Influence of historical landscapes, drainage evolution and ecological traits on patterns of genetic diversity in Southeast Asian freshwater snakehead fishes

Snakehead fishes in the family Channidae are obligate freshwater fishes represented by two extant genera, the African Parachannna and the Asian Channa. These species prefer still or slow flowing water bodies, where they are top predators that exercise high levels of parental care, have the ability to breathe air, can tolerate poor water quality, and interestingly, can aestivate or traverse terrestrial habitat in response to seasonal changes in freshwater habitat availability. These attributes suggest that snakehead fishes may possess high dispersal potential, irrespective of the terrestrial barriers that would otherwise constrain the distribution of most freshwater fishes. A number of biogeographical hypotheses have been developed to account for the modern distributions of snakehead fishes across two continents, including ancient vicariance during Gondwanan break-up, or recent colonisation tracking the formation of suitable climatic conditions. Taxonomic uncertainty also surrounds some members of the Channa genus, as geographical distributions for some taxa across southern and Southeast (SE) Asia are very large, and in one case is highly disjunct. The current study adopted a molecular genetics approach to gain an understanding of the evolution of this group of fishes, and in particular how the phylogeography of two Asian species may have been influenced by contemporary versus historical levels of dispersal and vicariance. First, a molecular phylogeny was constructed based on multiple DNA loci and calibrated with fossil evidence to provide a dated chronology of divergence events among extant species, and also within species with widespread geographical distributions. The data provide strong evidence that trans-continental distribution of the Channidae arose as a result of dispersal out of Asia and into Africa in the mid–Eocene. Among Asian Channa, deep divergence among lineages indicates that the Oligocene-Miocene boundary was a time of significant species radiation, potentially associated with historical changes in climate and drainage geomorphology. Mid-Miocene divergence among lineages suggests that a taxonomic revision is warranted for two taxa. Deep intra-specific divergence (~8Mya) was also detected between C. striata lineages that occur sympatrically in the Mekong River Basin. The study then examined the phylogeography and population structure of two major taxa, Channa striata (the chevron snakehead) and the C. micropeltes (the giant snakehead), across SE Asia. Species specific microsatellite loci were developed and used in addition to a mitochondrial DNA marker (Cyt b) to screen neutral genetic variation within and among wild populations. C. striata individuals were sampled across SE Asia (n=988), with the major focus being the Mekong Basin, which is the largest drainage basin in the region. The distributions of two divergent lineages were identified and admixture analysis showed that where they co-occur they are interbreeding, indicating that after long periods of evolution in isolation, divergence has not resulted in reproductive isolation. One lineage is predominantly confined to upland areas of northern Lao PDR to the north of the Khorat Plateau, while the other, which is more closely related to individuals from southern India, has a widespread distribution across mainland SE Asian and Sumatra. The phylogeographical pattern recovered is associated with past river networks, and high diversity and divergence among all populations sampled reveal that contemporary dispersal is very low for this taxon, even where populations occur in contiguous freshwater habitats. C. micropeltes (n=280) were also sampled from across the Mekong River Basin, focusing on the lower basin where it constitutes an important wild fishery resource. In comparison with C. striata, allelic diversity and genetic divergence among populations were extremely low, suggesting very recent colonisation of the greater Mekong region. Populations were significantly structured into at least three discrete populations in the lower Mekong. Results of this study have implications for establishing effective conservation plans for managing both species, that represent economically important wild fishery resources for the region. For C. micropeltes, it is likely that a single fisheries stock in the Tonle Sap Great Lake is being exploited by multiple fisheries operations, and future management initiatives for this species in this region will need to account for this. For C. striata, conservation of natural levels of genetic variation will require management initiatives designed to promote population persistence at very localised spatial scales, as the high level of population structuring uncovered for this species indicates that significant unique diversity is present at this fine spatial scale.

Bayesian spatiotemporal analysis of socio-ecologic drivers of Ross River Virus transmission in Queensland, Australia

This study aims to examine the impact of socio-ecologic factors on the transmission of Ross River virus (RRV) infection and to identify areas prone to social and ecologic-driven epidemics in Queensland, Australia. We used a Bayesian spatiotemporal conditional autoregressive model to quantify the relationship between monthly variation of RRV incidence and socio-ecologic factors and to determine spatiotemporal patterns. Our results show that the average increase in monthly RRV incidence was 2.4% (95% credible interval (CrI): 0.1–4.5%) and 2.0% (95% CrI: 1.6–2.3%) for a 1°C increase in monthly average maximum temperature and a 10 mm increase in monthly average rainfall, respectively. A significant spatiotemporal variation and interactive effect between temperature and rainfall on RRV incidence were found. No association between Socio-economic Index for Areas (SEIFA) and RRV was observed. The transmission of RRV in Queensland, Australia appeared to be primarily driven by ecologic variables rather than social factors.

