Modelling optimal risk allocation in PPP projects using artificial neural networks

This paper aims to establish, train, validate, and test artificial neural network (ANN) models for modelling risk allocation decision-making process in public-private partnership (PPP) projects, mainly drawing upon transaction cost economics. An industry-wide questionnaire survey was conducted to examine the risk allocation practice in PPP projects and collect the data for training the ANN models. The training and evaluation results, when compared with those of using traditional MLR modelling technique, show that the ANN models are satisfactory for modelling risk allocation decision-making process. The empirical evidence further verifies that it is appropriate to utilize transaction cost economics to interpret risk allocation decision-making process. It is recommended that, in addition to partners' risk management mechanism maturity level, decision-makers, both from public and private sectors, should also seriously consider influential factors including partner's risk management routines, partners' cooperation history, partners' risk management commitment, and risk management environmental uncertainty. All these factors influence the formation of optimal risk allocation strategies, either by their individual or interacting effects.

Active surface shaping for artificial skins

Artificial skins exhibit different mechanical properties in compare to natural skins. This drawback makes physical interaction with artificial skins to be different from natural skin. Increasing the performance of the artificial skins for robotic hands and medical applications is addressed in the present paper. The idea is to add active controls within artificial skins in order to improve their dynamic or static behaviors. This directly results into more interactivity of the artificial skins. To achieve this goal, a piece-wise linear anisotropic model for artificial skins is derived. Then a model of matrix of capacitive MEMS actuators for the control purpose is coupled with the model of artificial skin. Next an active surface shaping control is applied through the control of the capacitive MEMS actuators which shapes the skin with zero error and in a desired time. A simulation study is presented to validate the idea of using MEMS actuator for active artificial skins. In the simulation, we actively control 128 capacitive micro actuators for an artificial fingertip. The fingertip provides the required shape in a required time which means the dynamics of the skin is improved.

The effect of artificial night light on the abundance of nocturnal birds

Urbanisation is increasing rapidly, impacting on a broad range of species. The proliferation of electric light has transformed the night time environment; however, our understanding on the effects of artificial night lighting on fauna, including nocturnal birds, is extremely limited. The aim ofthis research was to determine whether artificial night light affected the abundance of nocturnal birds. Spotlighting surveys were undertaken in Research Park, Melbourne, Victoria, along three 300 m transects. Each transect was surveyed five times during three light treatments: when lights were on, 20 minutes after lights were turned off and when lights were absent, over a period often nights. A total of 123 nocturnal birds was detected during survey nights. Two species were recorded - the Southern Boobook Ninox novaeseelandiae and the Tawny Frogmouth Podargus strigoides. The Tawny Frogmouth was detected along all three transects (n=121); however, the Southern Boobook was detected along one transect only (n=2). None of the light treatments had a significant effect on bird abundance. Neither did location, habitat or the combined effects of light treatments, location and habitat. The results of this research will contribute to a growing body of knowledge and support future conservation activities for species in areas undergoing urbanisation. {The Victorian Naturalist 127 (5) 2010, 192-195).

Ecosystem restoration with teeth : what role for predators?

Recent advances highlight the potential for predators to restore ecosystems and confer resilience against globally threatening processes, including climate change and biological invasions. However, releasing the ecological benefits of predators entails significant challenges. Here, we discuss the economic, environmental and social considerations affecting predator-driven ecological restoration programmes, and suggest approaches for reducing the undesirable impacts of predators. Because the roles of predators are context dependent, we argue for increased emphasis on predator functionality in ecosystems and less on the identities and origins of species and genotypes. We emphasise that insufficient attention is currently given to the importance of variation in the social structures and behaviours of predators in influencing the dynamics of trophic interactions. Lastly, we outline experiments specifically designed to clarify the ecological roles of predators and their potential utility in ecosystem restoration.

Prediction of Drug Dissolution Profiles Using Artificial Neural Networks

This paper investigates the efficacy and reliability of Artificial Neural Networks (ANNs) as an intelligent decision support tool for pharmaceutical product formulation. Two case studies have been employed to evaluate capabilities of the Multilayer Perceptron network in predicting drug dissolution/release profiles. Performances of the network were evaluated using similarity factor (&fnof[sub 2]) — an index recommended by the United States Food and Drug Administration for profile comparison in pharmaceutical research. In addition, the bootstrap method was applied to assess the network prediction reliability by estimating confidence intervals associated with the results. The Multilayer Perceptron network also demonstrated a superior performance in comparison with multiple regression models. The results reveal that the ANN system has potentials to be a decision support tool for profile prediction in pharmaceutical experimentation, and the bootstrap method could be used as a means to assess reliability of the network prediction. [ABSTRACT FROM AUTHOR].

Detection of brain conditions from evoked responses using artificial neural networks

[No abstract available]

Green rosellas Platycercus caledonicus nesting in artificial structures

The Green Rosella Platycercus caledonicus nests primarily in hollows of living or dead eucalypt trees. This note describes an instance of Green Rosellas nesting in a wall cavity, and reviews other instances of nesting in artificial structures by this species. The building used in this instance appears to have been used by Green Rosellas in 2009 and 50 years earlier, in 1958.

Accurate modeling of low actuation voltage RFMEMS switches using artificial neural networks

This paper presents a fast and accurate method for extracting the scattering parameters of a RF MEMS switch by using its essential parameters. A neural network is developed for parametric modeling of the switch. The essential parameters of the switch are analyzed in terms of its return loss and isolation with variation of its geometrical component values. Simulation results show that the proposed approach can be used to accurately model the RF characteristics of RF-MEMS switches. The results show good agreement between the neural network prediction and electromagnetic simulations.