Behavioural isolation may facilitate homoploid hybrid speciation in cichlid fish

Hybrid speciation is constrained by the homogenizing effects of gene flow from the parental species. In the absence of post-mating isolation due to structural changes in the genome, or temporal or spatial premating isolation, another form of reproductive isolation would be needed for homoploid hybrid speciation to occur. Here, we investigate the potential of behavioural mate choice to generate assortative mating among hybrids and parental species. We made three-first-generation hybrid crosses between different species of African cichlid fish. In three-way mate-choice experiments, we allowed hybrid and nonhybrid females to mate with either hybrid or nonhybrid males. We found that hybrids generally mated nonrandomly and that hybridization can lead to the expression of new combinations of traits and preferences that behaviourally isolate hybrids from both parental species. Specifically, we find that the phenotypic distinctiveness of hybrids predicts the symmetry and extent of their reproductive isolation. Our data suggest that behavioural mate choice among hybrids may facilitate the establishment of isolated hybrid populations, even in proximity to one or both parental species.

Hybrid Breakdown in Cichlid Fish

Studies from a wide diversity of taxa have shown a negative relationship between genetic compatibility and the divergence time of hybridizing genomes. Theory predicts the main breakdown of fitness to happen after the F1 hybrid generation, when heterosis subsides and recessive allelic (Dobzhansky-Muller) incompatibilities are increasingly unmasked. We measured the fitness of F2 hybrids of African haplochromine cichlid fish bred from species pairs spanning several thousand to several million years divergence time. F2 hybrids consistently showed the lowest viability compared to F1 hybrids and non-hybrid crosses (crosses within the grandparental species), in agreement with hybrid breakdown. Especially the short- and long-term survival (2 weeks to 6 months) of F2 hybrids was significantly reduced. Overall, F2 hybrids showed a fitness reduction of 21% compared to F1 hybrids, and a reduction of 43% compared to the grandparental, non-hybrid crosses. We further observed a decrease of F2 hybrid viability with the genetic distance between grandparental lineages, suggesting an important role for negative epistatic interactions in cichlid fish postzygotic isolation. The estimated time window for successful production of F2 hybrids resulting from our data is consistent with the estimated divergence time between the multiple ancestral lineages that presumably hybridized in three major adaptive radiations of African cichlids.

Remote Monitoring of Fish in Small Streams: A Unified Approach Using Pit Tags

Accurate assessments of fish populations are often limited by re-observation or recapture events. Since the early 1990s, passive integrated transponders (PIT tags) have been used to understand the biology of many fish species. Until recently, PIT applications in small streams have been limited to physical recapture events. To maximize recapture probability, we constructed PIT antenna arrays in small streams to remotely detect individual fish. Experiences from two different laboratories (three case studies) allowed us to develop a unified approach to applying PIT technology for enhancing data assessments. Information on equipment, its installation, tag considerations, and array construction is provided. Theoretical and practical definitions are introduced to standardize metrics for assessing detection efficiency. We demonstrate how certain conditions (stream discharge, vibration, and ambient radio frequency noise) affect the detection efficiency and suggest that by monitoring these conditions, expectations of efficiency can be modified. We emphasize the importance of consistently estimating detection efficiency for fisheries applications.

Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

Aquatic ecology monitoring: water quality, fish, fish catch, sanitation and disease vectors: monitoring No.9, 9th – 12th September 2011

The results reported on were from a monitoring survey No. 9 undertaken between 9th and 12th September 2011 during construction period of the Bujagali Hydropower Project (BHPP). Two pre-construction, baseline surveys in April 2000 and April 2006 were conducted and so far, during construction phase of the project, eight monitoring surveys have been undertaken i.e. in September 2007, April 2008, April 2009, October 2009, April 2010, September 2010, April 2011 and the present one, in September 2011. Since 2009 biannual monitoring surveys have been conducted at an upstream and a downstream transect of the BHPP with emphasis on the following aspects: water quality determinants biology and ecology of fishes and food webs fish stock and fish catch including economic aspects of catch and sanitation/vector studies (bilharzias and river blindness)in addition to the above mentioned studies, a soil pH survey was undertaken on 15th October 2011 in the area behind the reservoir whose filling started a week earlier. The findings of pH status in the catchment of the dam are also contained in this report.

A Hybrid AUV design for shallow water reef navigation

The highly unstructured nature of coral reef environments makes them difficult for current robotic vehicles to efficiently navigate. Typical research and commercial platforms have limited autonomy within these environments and generally require tethers and significant external infrastructure. This paper outlines the development of a new robotic vehicle for underwater monitoring and surveying in highly unstructured environments and presents experimental results illustrating the vehicle’s performance. The hybrid AUV design developed by the CSIRO robotic reef monitoring team realises a compromise between endurance, manoeuvrability and functionality. The vehicle represents a new era in AUV design specifically focused at providing a truly low-cost research capability that will progress environmental monitoring through unaided navigation, cooperative robotics, sensor network distribution and data harvesting.

