One dimensional exciton luminescence induced by extended defects in nonpolar (Al,Ga)N/GaN quantum wells

In this study, we present the optical properties of nonpolar GaN/(Al,Ga)N single quantum wells (QWs) grown on either a- or m-plane GaN templates for Al contents set below 15%. In order to reduce the density of extended defects, the templates have been processed using the epitaxial lateral overgrowth technique. As expected for polarization-free heterostructures, the larger the QW width for a given Al content, the narrower the QW emission line. In structures with an Al content set to 5 or 10%, we also observe emission from excitons bound to the intersection of I1-type basal plane stacking faults (BSFs) with the QW. Similarly to what is seen in bulk material, the temperature dependence of BSF-bound QW exciton luminescence reveals intra-BSF localization. A qualitative model evidences the large spatial extension of the wavefunction of these BSF-bound QW excitons, making them extremely sensitive to potential fluctuations located in and away from BSF. Finally, polarization-dependent measurements show a strong emission anisotropy for BSF-bound QW excitons, which is related to their one-dimensional character and that confirms that the intersection between a BSF and a GaN/(Al,Ga)N QW can be described as a quantum wire.

Evaluation of the In desorption during the capped process in diluted nitride In(Ga)As quantum dots

Diluted nitride self-assembled In(Ga)AsN quantum dots (QDs) grown on GaAs substrates are potential candidates to emit in the windows of maximum transmittance for optical fibres (1.3-1.55 μm). In this paper, we analyse the effect of nitrogen addition on the indium desorption occurring during the capping process of InxGa1−xAs QDs (x = l and 0.7). The samples have been grown by molecular beam epitaxy and studied through transmission electron microscopy (TEM) and photoluminescence techniques. The composition distribution inside the dots was determined by statistical moiré analysis and measured by energy dispersive X-ray spectroscopy. First, the addition of nitrogen in In(Ga)As QDs gave rise to a strong redshift in the emission peak, together with a large loss of intensity and monochromaticity. Moreover, these samples showed changes in the QDs morphology as well as an increase in the density of defects. The statistical compositional analysis displayed a normal distribution in InAs QDs with an average In content of 0.7. Nevertheless, the addition of Ga and/or N leads to a bimodal distribution of the Indium content with two separated QD populations. We suggest that the nitrogen incorporation enhances the indium fixation inside the QDs where the indium/gallium ratio plays an important role in this process. The strong redshift observed in the PL should be explained not only by the N incorporation but also by the higher In content inside the QDs

SiGe nanowires grown by LPCVD using Ga-Au catalysts

The use of Ga-Au alloys as metal catalysts for the growth of SiGe nanowires has been investigated. The grown nanowires are cylindrical and straight, with a defect-free crystalline structure, sharp nanowire-droplet interfaces and an almost constant Ge atomic fraction throughout all their length. These features represent significant improvements over the results obtained using pure Au

SiGe/Si nanowire axial heterostructures grown by LPCVD using Ga-Au

The use of Ga-Au alloys of different compositions as metal catalysts for the growth of abrupt SiGe/Si nanowire axial heterostructures has been investigated. The heterostructures grown in a continuous process by just switching the gas precursors, show uniform nanowire diameters, almost abrupt compositional changes and no defects between the different sections. These features represent significant improvements over the results obtained using pure Au.

Effects of Conservation Interventions on the Archaeological Roman Site of Merida (Spain). Advance of Research

Efectos derivados de tratamientos de conservación y restauración sobre material inorgánico en yacimientos arqueológicos. Caso de estudio: Mérida.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Effects of conservation interventions on the archaeological Roman site of Merida (Spain). Advance of research.

Research on the assessment of the effects of conservation/restoration treatments on stone material has been significant in recent years, with focus on the early observation of decay caused by the application of these treatments. However, in the case of archaeological sites, research is still scarce and few studies on the subject have been published. Restoration, as everything else, has changed according to trends, mainly guided by the release of new products and technologies, an experimental field where scientific assessment of suitability, efficacy and durability pre-evaluations of treatments are not always conducted. Some efforts have been made to solve this problem in the architectural field, where functional needs and technical requirements force to set clear standards. Unfortunately, archaeological sites, unlike historic buildings, have specific features that preclude the extrapolation of these results. A critical review of the methodologies, products and restoration materials is necessary, coupled with deeper research on degradation mechanisms caused by these treatments in the mid- and long-term. The aim of this paper is to introduce the research on the above issues using Merida as a case study.

