Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Autonomous helicopter hover using an artificial neural network

Details the developments to date of an unmanned air vehicle (UAV) based on a standard size 60 model helicopter. The design goal is to have the helicopter achieve stable hover with the aid of an INS and stereo vision. The focus of the paper is on the development of an artificial neural network (ANN) that makes use of only the INS data to generate hover commands, which are used to directly manipulate the flight servos. Current results show that networks incorporating some form of recurrency (state history) offer little advantage over those without. At this stage, the ANN has partially maintained periods of hover even with misaligned sensors.

Adaptive aneuploidy protects against thiol peroxidase deficiency by increasing respiration via key mitochondrial proteins

Aerobic respiration is a fundamental energy-generating process; however, there is cost associated with living in an oxygen-rich environment, because partially reduced oxygen species can damage cellular components. Organisms evolved enzymes that alleviate this damage and protect the intracellular milieu, most notably thiol peroxidases, which are abundant and conserved enzymes that mediate hydrogen peroxide signaling and act as the first line of defense against oxidants in nearly all living organisms. Deletion of all eight thiol peroxidase genes in yeast (∆8 strain) is not lethal, but results in slow growth and a high mutation rate. Here we characterized mechanisms that allow yeast cells to survive under conditions of thiol peroxidase deficiency. Two independent ∆8 strains increased mitochondrial content, altered mitochondrial distribution, and became dependent on respiration for growth but they were not hypersensitive to H2O2. In addition, both strains independently acquired a second copy of chromosome XI and increased expression of genes encoded by it. Survival of ∆8 cells was dependent on mitochondrial cytochrome-c peroxidase (CCP1) and UTH1, present on chromosome XI. Coexpression of these genes in ∆8 cells led to the elimination of the extra copy of chromosome XI and improved cell growth, whereas deletion of either gene was lethal. Thus, thiol peroxidase deficiency requires dosage compensation of CCP1 and UTH1 via chromosome XI aneuploidy, wherein these proteins support hydroperoxide removal with the reducing equivalents generated by the electron transport chain. To our knowledge, this is the first evidence of adaptive aneuploidy counteracting oxidative stress.

Light history-dependent respiration explains the hysteresis in the daily ecosystem metabolism of seagrass

Oxygen flux between aquatic ecosystems and the water column is a measure of ecosystem metabolism. However, the oxygen flux varies during the day in a “hysteretic” pattern: there is higher net oxygen production at a given irradiance in the morning than in the afternoon. In this study, we investigated the mechanism responsible for the hysteresis in oxygen flux by measuring the daily pattern of oxygen flux, light, and temperature in a seagrass ecosystem (Zostera muelleri in Swansea Shoals, Australia) at three depths. We hypothesised that the oxygen flux pattern could be due to diel variations in either gross primary production or respiration in response to light history or temperature. Hysteresis in oxygen flux was clearly observed at all three depths. We compared this data to mathematical models, and found that the modification of ecosystem respiration by light history is the best explanation for the hysteresis in oxygen flux. Light history-dependent respiration might be due to diel variations in seagrass respiration or the dependence of bacterial production on dissolved organic carbon exudates. Our results indicate that the daily variation in respiration rate may be as important as the daily changes of photosynthetic characteristics in determining the metabolic status of aquatic ecosystems.

Implementation of Art1 and Art2 Artificial Neural Networks on Ring and Mesh Architectures

The Artificial Neural Networks (ANNs) are being used to solve a variety of problems in pattern recognition, robotic control, VLSI CAD and other areas. In most of these applications, a speedy response from the ANNs is imperative. However, ANNs comprise a large number of artificial neurons, and a massive interconnection network among them. Hence, implementation of these ANNs involves execution of computer-intensive operations. The usage of multiprocessor systems therefore becomes necessary. In this article, we have presented the implementation of ART1 and ART2 ANNs on ring and mesh architectures. The overall system design and implementation aspects are presented. The performance of the algorithm on ring, 2-dimensional mesh and n-dimensional mesh topologies is presented. The parallel algorithm presented for implementation of ART1 is not specific to any particular architecture. The parallel algorithm for ARTE is more suitable for a ring architecture.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Questions to Artificial Nature: A Philosophical Study of Interdisciplinary Models and their Functions in Scientific Practice