Implementation of an embedded sensor network for the coordination of Slocum gliders for coastal monitoring and observation

Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanography through their versatility, autonomy and endurance. However, they are still an underutilized technology. For coastal operations, the ability to track a certain feature is of interest to ocean scientists. Adaptive and predictive path planning requires frequent communication with significant data transfer. Currently, most AUVs rely on satellite phones as their primary communication. This communication protocol is expensive and slow. To reduce communication costs and provide adequate data transfer rates, we present a hardware modification along with a software system that provides an alternative robust disruption- tolerant communications framework enabling cost-effective glider operation in coastal regions. The framework is specifically designed to address multi-sensor deployments. We provide a system overview and present testing and coverage data for the network. Additionally, we include an application of ocean-model driven trajectory design, which can benefit from the use of this network and communication system. Simulation and implementation results are presented for single and multiple vehicle deployments. The presented combination of infrastructure, software development and deployment experience brings us closer to the goal of providing a reliable and cost-effective data transfer framework to enable real-time, optimal trajectory design, based on ocean model predictions, to gather in situ measurements of interesting and evolving ocean features and phenomena.

Improving matching process in social network

Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.

A social matching system for an online dating network : a preliminary study

Due to the change in attitudes and lifestyles, people expect to find new partners and friends via various ways now-a-days. Online dating networks create a network for people to meet each other and allow making contact with different objectives of developing a personal, romantic or sexual relationship. Due to the higher expectation of users, online matching companies are trying to adopt recommender systems. However, the existing recommendation techniques such as content-based, collaborative filtering or hybrid techniques focus on users explicit contact behaviors but ignore the implicit relationship among users in the network. This paper proposes a social matching system that uses past relations and user similarities in finding potential matches. The proposed system is evaluated on the dataset collected from an online dating network. Empirical analysis shows that the recommendation success rate has increased to 31% as compared to the baseline success rate of 19%.

Assessment of commercial network RTK user positioning performance over long inter-station distances

The paper provides an assessment of the performance of commercial Real Time Kinematic (RTK) systems over longer than recommended inter-station distances. The experiments were set up to test and analyse solutions from the i-MAX, MAX and VRS systems being operated with three triangle shaped network cells, each having an average inter-station distance of 69km, 118km and 166km. The performance characteristics appraised included initialization success rate, initialization time, RTK position accuracy and availability, ambiguity resolution risk and RTK integrity risk in order to provide a wider perspective of the performance of the testing systems. ----- ----- The results showed that the performances of all network RTK solutions assessed were affected by the increase in the inter-station distances to similar degrees. The MAX solution achieved the highest initialization success rate of 96.6% on average, albeit with a longer initialisation time. Two VRS approaches achieved lower initialization success rate of 80% over the large triangle. In terms of RTK positioning accuracy after successful initialisation, the results indicated a good agreement between the actual error growth in both horizontal and vertical components and the accuracy specified in the RMS and part per million (ppm) values by the manufacturers. ----- ----- Additionally, the VRS approaches performed better than the MAX and i-MAX when being tested under the standard triangle network with a mean inter-station distance of 69km. However as the inter-station distance increases, the network RTK software may fail to generate VRS correction and then may turn to operate in the nearest single-base RTK (or RAW) mode. The position uncertainty reached beyond 2 meters occasionally, showing that the RTK rover software was using an incorrect ambiguity fixed solution to estimate the rover position rather than automatically dropping back to using an ambiguity float solution. Results identified that the risk of incorrectly resolving ambiguities reached 18%, 20%, 13% and 25% for i-MAX, MAX, Leica VRS and Trimble VRS respectively when operating over the large triangle network. Additionally, the Coordinate Quality indicator values given by the Leica GX1230 GG rover receiver tended to be over-optimistic and not functioning well with the identification of incorrectly fixed integer ambiguity solutions. In summary, this independent assessment has identified some problems and failures that can occur in all of the systems tested, especially when being pushed beyond the recommended limits. While such failures are expected, they can offer useful insights into where users should be wary and how manufacturers might improve their products. The results also demonstrate that integrity monitoring of RTK solutions is indeed necessary for precision applications, thus deserving serious attention from researchers and system providers.

