct design guidelines for optimal reliability of the exposed pad family.     

Miniature in vivo robots for remote and harsh environments

Long-term human space exploration will require contingencies for emergency medical procedures including some capability to perform surgery. The ability to perform minimally invasive surgery (MIS) would be an important capability. The use of small incisions reduces surgical risk, but also eliminates the ability of the surgeon to view and touch the surgical environment directly. Robotic surgery, or telerobotic surgery, may provide emergency surgical care in remote or harsh environments such as space flight, or extremely forward environments such as battlefields. However, because current surgical robots are large and require extensive support personnel, their implementation has remained limited in forward environments, and they would be difficult, or impossible, to use in space flight or on battlefields. This paper presents experimental analysis of miniature fixed-base and mobile in vivo robots to support MIS surgery in remote and harsh environments. The objective is to develop wireless imaging and task-assisting robots that can be placed inside the abdominal cavity during surgery. Such robots will provide surgical task assistance and enable an on-site or remote surgeon to view the surgical environment from multiple angles. This approach is applicable to long-duration space flight, battlefield situations, and for traditional medical centers and other remote surgical locations.     

Modeling wireless fading channels via stochastic differential equations: identification and estimation based on noisy measurements

This paper is concerned with modeling and identification of wireless channels using noisy measurements. The models employed are governed by stochastic differential equations (SDEs) in state space form, while the identification method is based on the expectation-maximization (EM) algorithm and Kalman filtering. The algorithm is tested against real channel measurements. The results presented include state space models for the channels, estimates of inphase and quadrature components, and estimates of the corresponding Doppler power spectral densities (DPSDs), from sample noisy measurements. Based on the available measurements, it is concluded that state space models of order two are sufficient for wireless flat fading channel characterization.