This thesis presents an interdisciplinary analysis of how models and simulations function in the production of scientific knowledge. The work is informed by three scholarly traditions: studies on models and simulations in philosophy of science, so-called micro-sociological laboratory studies within science and technology studies, and cultural-historical activity theory. Methodologically, I adopt a naturalist epistemology and combine philosophical analysis with a qualitative, empirical case study of infectious-disease modelling. This study has a dual perspective throughout the analysis: it specifies the modelling practices and examines the models as objects of research. The research questions addressed in this study are: 1) How are models constructed and what functions do they have in the production of scientific knowledge? 2) What is interdisciplinarity in model construction? 3) How do models become a general research tool and why is this process problematic? The core argument is that the mediating models as investigative instruments (cf. Morgan and Morrison 1999) take questions as a starting point, and hence their construction is intentionally guided. This argument applies the interrogative model of inquiry (e.g., Sintonen 2005; Hintikka 1981), which conceives of all knowledge acquisition as process of seeking answers to questions. The first question addresses simulation models as Artificial Nature, which is manipulated in order to answer questions that initiated the model building. This account develops further the "epistemology of simulation" (cf. Winsberg 2003) by showing the interrelatedness of researchers and their objects in the process of modelling. The second question clarifies why interdisciplinary research collaboration is demanding and difficult to maintain. The nature of the impediments to disciplinary interaction are examined by introducing the idea of object-oriented interdisciplinarity, which provides an analytical framework to study the changes in the degree of interdisciplinarity, the tools and research practices developed to support the collaboration, and the mode of collaboration in relation to the historically mutable object of research. As my interest is in the models as interdisciplinary objects, the third research problem seeks to answer my question of how we might characterise these objects, what is typical for them, and what kind of changes happen in the process of modelling. Here I examine the tension between specified, question-oriented models and more general models, and suggest that the specified models form a group of their own. I call these Tailor-made models, in opposition to the process of building a simulation platform that aims at generalisability and utility for health-policy. This tension also underlines the challenge of applying research results (or methods and tools) to discuss and solve problems in decision-making processes.

Ultraviolet radiation effects on fruit surface respiration and chlorophyll fluorescence

High-value fruit crops are exposed to a range of environmental conditions that can reduce fruit quality. Solar injury (SI) or sunburn is a common disorder in tropical, sub-tropical, and temperate climates and is related to: 1) high fruit surface temperature; 2) high visible light intensity; and, 3) ultraviolet radiation (UV). Positional changes in fruit that are caused by increased weight or abrupt changes that result from summer pruning, limb breakage, or other damage to the canopy can expose fruit to high solar radiation levels, increased fruit surface temperatures, and increased UV exposure that are higher than the conditions to which they are adapted. In our studies, we examined the effects of high fruit surface temperature, saturating photosynthetically-active radiation (PAR), and short-term UV exposure on chlorophyll fluorescence, respiration, and photosynthesis of fruit peel tissues from tropical and temperate fruit in a simulation of these acute environmental changes. All tropical fruits (citrus, macadamia, avocado, pineapple, and custard apple) and the apple cultivars 'Gala', 'Gold Rush', and 'Granny Smith' increased dark respiration (A0) when exposed to UV, suggesting that UV repair mechanisms were induced. The maximum quantum efficiency of photosystem II (Fv/Fm) and the quantum efficiency of photosystem II (ΦII) were unaffected, indicating no adverse effects on photosystem II (PSII). In contrast, 'Braeburn' apple had a reduced Fv/Fm with no increase in A0 on all sampling dates. There was a consistent pattern in all studies. When Fv/Fm was unaffected by UV treatment, A0 increased significantly. Conversely, when Fv/Fm was reduced by UV treatment, then A0 was unaffected. The pattern suggests that when UV repair mechanisms are effective, PSII is adequately protected, and that this protection occurs at the cost of higher respiration. However, when the UV repair mechanisms are ineffective, not only is PSII damaged, but there is additional short-term damage to the repair mechanisms, indicated by a lack of respiration to provide energy.

