Evolutionary biology of Gondwanan non-biting midges (Diptera: Chironomidae)

The potential restriction to effective dispersal and gene flow caused by habitat fragmentation can apply to multiple levels of evolutionary scale; from the fragmentation of ancient supercontinents driving diversification and speciation on disjunct landmasses, to the isolation of proximate populations as a result of their inability to cross intervening unsuitable habitat. Investigating the role of habitat fragmentation in driving diversity within and among taxa can thus include inferences of phylogenetic relationships among taxa, assessments of intraspecific phylogeographic structure and analyses of gene flow among neighbouring populations. The proposed Gondwanan clade within the chironomid (non-biting midge) subfamily Orthocladiinae (Diptera: Chironomidae) represents a model system for investigating the role that population fragmentation and isolation has played at different evolutionary scales. A pilot study by Krosch et al (2009) indentified several highly divergent lineages restricted to ancient rainforest refugia and limited gene flow among proximate sites within a refuge for one member of this clade, Echinocladius martini Cranston. This study provided a framework for investigating the evolutionary history of this taxon and its relatives more thoroughly. Populations of E. martini were sampled in the Paluma bioregion of northeast Queensland to investigate patterns of fine-scale within- and among-stream dispersal and gene flow within a refuge more rigorously. Data was incorporated from Krosch et al (2009) and additional sites were sampled up- and downstream of the original sites. Analyses of genetic structure revealed strong natal site fidelity and high genetic structure among geographically proximate streams. Little evidence was found for regular headwater exchange among upstream sites, but there was distinct evidence for rare adult flight among sites on separate stream reaches. Overall, however, the distribution of shared haplotypes implied that both larval and adult dispersal was largely limited to the natal stream channel. Patterns of regional phylogeographic structure were examined in two related austral orthoclad taxa – Naonella forsythi Boothroyd from New Zealand and Ferringtonia patagonica Sæther and Andersen from southern South America – to provide a comparison with patterns revealed in their close relative E. martini. Both taxa inhabit tectonically active areas of the southern hemisphere that have also experienced several glaciation events throughout the Plio-Pleistocene that are thought to have affected population structure dramatically in many taxa. Four highly divergent lineages estimated to have diverged since the late Miocene were revealed in each taxon, mirroring patterns in E. martini; however, there was no evidence for local geographical endemism, implying substantial range expansion post-diversification. The differences in pattern evident among the three related taxa were suggested to have been influenced by variation in the responses of closed forest habitat to climatic fluctuations during interglacial periods across the three landmasses. Phylogeographic structure in E. martini was resolved at a continental scale by expanding upon the sampling design of Krosch et al (2009) to encompass populations in southeast Queensland, New South Wales and Victoria. Patterns of phylogeographic structure were consistent with expectations and several previously unrecognised lineages were revealed from central- and southern Australia that were geographically endemic to closed forest refugia. Estimated divergence times were congruent with the timing of Plio-Pleistocene rainforest contractions across the east coast of Australia. This suggested that dispersal and gene flow of E. martini among isolated refugia was highly restricted and that this taxon was susceptible to the impacts of habitat change. Broader phylogenetic relationships among taxa considered to be members of this Gondwanan orthoclad group were resolved in order to test expected patterns of evolutionary affinities across the austral continents. The inferred phylogeny and estimated divergence times did not accord with expected patterns based on the geological sequence of break-up of the Gondwanan supercontinent and implied instead several transoceanic dispersal events post-vicariance. Difficulties in appropriate taxonomic sampling and accurate calibration of molecular phylogenies notwithstanding, the sampling regime implemented in the current study has been the most intensive yet performed for austral members of the Orthocladiinae and unsurprisingly has revealed both novel taxa and phylogenetic relationships within and among described genera. Several novel associations between life stages are made here for both described and previously unknown taxa. Investigating evolutionary relationships within and among members of this clade of proposed Gondwanan orthoclad taxa has demonstrated that a complex interaction between historical population fragmentation and dispersal at several levels of evolutionary scale has been important in driving diversification in this group. While interruptions to migration, colonisation and gene flow driven by population fragmentation have clearly contributed to the development and maintenance of much of the diversity present in this group, long-distance dispersal has also played a role in influencing diversification of continental biotas and facilitating gene flow among disjunct populations.

Multiple evolutionary rate classes in animal genome evolution

The proportion of functional sequence in the human genome is currently a subject of debate. The most widely accepted figure is that approximately 5% is under purifying selection. In Drosophila, estimates are an order of magnitude higher, though this corresponds to a similar quantity of sequence. These estimates depend on the difference between the distribution of genomewide evolutionary rates and that observed in a subset of sequences presumed to be neutrally evolving. Motivated by the widening gap between these estimates and experimental evidence of genome function, especially in mammals, we developed a sensitive technique for evaluating such distributions and found that they are much more complex than previously apparent. We found strong evidence for at least nine well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least seven classes in an alignment of four mammals, including human. We also identified at least three rate classes in human ancestral repeats. By positing that the largest of these ancestral repeat classes is neutrally evolving, we estimate that the proportion of nonneutrally evolving sequence is 30% of human ancestral repeats and 45% of the aligned portion of the genome. However, we also question whether any of the classes represent neutrally evolving sequences and argue that a plausible alternative is that they reflect variable structure-function constraints operating throughout the genomes of complex organisms.

