Rigs-to-reefs: Will the deep sea benefit from artificial habitat?

As a peak in the global number of offshore oil rigs requiring decommissioning approaches, there is growing pressure for the implementation of a "rigs-to-reefs" program in the deep sea, whereby obsolete rigs are converted into artificial reefs. Such decommissioned rigs could enhance biological productivity, improve ecological connectivity, and facilitate conservation/restoration of deep-sea benthos (eg cold-water corals) by restricting access to fishing trawlers. Preliminary evidence indicates that decommissioned rigs in shallower waters can also help rebuild declining fish stocks. Conversely, potential negative impacts include physical damage to existing benthic habitats within the "drop zone", undesired changes in marine food webs, facilitation of the spread of invasive species, and release of contaminants as rigs corrode. We discuss key areas for future research and suggest alternatives to offset or minimize negative impacts. Overall, a rigs-to-reefs program may be a valid option for deep-sea benthic conservation. © The Ecological Society of America.

An improved Gbest guided artificial bee colony (IGGABC) algorithm for classification and prediction tasks

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploitation process for solving mathematical problems, however the poor exploration creates problems like slow convergence and trapping in local minima. In this paper, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration processes. The experimental results show that IGGABC algorithm performs better than that standard GGABC, BP and ABC algorithms for Boolean data classification and time-series prediction tasks.

Artificial neural network modeling of flow stress in hot rolling

In this study, an artificial neural network model is proposed to predict the flow stress variations during the hot rolling process. Optimization of the proposed neural network with respect to number of neurons within the hidden layer, different training methods and transfer functions of the neural network is performed. The results of the optimal network were compared with those of the conventional analytic method and it is shown that using an optimal neural network the mean calculated error is drastically reduced.

Teaching a digital performing agent: artificial neural network and hidden Markov Model for recognising and performing dance movement

For a Digital Performing Agent to be able to perform live with a human dancer, it would be useful for the agent to be able to contextualize the movement the dancer is performing and to have a suitable movement vocabulary with which to contribute to the performance. In this paper we will discuss our research into the use of Artificial Neural Networks (ANN) as a means of allowing a software agent to learn a shared vocabulary of movement from a dancer. The agent is able to use the learnt movements to form an internal representation of what the dancer is performing, allowing it to follow the dancer, generate movement sequences based on the dancer's current movement and dance independently of the dancer using a shared movement vocabulary. By combining the ANN with a Hidden Markov Model (HMM) the agent is able to recognize short full body movement phrases and respond when the dancer performs these phrases. We consider the relationship between the dancer and agent as a means of supporting the agent's learning and performance, rather than developing the agent's capability in a self-contained fashion.

Development of a novel soft parallel robot equipped with polymeric artificial muscles

This paper presents the design, analysis and fabrication of a novel low-cost soft parallel robot for biomedical applications, including bio-micromanipulation devices. The robot consists of two active flexible polymer actuator-based links, which are connected to two rigid links by means of flexible joints. A mathematical model is established between the input voltage to the polymer actuators and the robot's end effector position. The robot has two degrees-of-freedom, making it suitable for handling planar micromanipulation tasks. Moreover, a number of robots can be configured to operate in a cooperative manner for increasing micromanipulation dexterity. Finally, the experimental results demonstrate two main motion modes of the robot.

Artificial selection for food colour preferences

Colour is an important factor in food detection and acquisition by animals using visually based foraging. Colour can be used to identify the suitability of a food source or improve the efficiency of food detection, and can even be linked to mate choice. Food colour preferences are known to exist, but whether these preferences are heritable and how these preferences evolve is unknown. Using the freshwater fish Poecilia reticulata, we artificially selected for chase behaviour towards two different-coloured moving stimuli: red and blue spots. A response to selection was only seen for chase behaviours towards the red, with realized heritabilities ranging from 0.25 to 0.30. Despite intense selection, no significant chase response was recorded for the blue-selected lines. This lack of response may be due to the motion-detection mechanism in the guppy visual system and may have novel implications for the evolvability of responses to colour-related signals. The behavioural response to several colours after five generations of selection suggests that the colour opponency system of the fish may regulate the response to selection.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.

Artificial water points facilitate the spread of an invasive vertebrate in arid Australia

Summary: The spread of invasive species after their initial introduction is often facilitated by human actions. In some cases, invaders only become established in habitats where dominant native species have been displaced as a result of human actions or where humans inadvertently provide essential resources such as food, water or shelter. We investigated if dams that provide water for livestock have facilitated the cane toad's (Rhinella marina) invasion of a hot semi-arid landscape by providing toads with a resource subsidy and hence refuge from extreme heat and aridity. To determine the relationship between the presence of surface water and habitat occupancy by toads, we surveyed natural and artificial water features for cane toads during the annual dry season. We used radiotracking and acoustic tags to determine whether movement patterns and shelter use of cane toads were focussed around dams. To determine whether dams provide toads with refuge from extreme heat and aridity, we deployed plaster models with internal thermometers to estimate ambient temperatures and toad desiccation rates in shelter sites. To determine whether dams alleviate the stress experienced by toads, we measured plasma corticosterone levels of toads that sheltered in and away from dams. Toads were present in sites with standing water and absent from waterless sites. Most radiotracked toads sheltered within 1 m of water. Toad movements were focussed around water. Toads tracked with passive acoustic telemetry over a 6-month dry season were highly resident at dams. Plaster models placed in toad shelter sites away from the water lost 27% more mass and experienced higher temperatures than models placed near the water's edge. Toads that sheltered in terrestrial shelters exhibited higher plasma corticosterone levels compared to toads that sheltered near dams. Dams provide toads with refuge habitats where they are less at risk from overheating and dehydration. Synthesis and applications. Artificial water points can facilitate biological invasions in arid regions by providing a resource subsidy for water-dependent invasive species. Our study suggests that there is scope to control populations of water-dependent invasive vertebrates in arid regions by restricting their access to artificial water points.

Statistical modelling of artificial neural network for sorting temporally synchronous spikes

Artificial neural network (ANN) models are able to predict future events based on current data. The usefulness of an ANN lies in the capacity of the model to learn and adjust the weights following previous errors during training. In this study, we carefully analyse the existing methods in neuronal spike sorting algorithms. The current methods use clustering as a basis to establish the ground truths, which requires tedious procedures pertaining to feature selection and evaluation of the selected features. Even so, the accuracy of clusters is still questionable. Here, we develop an ANN model to specially address the present drawbacks and major challenges in neuronal spike sorting. New enhancements are introduced into the conventional backpropagation ANN for determining the network weights, input nodes, target node, and error calculation. Coiflet modelling of noise is employed to enhance the spike shape features and overshadow noise. The ANN is used in conjunction with a special spiking event detection technique to prioritize the targets. The proposed enhancements are able to bolster the training concept, and on the whole, contributing to sorting neuronal spikes with close approximations.