Estimation of light interception in research environments: A joint approach using directional light sensors and 3D virtual plants applied to sunflower (Helianthus annuus) and Arabidopsis thaliana in natural and artificial conditions

Light interception is a major factor influencing plant development and biomass production. Several methods have been proposed to determine this variable, but its calculation remains difficult in artificial environments with heterogeneous light. We propose a method that uses 3D virtual plant modelling and directional light characterisation to estimate light interception in highly heterogeneous light environments such as growth chambers and glasshouses. Intercepted light was estimated by coupling an architectural model and a light model for different genotypes of the rosette species Arabidopsis thaliana (L.) Heynh and a sunflower crop. The model was applied to plants of contrasting architectures, cultivated in isolation or in canopy, in natural or artificial environments, and under contrasting light conditions. The model gave satisfactory results when compared with observed data and enabled calculation of light interception in situations where direct measurements or classical methods were inefficient, such as young crops, isolated plants or artificial conditions. Furthermore, the model revealed that A. thaliana increased its light interception efficiency when shaded. To conclude, the method can be used to calculate intercepted light at organ, plant and plot levels, in natural and artificial environments, and should be useful in the investigation of genotype-environment interactions for plant architecture and light interception efficiency. This paper originates from a presentation at the 5th International Workshop on Functional–Structural Plant Models, Napier, New Zealand, November 2007.

Stocking density and artificial habitat influence stock structure and yield from intensive nursery systems for mud crabs Scylla serrata (Forsskål 1775)

Intensive nursery systems are designed to culture mud crab postlarvae through a critical phase in preparation for stocking into growout systems. This study investigated the influence of stocking density and provision of artificial habitat on the yield of a cage culture system. For each of three batches of postlarvae, survival, growth and claw loss were assessed after each of three nursery phases ending at crab instars C1/C2, C4/C5 and C7/C8. Survival through the first phase was highly variable among batches with a maximum survival of 80% from megalops to a mean crab instar of 1.5. Stocking density between 625 and 2300 m-2 did not influence survival or growth in this first phase. Stocking densities tested in phases 2 and 3 were 62.5, 125 and 250 m -2. At the end of phases 2 and 3, there were five instar stages present, representing a more than 20-fold size disparity within the populations. Survival became increasingly density-sensitive following the first phase, with higher densities resulting in significantly lower survival (phase 2: 63% vs. 79%; phase 3: 57% vs. 64%). The addition of artificial habitat in the form of pleated netting significantly improved survival at all densities. The mean instar attained by the end of phase 2 was significantly larger at a lower stocking density and without artificial habitat. No significant effect of density or habitat on harvest size was detected in phase 3. The highest incidence of claw loss was 36% but was reduced by lowering stocking densities and addition of habitat. For intensive commercial production, yield can be significantly increased by addition of a simple net structure but rapidly decreases the longer crablets remain in the nursery.

Treatment of shrimp farm effluent with omnivorous finfish and artificial substrates