A New robot for environmental monitoring on the Great Barrier Reef

A vast amount of research into autonomous underwater navigation has, and is, being conducted around the world. However, typical research and commercial platforms have limited autonomy and are generally unable to navigate efficiently within coral reef environments without tethers and significant external infrastructure. This paper outlines the development and presents experimental results into the performance evaluation of a new robotic vehicle for underwater monitoring and surveying in highly unstructured environments. The hybrid AUV design developed by the CSIRO robotic reef monitoring team realises a compromise between endurance, manoeuvrability and functionality. The vehicle represents a new era in AUV design specifically focused at providing a truly lowcost research capability that will progress environmental monitoring through unaided navigation, cooperative robotics, sensor network distribution and data harvesting.

Monitoring energy performance of air conditioners in classrooms

Air quality and temperatures in classrooms are important factors influencing the student learning process. To improve the thermal comfort of classrooms for Queensland State Schools, Queensland Government initiated the "Cooler Schools Program". One of the key objectives under this program was to develop low energy cooling systems as an alternative to high energy demand conventioanl split system of air conditioning (AC) systems. In order to compare and evaluate the energy performance of different types of air conditioners installed in classrooms, monitoring systems were installed in a state primary school located in the greater outer urban area of Brisbane, Australia. It was found that the installation of monitoring systems could have a significant impact on the accuracy of the data being collected. By comparing the estimated energy efficiency ratio (EER)for four qualified air conditioners included in this study, it was also found that AC6, a hybrid air conditioner newly developed by the Queensland Department of Public Works (DPW), had the best energy performance, although the current data were not able to show the full advantages of the system.

Performance characterization of VGCF/epoxy nanocomposite sensors under static load cycles and in static structural health monitoring

Compared to conventional metal-foil strain gauges, nanocomposite piezoresistive strain sensors have demonstrated high strain sensitivity and have been attracting increasing attention in recent years. To fulfil their ultimate success, the performance of vapor growth carbon fiber (VGCF)/epoxy nanocomposite strain sensors subjected to static cyclic loads was evaluated in this work. A strain-equivalent quantity (resistance change ratio) in cantilever beams with intentionally induced notches in bending was evaluated using the conventional metal-foil strain gauges and the VGCF/epoxy nanocomposite sensors. Compared to the metal-foil strain gauges, the nanocomposite sensors are much more sensitive to even slight structural damage. Therefore, it was confirmed that the signal stability, reproducibility, and durability of these nanocomposite sensors are very promising, leading to the present endeavor to apply them for static structural health monitoring.

Design, simulation and analysis of a parallel hybrid electric propulsion system for unmanned aerial vehicles

Aerial Vehicles (UAV) has become a significant growing segment of the global aviation industry. These vehicles are developed with the intention of operating in regions where the presence of onboard human pilots is either too risky or unnecessary. Their popularity with both the military and civilian sectors have seen the use of UAVs in a diverse range of applications, from reconnaissance and surveillance tasks for the military, to civilian uses such as aid relief and monitoring tasks. Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. This paper presents the development of a parallel Hybrid Electric Propulsion System (HEPS) on a small fixed-wing UAV incorporating an Ideal Operating Line (IOL) control strategy. A simulation model of an UAV was developed in the MATLAB Simulink environment, utilising the AeroSim Blockset and the in-built Aerosonde UAV block and its parameters. An IOL analysis of an Aerosonde engine was performed, and the most efficient (i.e. provides greatest torque output at the least fuel consumption) points of operation for this engine were determined. Simulation models of the components in a HEPS were designed and constructed in the MATLAB Simulink environment. It was demonstrated through simulation that an UAV with the current HEPS configuration was capable of achieving a fuel saving of 6.5%, compared to the ICE-only configuration. These components form the basis for the development of a complete simulation model of a Hybrid-Electric UAV (HEUAV).

A Hybrid Queue-based Bayesian Network Framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and Exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an $R^2$ goodness of fit of 0.9994 and 0.9982 respectively over a 10 hour test period. The utility of the framework is demonstrated on a number of usage scenarios including real time monitoring and `what-if' analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.

Pathway to sustainability : the Lady Elliot Island hybrid solar power station

The Lady Elliot Island eco-resort, on the Great Barrier Reef, operates with a strong sustainability ethic, and has broken away from its reliance on diesel generators, an initiative which has ongoing and substantial economic benefit. The first step was an energy audit that led to a 35% reduction in energy usage, to an average of 575 kWh per day. The eco-resort then commissioned a hybrid solar power station, in 2008, with energy storage in battery banks. Solar power is currently (2013) providing about 160 kWh of energy per day, and the eco-resort’s diesel fuel usage has decreased from 550 to 100 litres per day, enabling the power station to pay for itself in 3 years. The eco-resort plans to complete its transition to renewable energy by 2015, by installing additional solar panels, and a 10-15 kW wind turbine. This paper starts by discussing why the eco-resort chose a hybrid solar power station to transition to renewable energy, and the barriers to change. It then describes the power station, upgrades through to 2013, the power control system, the problems that were solved to realise the potential of a facility operating in a harsh and remote environment, and its performance. The paper concludes by outlining other eco-resort sustainability practices, including education and knowledge-sharing initiatives, and monitoring the island’s environmental and ecological condition.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.