Active transonic aerofoil design optimization using robust multiobjective evolutionary algorithms

The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary-layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. Two test cases are conducted: the first test assumes the boundary-layer transition position is at 45% of chord from the leading edge, and the second test considers robust design optimization for the shock control bump at the variability of boundary-layer transition positions. The numerical result shows that the optimization method coupled to uncertainty design techniques produces Pareto optimal shock-control-bump shapes, which have low sensitivity and high aerodynamic performance while having significant total drag reduction.

Double-shock control bump design optimization using hybridized evolutionary algorithms

This study investigates the application of two advanced optimization methods for solving active flow control (AFC) device shape design problem and compares their optimization efficiency in terms of computational cost and design quality. The first optimization method uses hierarchical asynchronous parallel multi-objective evolutionary algorithm and the second uses hybridized evolutionary algorithm with Nash-Game strategies (Hybrid-Game). Both optimization methods are based on a canonical evolution strategy and incorporate the concepts of parallel computing and asynchronous evaluation. One type of AFC device named shock control bump (SCB) is considered and applied to a natural laminar flow (NLF) aerofoil. The concept of SCB is used to decelerate supersonic flow on suction/pressure side of transonic aerofoil that leads to a delay of shock occurrence. Such active flow technique reduces total drag at transonic speeds which is of special interest to commercial aircraft. Numerical results show that the Hybrid-Game helps an EA to accelerate optimization process. From the practical point of view, applying a SCB on the suction and pressure sides significantly reduces transonic total drag and improves lift-to-drag (L/D) value when compared to the baseline design.

Why evolutionary realism underpins evolutionary economic analysis and theory

This paper offers a reply to Jochen Runde's critical appraisal of the ontological framework underpinning Dopfer and Potts's (2008) General Theory of Economic Evolution. We argue that Runde's comprehensive critique contains several of what we perceive to be misunderstandings in relation to the key concepts of ‘generic’ and ‘meso’ that we seek here to unpack and redress.

Cross likelihood ratio based speaker clustering using eigenvoice models

This paper proposes the use of eigenvoice modeling techniques with the Cross Likelihood Ratio (CLR) as a criterion for speaker clustering within a speaker diarization system. The CLR has previously been shown to be a robust decision criterion for speaker clustering using Gaussian Mixture Models. Recently, eigenvoice modeling techniques have become increasingly popular, due to its ability to adequately represent a speaker based on sparse training data, as well as an improved capture of differences in speaker characteristics. This paper hence proposes that it would be beneficial to capitalize on the advantages of eigenvoice modeling in a CLR framework. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 35.1% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Manufacturing Yield Improvement by Clustering

Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.

An FPGA-based approach to multi-objective evolutionary algorithm for multi-disciplinary design optimisation

This paper investigates the field programmable gate array (FPGA) approach for multi-objective and multi-disciplinary design optimisation (MDO) problems. One class of optimisation method that has been well-studied and established for large and complex problems, such as those inherited in MDO, is multi-objective evolutionary algorithms (MOEAs). The MOEA, nondominated sorting genetic algorithm II (NSGA-II), is hardware implemented on an FPGA chip. The NSGA-II on FPGA application to multi-objective test problem suites has verified the designed implementation effectiveness. Results show that NSGA-II on FPGA is three orders of magnitude better than the PC based counterpart.

Efficient hybrid-game strategies coupled to evolutionary algorithms for robust multidisciplinary design optimization in aerospace engineering

A number of game strategies have been developed in past decades and used in the fields of economics, engineering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high quality solutions with less computational time. In this paper, two optimization techniques are considered; the first optimization method uses multifidelity hierarchical Pareto-optimality. The second optimization method uses the combination of game strategies Nash-equilibrium and Pareto-optimality. This paper shows how game strategies can be coupled to multiobjective evolutionary algorithms and robust design techniques to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid and non-Hybrid-Game strategies are demonstrated.

Identifying differences in safe roads and crash prone roads using clustering data mining

Road asset managers are overwhelmed with a high volume of raw data which they need to process and utilise in supporting their decision making. This paper presents a method that processes road-crash data of a whole road network and exposes hidden value inherent in the data by deploying the clustering data mining method. The goal of the method is to partition the road network into a set of groups (classes) based on common data and characterise the class crash types to produce a crash profiles for each cluster. By comparing similar road classes with differing crash types and rates, insight can be gained into these differences that are caused by the particular characteristics of their roads. These differences can be used as evidence in knowledge development and decision support.

Evolutionary morphing for music composition

From one view of composition—let us call it the inspired or “Mozartian” view—musical compositions arrive fully formed in the mind of the composer and simply require transcription. In reality, however, it seems that very few people are so inspired, and composition is often more akin to a gradual clarification and refinement of partially formed ideas on the musical landscape. Particular landmarks in the compositional landscape tend to become clear before others, such that the incomplete piece is a patchwork of disconnected musical islands. An interactive evolutionary morphing system may provide some assistance for composers, to help build bridges between musical islands by generating hybrid musical transitions.