Long-term environmental sustainability and community acceptance of the shrimp farming industry in Australia requires on-going development of efficient cost-effective effluent treatment options. In this study, we aimed to evaluate the effectiveness of a shrimp farm treatment system containing finfish and vertical artificial substrates (VAS). This was achieved by (1) quantifying the individual and collective effects of grey mullet (Mugil cephalus L.) and VASs on water and sediment quality, and (2) comparing the retention of N in treatment systems with and without the presence of finfish (M. cephalus and the siganid Siganus nebulosus (Quoy & Gaimard)), where light was selectively removed. Artificial substrates were found to significantly improve the settlement of particulate material, regardless of the presence of finfish. Mullet actively resuspended settled solids and reduced the production of nitrate when artificial substrates were absent. However, appreciable nitrification was observed when mullet were present together with artificial substrates. The total quantity of N retained by the mullet was found to be 1.8– 2.4% of the incoming pond effluent N. It was estimated that only 21% of the pond effluent N was available for mullet consumption. When S. nebulosus was added, total finfish N retention increased from 1.8% to 3.9%, N retention by mullet also improved (78±16 to 132±21-mg N day−1 before and after siganid addition respectively). Presence of filamentous macroalgae (Enteromorpha spp.) was found to improve the removal of N from pond effluent relative to treatments where light was excluded. Denitrification was also a significant sink for N (up to 24% N removed). Despite the absence of algal productivity and greater availability of nitrate, denitrification was not higher in treatments where light was excluded. Mullet were found to have no effect on the rates of denitrification but significantly reduced macroalgal growth on the surface of the water. When mullet were absent, excessive macroalgal growth led to reduced dissolved oxygen concentrations and nitrification. This study concludes that the culture of mullet alone in shrimp farm effluent treatment systems does not result in significant retention of N but can contribute to the control of macroalgal biomass. To improve N retention and removal, further work should focus on polyculturing a range of species and also on improving denitrification.

Nutrient levels in experimental tanks supplied with prawn pond effluent: the effect of artificial substrate and different densities of the banana prawn Penaeus merguiensis (de Man)

To experimentally investigate the effect of vertical artificial substrate and different densities of the banana prawn Penaeus (Fenneropenaeus) merguiensis on nutrient levels in prawn pond effluent, a time series experiment was conducted in a replicated tank system supplied periodically with discharge from a prawn production pond. Few differences (P>0.05) were detected between tanks without prawns, and tanks with low densities (5 prawns in 1700 litres) of prawns (10-12 g), in terms of nitrogen and phosphorus in the water column over the 28-day experimental period. Higher densities of prawns (starting at 25 or 50 per tank) caused an elevation of these macronutrients in the water column. This was partly due to prawn biomass losses from mortalities and weight reductions in the tank system. The survival and condition of prawns was significantly (P<0.05) reduced in tanks at these higher densities. The presence of artificial substrate (2 m2 tank-1) did not affect (P>0.05) the levels of nutrients in tank water columns, but significantly (P<0.05) increased the amount of nitrogen in tank residues left at the end of the trial when no prawns were present. The prawns had obviously been grazing on surfaces inside the tanks, and their swimming actions appeared to keep light particulate matter in suspension. Higher prawn densities increased microalgal blooms, which presumably kept ammonia levels low, and it is suggested that this association may provide the means for improved remediation of prawn farm effluent in the future.

Fencing artificial waterpoints failed to influence density and distribution of red kangaroos (Macropus rufus)

Provision of artificial waterpoints in Australian rangelands has resulted in an increase in the range and density of kangaroos. At high densities, kangaroos can inhibit vegetation regeneration, particularly in some protected areas where harvesting is prohibited. Fencing off waterpoints has been proposed to limit these impacts. Our aim was to determine whether fencing off waterpoints during a drought (when kangaroos would be especially water-limited) would influence the density and distribution of red kangaroos (Macropus rufus). Two waterpoints were fenced within the first 6 months of the 27-month study and a further two waterpoints were kept unfenced as controls in Idalia National Park, western Queensland. We estimated kangaroo densities around waterpoints from walked line-transect counts, and their grazing distribution from dung-pellet counts. Fencing off waterpoints failed to influence either the density or distribution up to 4 km from the waterpoints. Our results indicate that food availability, rather than the location of waterpoints, determines kangaroo distribution. Few areas in the rangelands are beyond kangaroos' convenient reach from permanent waterpoints. Therefore, fencing off waterpoints without explicitly considering the spatial context in relation to other available water sources will fail to achieve vegetation regeneration.