ALBERT

Description: The interest of microelectronics industry in new organic compounds for the manufacture of luminescent devices has increased substantially in the last decade. In this paper, we carried out a study of the usage feasibility of three organic bidentate ligands (2,6-dihydroxyanthraquinone, anthraflavic acid and potassium derivative salt of anthraflavic acid) for the synthesis of an organic semiconductor based in silicon phthalocyanines (SiPcs). We report the visible photoluminescence (PL) at room temperature obtained from thermal-evaporated thin films of these new materials. The surface morphology of these films was analyzed by atomic force microscopy (AFM) and scanning electron microscopy (SEM). AFM indicated that the thermal evaporation technique is an excellent resource in order to obtain low thin film roughness when depositing these kinds of compounds. Fourier transform infrared spectroscopy (FTIR) spectroscopy was employed to investigate possible changes in the intra-molecular bonds and to identify any evidence of crystallinity in the powder compounds and in the thin films after their deposition. FTIR showed that there was not any important change in the samples after the thermal deposition. The absorption coefficient (α) in the absorption region reveals non-direct transitions. Furthermore, the PL of all the investigated samples were observed with the naked eye in a bright background and also measured by a spectrofluorometer. The normalized PL spectra showed a Stokes shift ≈ 0.6 eV in two of our three samples, and no PL emission in the last one. Those results indicate that the Vis PL comes from a recombination of charge carriers between conduction band and valence band preceded by a non-radiative relaxation in the conduction band tails.

Description: This paper deals with the application of Unmanned Aerial Vehicles (UAV) techniques and high resolution photogrammetry to study the evolution of a landslide affecting olive groves. The last decade has seen an extensive use of UAV, a technology in clear progression in many environmental applications like landslide research. The methodology starts with the execution of UAV flights to acquire very high resolution images, which are oriented and georeferenced by means of aerial triangulation, bundle block adjustment and Structure from Motion (SfM) techniques, using ground control points (GCPs) as well as points transferred between flights. After Digital Surface Models (DSMs) and orthophotographs were obtained, both differential models and displacements at DSM check points between campaigns were calculated. Vertical and horizontal displacements in the range of a few decimeters to several meters were respectively measured. Finally, as the landslide occurred in an olive grove which presents a regular pattern, a semi-automatic approach to identifying and determining horizontal displacements between olive tree centroids was also developed. In conclusion, the study shows that landslide monitoring can be carried out with the required accuracy—in the order of 0.10 to 0.15 m—by means of the combination of non-invasive techniques such as UAV, photogrammetry and geographic information system (GIS).

Description: Entropy, Vol. 20, Pages 35: Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer’s Disease and Mild Cognitive Impairment Entropy doi: 10.3390/e20010035 Authors: Saúl Ruiz-Gómez Carlos Gómez Jesús Poza Gonzalo Gutiérrez-Tobal Miguel Tola-Arribas Mónica Cano Roberto Hornero The discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.

Description: Algorithms, Vol. 10, Pages 137: Weakly Coupled Distributed Calculation of Lyapunov Exponents for Non-Linear Dynamical Systems Algorithms doi: 10.3390/a10040137 Authors: Jorge Hernández-Gómez Carlos Couder-Castañeda Israel Herrera-Díaz Norberto Flores-Guzmán Enrique Gómez-Cruz Numerical estimation of Lyapunov exponents in non-linear dynamical systems results in a very high computational cost. This is due to the large-scale computational cost of several Runge–Kutta problems that need to be calculated. In this work we introduce a parallel implementation based on MPI (Message Passing Interface) for the calculation of the Lyapunov exponents for a multidimensional dynamical system, considering a weakly coupled algorithm. Since we work on an academic high-latency cluster interconnected with a gigabit switch, the design has to be oriented to reduce the number of messages required. With the design introduced in this work, the computing time is drastically reduced, and the obtained performance leads to close to optimal speed-up ratios. The implemented parallelisation allows us to carry out many experiments for the calculation of several Lyapunov exponents with a low-cost cluster. The numerical experiments showed a high scalability, which we showed with up to 68 cores.