Designing sustainable medical devices

Stakeholders in the medical device manufacturing industry are becoming more concerned about the environmental impact of their products and processes. The consumers are also becoming more aware of the negative impact that manufacturers can have on the environment. Government initiatives continue to increase environmental awareness through the development of new policy and legislation, encouraging industry to become more accountable for the environmental impact of their products and operations. The ISO 14001 standard, Environmental Management Systems-Requirements with Guidance for Use, sets guidelines to enable businesses to recognize the environmental effects of their products and processes. Departments can use the tool to set targets to lower the environmental impact and identify areas of high environmental concern when designing, purchasing, and marketing products. Research in these areas will be used to develop the environmental scoring tool to aid in the design of future sustainable medical devices

Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

Attitude determination of the spin-stabilized Project Scanner spacecraft

Attitude determination of spin-stabilized spacecraft using star mapping techniqu

Metallic phase of the quantum Hall effect in four-dimensional space

We study the phase diagram of the quantum Hall effect in four-dimensional (4D) space. Unlike in 2D, in 4D there exists a metallic as well as an insulating phase, depending on the disorder strength. The critical exponent $\nu\approx 1.2$ of the diverging localization length at the quantum Hall insulator-to-metal transition differs from the semiclassical value $\nu=1$ of 4D Anderson transitions in the presence of time-reversal symmetry. Our numerical analysis is based on a mapping of the 4D Hamiltonian onto a 1D dynamical system, providing a route towards the experimental realization of the 4D quantum Hall effect.Comment: 4+epsilon pages, 3 figure

Content Based Image Retrieval by Convolutional Neural Networks

Hamreras S., Benítez-Rochel R., Boucheham B., Molina-Cabello M.A., López-Rubio E. (2019) Content Based Image Retrieval by Convolutional Neural Networks. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer.In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in Content based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic gap between low level and high-level features. Thus, improving retrieval results. Our CNN is the result of a transfer learning technique using Alexnet pretrained network. It learns how to extract representative features from a learning database and then uses this knowledge in query feature extraction. Experimentations performed on Wang (Corel 1K) database show a significant improvement in terms of precision over the state of the art classic approaches.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The background from single electromagnetic subcascades for a stereo system of air Cherenkov telescopes

The MAGIC experiment, a very large Imaging Air Cherenkov Telescope (IACT) with sensitivity to low energy (E < 100 GeV) VHE gamma rays, has been operated since 2004. It has been found that the gamma/hadron separation in IACTs becomes much more difficult below 100 GeV [Albert et al 2008] A system of two large telescopes may eventually be triggered by hadronic events containing Cherenkov light from only one electromagnetic subcascade or two gamma subcascades, which are products of the single pi^0 decay. This is a possible reason for the deterioration of the experiment's sensitivity below 100 GeV. In this paper a system of two MAGIC telescopes working in stereoscopic mode is studied using Monte Carlo simulations. The detected images have similar shapes to that of primary gamma-rays and they have small sizes (mainly below 400 photoelectrons (p.e.)) which correspond to an energy of primary gamma-rays below 100 GeV. The background from single or two electromagnetic subcascdes is concentrated at energies below 200 GeV. Finally the number of background events is compared to the number of VHE gamma-ray excess events from the Crab Nebula. The investigated background survives simple cuts for sizes below 250 p.e. and thus the experiment's sensitivity deteriorates at lower energies.Comment: 15 pages, 7 figures, published in Journ.of Phys.