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.

Stress at the synapse : signal transduction mechanisms of adrenal steroids at neuronal membranes

As the key neuron-to-neuron interface, the synapse is involved in learning and memory, including traumatic memories during times of stress. However, the signal transduction mechanisms by which stress mediates its lasting effects on synapse transmission and on memory are not fully understood. A key component of the stress response is the increased secretion of adrenal steroids. Adrenal steroids (e.g., cortisol) bind to genomic mineralocorticoid and glucocorticoid receptors (gMRs and gGRs) in the cytosol. In addition, they may act through membrane receptors (mMRs and mGRs), and signal transduction through these receptors may allow for rapid modulation of synaptic transmission as well as modulation of membrane ion currents. mMRs increase synaptic and neuronal excitability; mechanisms include the facilitation of glutamate release through extracellular signal-regulated kinase signal transduction. In contrast, mGRs decrease synaptic and neuronal excitability by reducing calcium currents through N-methyl-D-aspartate receptors and voltage-gated calcium channels by way of protein kinase A- and G protein-dependent mechanisms. This body of functional data complements anatomical evidence localizing GRs to the postsynaptic membrane. Finally, accumulating data also suggest the possibility that mMRs and mGRs may show an inverted U-shaped dose response, whereby glutamatergic synaptic transmission is increased by low doses of corticosterone acting at mMRs and decreased by higher doses acting at mGRs. Thus, synaptic transmission is regulated by mMRs and mGRs, and part of the stress signaling response is a direct and bidirectional modulation of the synapse itself by adrenal steroids.

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.

Incorporation of human recombinant tropoelastin into silk fibroin membranes with the view to repairing Bruch’s membrane

Bombyx mori silk fibroin membranes provide a potential delivery vehicle for both cells and extracellular matrix (ECM) components into diseased or injured tissues. We have previously demonstrated the feasibility of growing retinal pigment epithelial cells (RPE) on fibroin membranes with the view to repairing the retina of patients afflicted with age-related macular degeneration (AMD). The goal of the present study was to investigate the feasibility of incorporating the ECM component elastin, in the form of human recombinant tropoelastin, into these same membranes. Two basic strategies were explored: (1) membranes prepared from blended solutions of fibroin and tropoelastin; and (2) layered constructs prepared from sequentially cast solutions of fibroin, tropoelastin, and fibroin. Optimal conditions for RPE attachment were achieved using a tropoelastin-fibroin blend ratio of 10 to 90 parts by weight. Retention of tropoelastin within the blend and layered constructs was confirmed by immunolabelling and Fourier-transform infrared spectroscopy (FTIR). In the layered constructs, the bulk of tropoelastin was apparently absorbed into the initially cast fibroin layer. Blend membranes displayed higher elastic modulus, percentage elongation, and tensile strength (p < 0.01) when compared to the layered constructs. RPE cell response to fibroin membranes was not affected by the presence of tropoelastin. These findings support the potential use of fibroin membranes for the co-delivery of RPE cells and tropoelastin.

Free-standing alumina nanobottles and nanotubes pre-integrated into nanoporous alumina membranes

A novel interfacial structure consisting of long (up to 5 μm), thin (about 300 nm), highly-ordered, free-standing, highly-reproducible aluminum oxide nanobottles and long tubular nanocapsules attached to a rigid, thin (less than 1 μm) nanoporous anodic alumina membrane is fabricated by simple, fast, catalyst-free, environmentally friendly voltage-pulse anodization. A growth mechanism is proposed based on the formation of straight channels in alumina membrane by anodization, followed by neck formation due to a sophisticated voltage control during the process. This process can be used for the fabrication of alumina nanocontainers with highly controllable geometrical size and volume, vitally important for various applications such as material and energy storage, targeted drug and diagnostic agent delivery, controlled drug and active agent release, gene and biomolecule reservoirs, micro-biologically protected platforms, nano-bioreactors, tissue engineering and hydrogen storage.

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.

Calculations of helium separation via uniform pores of stanene-based membranes

The development of low energy cost membranes to separate He from noble gas mixtures is highly desired. In this work, we studied He purification using recently experimentally realized, two-dimensional stanene (2D Sn) and decorated 2D Sn (SnH and SnF) honeycomb lattices by density functional theory calculations. To increase the permeability of noble gases through pristine 2D Sn at room temperature (298 K), two practical strategies (i.e., the application of strain and functionalization) are proposed. With their high concentration of large pores, 2D Sn-based membrane materials demonstrate excellent helium purification and can serve as a superior membrane over traditionally used, porous materials. In addition, the separation performance of these 2D Sn-based membrane materials can be significantly tuned by application of strain to optimize the He purification properties by taking both diffusion and selectivity into account. Our results are the first calculations of He separation in a defect-free honeycomb lattice, highlighting new interesting materials for helium separation for future experimental validation.

Threats and Promises: The Promotion of Artificial Lighting to Women Consumers in the United States, 1880s – 1960s

This article examines the development of a specific gendered discourse in the United States in the first half of the twentieth century that united key beliefs about feminine beauty, identity, and the domestic interior with particular electric lighting technologies and effects. Largely driven by the electrical industry’s marketing rhetoric, American women were encouraged to adopt electric lighting as a beauty aid and ally in a host of domestic tasks. Drawing evidence from a number of primary texts, including women’s magazines, lighting and electrical industry trade journals, manufacturer-generated marketing materials, and popular home decoration and beauty advice literature, this study shifts the focus away from lighting as a basic utility, demonstrating the ways in which modern electric illumination was culturally constructed as a desirable personal and environmental beautifier as well as a means of harmonizing the domestic interior.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Thin nanoporous metal-insulator-metal membranes

Insulating nanoporous materials are promising platforms for soft-ionizing membranes; however, improvement in fabrication processes and the quality and high breakdown resistance of the thin insulator layers are needed for high integration and performance. Here, scalable fabrication of highly porous, thin, silicon dioxide membranes with controlled thickness is demonstrated using plasma-enhanced chemical-vapor-deposition. The fabricated membranes exhibit good insulating properties with a breakdown voltage of 1 × 107 V/cm. Our calculations suggest that the average electric field inside a nanopore of the membranes can be as high as 1 × 106 V/cm; sufficient for ionization of wide range of molecules. These metal–insulator–metal nanoporous arrays are promising for applications such soft ionizing membranes for mass spectroscopy.

The Edge of Danger: artificial lighting and the dialectics of domestic occupation in Philip Johnson's Glass and Guest Houses

In the first half of the twentieth century the dematerializing of boundaries between enclosure and exposure problematized traditional acts of “occupation” and understandings of the domestic environment. As a space of escalating technological control, the modern domestic interior offered new potential to re-define the meaning and means of habitation. This shift is clearly expressed in the transformation of electric lighting technology and applications for the modern interior in the mid-twentieth century. Addressing these issues, this paper examines the critical role of electric lighting in regulating and framing both the public and private occupation of Philip Johnson’s New Canaan estate. Exploring the dialectically paired transparent Glass House and opaque Guest House (both 1949), this study illustrates how Johnson employed artificial light to control the visual environment of the estate as well as to aestheticize the performance of domestic space. Looking closely at the use of artificial light to create emotive effects as well as to intensify the experience of occupation, this revisiting of the iconic Glass House and lesser-known Guest House provides a more complex understanding of Johnson’s work and the means with which he inhabited his own architecture. Calling attention to the importance of Johnson serving as both architect and client, and his particular interest in exploring the new potential of architectural lighting in this period, this paper investigates Johnson’s use of electric light to support architectural narratives, maintain visual order and control, and to suit the nuanced desires of domestic occupation.