Transparent Exopolymer Particles (TEP): an overlooked factor in the process of biofilm formation in aquatic environments

We hypothesize that transparent exopolymer particles (TEP), present in high concentrations in most sea and freshwaters, are critical agents for biofilm initiation and development in many natural and anthropogenic aquatic environments. These gel-like particles appear in many forms; amorphous blobs, clouds, sheets, filaments or clumps ranging in size from ~2 to ~200 µm. TEP are mostly polysaccharide, negatively charged, very sticky and are frequently colonized by bacteria. TEP may be considered a "planktonic" subgroup of exopolymeric substances (EPS), widely studied in biofilm research. Recognition of TEP involvement in biofilm formation has important implications for a comprehensive understanding of the complexities of this process in aquatic environments and may also contribute to the considerable efforts being made in the global water industry to mitigate the harmful effects of biofouling in water treatment and desalination plants

Quantitative Selection of Long-Short Hedge Funds

We develop a quantitative model to select hedge funds in the long-short equity sector. The selection strategy is verified on a survivorship-bias-free hedge fund database, from January 1990 to September 2002. We focus on the hedge funds acting exclusively in the U.S. market. We identify Fama-French factors and GSCI as the risk factors. Based on the evidence that many hedge funds do not exhibit persistent performance, we believe that persistent alpha is not generated based on publicly available information and opportunistic changes of exposure with respect to the risk factors. Instead we expect moderate exposure funds to be those who establish investment decisions based on special information or proprietary research. A hedge fund selection strategy is introduced and checked with out-of-sample data. A simulation of hedge funds from 1927 to 2002 is conducted. The funds selected according to our strategy demonstrate superior performance persistently.Hedge Fund; Long Short Strategy; Fama-French; Commodity; Performance Persistence; Skewness; Selection

Omega Portfolio Construction with Johnson Distributions

The Omega performance measure is equiped with the original family of Johnson distributions. Explicit representations for Omega or Sharpe with all four Johnson cumulated densities were derived to construct portfolios with respect to 4 mutually independent moments. Additionally, decompositions of higher portfolio moments were derived to include expected higher moments on an individual fund or strategy level. Hedge fund index back-testing has shown that Johnson-Omega gives significantly higher returns without sacrificing capital protection needs. Omega with Johnson distributions solves the weaknesses from Sharpe and achieves a more predictable and stable performance by exploiting the persistence of potentially significant higher moments up to fourth order.Sharpe,Omega, Johnson distribution, Skewness, Higher Moments,Significant Moments, Portfolio Construction, Hedge Funds, Market Neutral

Real-time evolution of an embedded controller for an autonomous helicopter

In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it, thus avoiding the ldquoreality gaprdquo. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GApsilas individuals converge towards more robust solutions

Supervised Control of a Flying Performing Robot using its Intrinsic Sound

We present the current results of our ongoing research in achieving efficient control of a flying robot for a wide variety of possible applications. A lightweight small indoor helicopter has been equipped with an embedded system and relatively simple sensors to achieve autonomous stable flight. The controllers have been tuned using genetic algorithms to further enhance flight stability. A number of additional sensors would need to be attached to the helicopter to enable it to sense more of its environment such as its current location or the location of obstacles like the walls of the room it is flying in. The lightweight nature of the helicopter very much restricts the amount of sensors that can be attached to it. We propose utilising the intrinsic sound signatures of the helicopter to locate it and to extract features about its current state, using another supervising robot. The analysis of this information is then sent back to the helicopter using an uplink to enable the helicopter to further stabilise its flight and correct its position and flight path without the need for additional sensors