Future long-horizon performance measurement conditional on past survival

This paper examines the measurement of long-horizon abnormal performance when stock selection is conditional on an extended period of past survival. Filtering on survival results in a sample driven towards more-established, frequently traded stocks and this has implications for the choice of benchmark used in performance measurement (especially in the presence of the well-documented size effect). A simulation study is conducted to document the properties of commonly employed performance measures conditional on past survival. The results suggest that the popular index benchmarks used in long-horizon event studies are severely biased and yield test statistics that are badly misspecified. In contrast, a matched-stock benchmark based on size and industry performs consistently well. Also, an eligible-stock index designed to mitigate the influence of the size effect proves effective.

Anisotropy-based robust performance analysis of finite horizon linear discrete time varying systems

We consider a problem of robust performance analysis of linear discrete time varying systems on a bounded time interval. The system is represented in the state-space form. It is driven by a random input disturbance with imprecisely known probability distribution; this distributional uncertainty is described in terms of entropy. The worst-case performance of the system is quantified by its a-anisotropic norm. Computing the anisotropic norm is reduced to solving a set of difference Riccati and Lyapunov equations and a special form equation.

General practice in Australia 2020: "robust and ready" or "rudderless and reeling"?

The future role and structure of Australian general practice remains uncertain, despite a decade of seemingly constant change following the release of the National Health Strategy papers. Some of the suggested change strategies (such as rural Practice Incentive Payments and practice accreditation) have been implemented; others (such as general practitioner involvement with area health authorities in delivering national goals and targets for communities) still await attention. An overarching vision for our health care system in 2020 and general practice's role within it are still to be clearly enunciated. Australia is at variance with other Western countries, such as the United Kingdom, Canada and New Zealand, which have spent significant time refocusing their health systems to deal with an ageing population with an increased burden of chronic disease. Health bureaucrats and governments need to invest strategically in operational primary care now. This will require the active commitment of general practice's national bodies to articulate and actively promote a shared vision for Australian general practice.

Expression of growth factors in Dictyostelium discoideum

Growth factors and their binding proteins are important proteins regulating mammalian cell proliferation and differentiation so there is considerable interest in producing them as recombinant proteins, especially in hosts that do not already produce a complex mixture of growth factors. Many growth factors require posttranslational modifications making them unsuitable for production in Escherichia coli or other prokaryotes. Since several expression vector systems have been recently developed for foreign protein production in the cellular slime mould, Dictyostelium discoideum, we attempted to use two of these systems to express human insulin-like growth factor binding protein 6 (hIGFBP6) and bovine beta-cellulin (bBTC) as secreted proteins. Although both proteins were successfully produced in stably transformed amoebae, no secretion was detected in spite of several attempts to facilitate this occurring.

Nitrification in a Vertisol subsoil and its relationship to the accumulation of ammonium-nitrogen at depth

Unusually high concentrations of ammonium have been observed in a Vertisol below 1 m depth in southeast Queensland. This study investigated the possibility that an absence of nitrification is allowing this ammonium to accumulate and persist over time, and examined the soil environmental characteristics that may be responsible for limiting nitrifying organisms. The possibility that anaerobiosis, soil acidity, soil salinity, low organic carbon concentrations, and/or an absence of active nitrifying microorganisms were responsible for limiting nitrification was examined in laboratory and field studies. The presence/absence of anaerobic conditions was determined qualitatively using a field test to give an indication of electron lability. In addition, an incubation study was conducted and soil environmental conditions were improved for nitrifying organisms by adjusting the pH from 4.4 to 7, adjusting the electrical conductivity from 1.6 to 0.5 dS/m, amending with a soluble carbon substrate at a rate of 500 mg/kg, and using microorganisms from the surface horizon to inoculate to the subsoil. Over a 180-day period no nitrification was detected in the control samples from the incubation study, indicating that an extremely low rate of nitrification is likely to be responsible for allowing ammonium to accumulate in this soil. Analysis of the effect of soil environmental conditions on nitrification revealed that anaerobic conditions did not exist at depth and that pH, EC, organic carbon, and inoculation treatments added in isolation had no effect on nitrification. However, when inoculum was added to the soil in combination with pH, a significant increase in nitrification was observed, and the greatest amount of nitrification was observed when inoculum, pH, and EC treatments were added in combination. It was concluded that the reason for the low rate of nitrification in this soil is primarily the absence of a significant population of active nitrifying microorganisms, which may have been unable to colonise the subsoil environment due to its acidic, and to a lesser extent, its saline environment.

The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.