Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.

A massive association around the obscured open cluster RSGC3

Context. Four clusters of red supergiants have been discovered in a region of the Milky Way close to base of the Scutum-Crux Arm and the tip of the Long Bar. Population synthesis models indicate that they must be very massive to harbour so many supergiants. If the clusters are physically connected, this Scutum Complex would be the largest and most massive star-forming region ever identified in the Milky Way. Aims. The spatial extent of one of these clusters, RSGC3, has not been investigated. In this paper we explore the possibility that a population of red supergiants could be located in its vicinity. Methods. We utilised 2MASS JHKS photometry to identify candidate obscured luminous red stars in the vicinity of RSGC3. We observed a sample of candidates with the TWIN spectrograph on the 3.5-m telescope at Calar Alto, obtaining intermediate-resolution spectroscopy in the 8000−9000 Å range. We re-evaluated a number of classification criteria proposed in the literature for this spectral range and found that we could use our spectra to derive spectral types and luminosity classes. Results. We measured the radial velocity of five members of RSGC3, finding velocities similar to the average for members of Stephenson 2. Among the candidates observed outside the cluster, our spectra revealed eight M-type supergiants at distances <18′ from the centre of RSGC3, distributed in two clumps. The southern clump is most likely another cluster of red supergiants, with reddening and age identical to RSGC3. From 2MASS photometry, we identified four likely supergiant members of the cluster in addition to the five spectroscopically observed. The northern clump may be a small cluster with similar parameters. Photometric analysis of the area around RSGC3 suggests the presence of a large (>30) population of red supergiants with similar colours. Conclusions. Our data suggest that the massive cluster RSGC3 is surrounded by an extended association, which may be very massive ( ≳ 105 M⊙). We also show that supergiants in the Scutum Complex may be characterised via a combination of 2MASS photometry and intermediate-to-high-resolution spectroscopy in the Z band.

Optical initialization, readout, and dynamics of a Mn spin in a quantum dot

We have investigated the spin preparation efficiency by optical pumping of individual Mn atoms embedded in CdTe/ZnTe quantum dots. Monitoring the time dependence of the intensity of the fluorescence during the resonant optical pumping process in individual quantum dots allows to directly probe the dynamics of the initialization of the Mn spin. This technique presents the convenience of including preparation and readout of the Mn spin in the same step. Our measurements demonstrate that Mn spin initialization, at zero magnetic field, can reach an efficiency of 75% and occurs in the tens of nanoseconds range when a laser resonantly drives at saturation one of the quantum-dot transition. We observe that the efficiency of optical pumping changes from dot-to-dot and is affected by a magnetic field of a few tens of millitesla applied in Voigt or Faraday configuration. This is attributed to the local strain distribution at the Mn location which predominantly determines the dynamics of the Mn spin under weak magnetic field. The spectral distribution of the spin-flip-scattered photons from quantum dots presenting a weak optical pumping efficiency reveals a significant spin relaxation for the exciton split in the exchange field of the Mn spin.

An improved procedure for obtaining and maintaining well characterized partial water saturation states on concrete samples to be used for mass transport tests

A conditioning procedure is proposed allowing to install into the concrete specimens any selected value of water saturation degree with homogeneous moisture distribution. This is achieved within the least time and the minimum alteration of the concrete specimens. The protocol has the following steps: obtaining basic drying data at 50 °C (water absorption capacity and drying curves); unidirectional drying of the specimens at 50 °C until reaching the target saturation degree values; redistribution phase in closed containers at 50 °C (with measurement of the quasi-equilibrium relative humidities); storage into controlled environment chambers until and during mass transport tests, if necessary. A water transport model is used to derive transport parameters of the tested materials from the drying data, i.e., relative permeabilities and apparent water diffusion coefficients. The model also allows calculating moisture profiles during isothermal drying and redistribution phases, thus allowing optimization of the redistribution times for obtaining homogeneous moisture distributions.

Phase equilibria of the water + 1-butanol + toluene ternary system at 101.3 kPa

Isobaric vapour–liquid and vapour–liquid–liquid equilibrium data for the water + 1-butanol + toluene ternary system were measured at 101.3 kPa with a modified VLE 602 Fischer apparatus. In addition, the liquid–liquid equilibrium data at 313.15 K were measured and compared with data from other authors at different temperatures. The system exhibits a ternary heterogeneous azeotrope whose temperature and composition have been determined by interpolation. The thermodynamic consistency of the experimental vapour–liquid and vapour–liquid–liquid data was checked by means of the Wisniak’s Li/Wi consistency test. Moreover, the vapour–liquid and the liquid–liquid equilibrium correlation for the ternary system with NRTL and UNIQUAC models, together with the prediction made with the UNIFAC model, were studied and discussed.

English as the medium of instruction: a response to internationalization

The status of English as a lingua franca has led European universities to implement the use of this language as a medium of instruction (EMI). This study presents an analysis of the status quo of EMI at the University of Alicante. It takes into account the institution`s language policy and the programs which offer subjects in English, as well as the challenges, needs and benefits of the professors and students. Qualitative and quantitative data was collected by means of questionnaires and semi-structured interviews. The findings of this needs analysis will help us to create an action plan that will include teacher and student training schemes to foster internationalization.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.