Quantum spin Hall phase in multilayer graphene

The so-called quantum spin Hall phase is a topologically nontrivial insulating phase that is predicted to appear in graphene and graphenelike systems. In this paper we address the question of whether this topological property persists in multilayered systems. We consider two situations: purely multilayer graphene and heterostructures where graphene is encapsulated by trivial insulators with a strong spin-orbit coupling. We use a four-orbital tight-binding model that includes full atomic spin-orbit coupling and we calculate the Z2 topological invariant of the bulk states as well as the edge states of semi-infinite crystals with armchair termination. For homogeneous multilayers we find that even when the spin-orbit interaction opens a gap for all possible stackings, only those with an odd number of layers host gapless edge states while those with an even number of layers are trivial insulators. For heterostructures where graphene is encapsulated by trivial insulators, it turns out that interlayer coupling is able to induce a topological gap whose size is controlled by the spin-orbit coupling of the encapsulating materials, indicating that the quantum spin Hall phase can be induced by proximity to trivial insulators.

A model driven framework for geographic knowledge discovery

Geographic knowledge discovery (GKD) is the process of extracting information and knowledge from massive georeferenced databases. Usually the process is accomplished by two different systems, the Geographic Information Systems (GIS) and the data mining engines. However, the development of those systems is a complex task due to it does not follow a systematic, integrated and standard methodology. To overcome these pitfalls, in this paper, we propose a modeling framework that addresses the development of the different parts of a multilayer GKD process. The main advantages of our framework are that: (i) it reduces the design effort, (ii) it improves quality systems obtained, (iii) it is independent of platforms, (iv) it facilitates the use of data mining techniques on geo-referenced data, and finally, (v) it ameliorates the communication between different users.

Spin separation in digital ferromagnetic heterostructures

In a study of the ferromagnetic phase of a multilayer digital ferromagnetic semiconductor in the mean-field and effective-mass approximations, we find the exchange interaction to have the dominant energy scale of the problem, effectively controlling the spatial distribution of the carrier spins in the digital ferromagnetic heterostructures. In the ferromagnetic phase, the majority-spin and minority-spin carriers tend to be in different regions of the space (spin separation). Hence, the charge distribution of carriers also changes noticeably from the ferromagnetic to the paramagnetic phase. An example of a design to exploit these phenomena is given here.

Caracterización de vidrios para la evaluación del aislamiento acústico a ruido aéreo

La principal aportación del trabajo es la formulación de una alternativa que facilita la determinación experimental del factor de pérdidas y, en consecuencia, mejorar las predicciones de aislamiento a ruido aéreo para vidrios con una o más capas intermedias sea cual fuere su naturaleza. Además, se realiza una revisión de las normativas relacionadas con los ensayos de los parámetros mecánicos necesarios para la caracterizar los vidrios, centrándonos en los monolíticos y los laminados. En efecto, uno de los problemas que se plantea en el contexto de la acústica de la edificación actualmente es el de cumplir con los requisitos de aislamiento acústico a ruido aéreo en fachada que exige el vigente Código Técnico de la Edificación (CTE). Como sabemos, en la fachada podemos distinguir la parte ciega y la parte hueca. La parte más débil en lo concerniente a aislamiento a ruido aéreo es la hueca (compuesta por el vidrio, carpintería y otros elementos). Si la carpintería es la adecuada, la superficie de vidrio se convierte en el elemento limitante. El Catálogo de Elementos Constructivos (CEC) del CTE, la propia norma UNE-EN 12758:2011, así como algunos, cada vez más, fabricantes ofrecen datos del aislamiento de vidrios simples, vidrios laminados y vidrios dobles. En el caso de vidrios laminados, estos datos se limitan únicamente en los que tienen un sola lámina intermedia y, además, no acústicos. Podemos hablar, por tanto, de una laguna de información en este sentido. Para obtener predicciones fiables de aislamiento acústico a ruido aéreo de particiones multicapa, como pueden ser los vidrios laminados es necesario disponer de las características mecánicas y una de las más relevantes es el factor de pérdidas.

An Electronic Reconfigurable Neural Architecture for Intrusion Detection

The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.