A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots. The framework proposed combines nonlinear dimensionality reduction, nonlinear regression under noise, and variational Bayesian learning to create consistent probabilistic representations of places from images. These generative models are learnt from a few images and used for multi-class place recognition where classification is computed from a set of feature-vectors. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions and blurring. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Flight testing of an augmented separation management system providing separation protection during failure of the central communication network

Future air traffic management concepts often involve the proposal of automated separation management algorithms that replaces human air traffic controllers. This paper proposes a new type of automated separation management algorithm (based on the satisficing approach) that utilizes inter-aircraft communication and a track file manager (or bank of Kalman filters) that is capable of resolving conflicts during periods of communication failure. The proposed separation management algorithm is tested in a range of flight scenarios involving during periods of communication failure, in both simulation and flight test (flight tests were conducted as part of the Smart Skies project). The intention of the conducted flight tests was to investigate the benefits of using inter-aircraft communication to provide an extra layer of safety protection in support air traffic management during periods of failure of the communication network. These benefits were confirmed.

Contexts, hybrids and network governance : a comparison of three case-studies in infrastructure governance

While hybrid governance arrangements have been a major element of organisational architecture for some time, the contemporary operating environment has brought to the fore new conditions and expectations for the governance of entities that span conventional public sector departments, private firms and community organisations or groups. These conditions have resulted in a broader array of mixed governance configurations including Public Private Partnerships, alliances, and formal and informal collaborations. In some such arrangements, market based or ‘complete’ contractual relationships have been introduced to replace or supplement existing traditional ‘hierarchical’ and/or newer relational ‘network-oriented’ institutional associations. While there has been a greater reliance on collaborative or relational contracts as an underpinning institutional model, other modes of hierarchy and market may remain in operation. The success of these emergent hybrid forms has been mixed. There are examples of hybrids that have been well adopted, achieving the desired goals of efficiency, effectiveness and financial accountability; while others have experienced implementation problems which have undermined their results. This paper postulates that the cultural and institutional context within which hybrids operate may contribute to the implementation processes employed and the level of success attained. The paper explores hybrid arrangements through three cases of the use of inter-organisational arrangements in three different national contexts. Distilling the various elements of hybrids and the impact of institutional context will provide important insights for those charged with the responsibility for the formation and key infrastructure and public value development.

Approximate Bayesian computation using indirect inference

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms.

Improving matching process in social network using implicit and explicit user information

Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).

Expression and connection : the integration of the reflective learning process and the writing process into social network sites

A number of instructors have recently adopted social network sites (SNSs) for learning. However, the learning design of SNSs often remains at a preliminary level similar to a personal log book because it does not properly include reflective learning elements such as individual reflection and collaboration. This article looks at the reflective learning process and the public writing process as a way of improving the quality of reflective learning on SNSs. It proposes a reflective learning model on SNSs based on two key pedagogical concepts for social networking: individual expression and collaborative connection. It is expected that the model would be helpful for instructors in designing a reflective learning process on SNSs in an effective and flexible way.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.

Test and evaluation of non conventional instrument transformers and sampled value process bus on Powerlink's transmission network

IEC 61850 Process Bus technology has the potential to improve cost, performance and reliability of substation design. Substantial costs associated with copper wiring (designing, documentation, construction, commissioning and troubleshooting) can be reduced with the application of digital Process Bus technology, especially those based upon international standards. An IEC 61850-9-2 based sampled value Process Bus is an enabling technology for the application of Non-Conventional Instrument Transformers (NCIT). Retaining the output of the NCIT in its native digital form, rather than conversion to an analogue output, allows for improved transient performance, dynamic range, safety, reliability and reduced cost. In this paper we report on a pilot installation using NCITs communicating across a switched Ethernet network using the UCAIug Implementation Guideline for IEC 61850-9-2 (9-2 Light Edition or 9-2LE). This system was commissioned in a 275 kV Line Reactor bay at Powerlink Queensland’s Braemar substation in 2009, with sampled value protection IEDs 'shadowing' the existing protection system. The results of commissioning tests and twelve months of service experience using a Fibre Optic Current Transformer (FOCT) from Smart Digital Optics (SDO) are presented, including the response of the system to fault conditions. A number of remaining issues to be resolved to enable wide-scale deployment of NCITs and IEC 61850-9-2 Process Bus technology are also discussed.