Offline GA-Based optimization for heterogeneous modular multi-configurable chained microrobots

This paper presents a GA-based optimization procedure for bioinspired heterogeneous modular multiconfigurable chained microrobots. When constructing heterogeneous chained modular robots that are composed of several different drive modules, one must select the type and position of the modules that form the chain. One must also develop new locomotion gaits that combine the different drive modules. These are two new features of heterogeneous modular robots that they do not share with homogeneous modular robots. This paper presents an offline control system that allows the development of new configuration schemes and locomotion gaits for these heterogeneous modular multiconfigurable chained microrobots. The offline control system is based on a simulator that is specifically designed for chained modular robots and allows them to develop and learn new locomotion patterns.

Cu(In,Ga)Se2 absorber thinning and the homo-interface model: Influence of Mo back contact and 3-stage process on device characteristics

Thinning the absorber layer is one of the possibilities envisaged to further decrease the production costs of Cu(In,Ga)Se2 (CIGSe) thin films solar cell technology. In the present study, the electronic transport in submicron CIGSe-based devices has been investigated and compared to that of standard devices. It is observed that when the absorber is around 0.5 μm-thick, tunnelling enhanced interface recombination dominates, which harms cells energy conversion efficiency. It is also shown that by varying either the properties of the Mo back contact or the characteristics of 3-stage growth processing, one can shift the dominating recombination mechanism from interface to space charge region and thereby improve the cells efficiency. Discussions on these experimental facts led to the conclusions that 3-stage process implies the formation of a CIGSe/CIGSe homo-interface, whose location as well as properties rule the device operation; its influence is enhanced in submicron CIGSe based solar cells.

Arabidopsis DELLA and two HD-ZIP transcription factors regulate GA signalling in the epidermis through the L1 cis-element

Gibberellins (GAs) are plant hormones that affect plant growth and regulate gene expression differentially across tissues. To study the molecular mechanisms underlying GA signaling in Arabidopsis thaliana, we focused on a GDSL lipase gene (LIP1) induced by GA and repressed by DELLA proteins. LIP1 contains an L1 box promoter sequence, conserved in the promoters of epidermis-specific genes, that is bound by ATML1, an HD-ZIP transcription factor required for epidermis specification. In this study, we demonstrate that LIP1 is specifically expressed in the epidermis and that its L1 box sequence mediates GA-induced transcription. We show that this sequence is overrepresented in the upstream regulatory regions of GA-induced and DELLA-repressed transcriptomes and that blocking GA signaling in the epidermis represses the expression of L1 box–containing genes and negatively affects seed germination. We show that DELLA proteins interact directly with ATML1 and its paralogue PDF2 and that silencing of both HD-ZIP transcription factors inhibits epidermal gene expression and delays germination. Our results indicate that, upon seed imbibition, increased GA levels reduce DELLA protein abundance and release ATML1/PDF2 to activate L1 box gene expression, thus enhancing germination potential.

Demonstration of (In, Ga)N/GaN Core-Shell Micro Light-Emitting Diodes Grown by Molecular Beam Epitaxy on Ordered MOVPE GaN Pillars

This work reports on the growth of (In, Ga)N core−shell micro pillars by plasma-assisted molecular beam epitaxy using an ordered array of GaN cores grown by metal organic vapor phase epitaxy as a template. Upon (In, Ga)N growth, core−shell structures with emission at around 3.0 eV are formed. Further, the fabrication of a core−shell pin structure is demonstrated.

Lattice pulling effect and strain relaxation in axial (In, Ga)N/GaN nanowire heterostructures grown on GaN-buffered Si(111) substrate

Transmission electron microscopy and spatially resolved electron energy-loss spectroscopy have been applied to investigate the indium distribution and the interface morphology in axial (In,Ga)N/GaN nanowire heterostructures. The ordered axial (In,Ga)N/GaN nanowire heterostructures with an indium concentration up to 80% are grown by molecular beam epitaxy on GaN-buffered Si(111) substrates. We observed a pronounced lattice pulling effect in all the nanowire samples given in a broad transition region at the interface. The lattice pulling effect becomes smaller and the (In,Ga)N/GaN interface width is reduced as the indium concentration is increased in the (In,Ga)N section. The result can be interpreted in terms of the increased plastic strain relaxation via the generation of the misfit dislocations at the interface.

Electron tomography of (In,Ga)N insertions in GaN nanocolumns grown on semi-polar (11(2)over-bar2) GaN templates

We present results of scanning transmission electron tomography on GaN/(In,Ga)N/GaN nanocolumns (NCs) that grew uniformly inclined towards the patterned, semi-polar GaN( 112̄ 2 ) substrate surface by molecular beam epitaxy. For the practical realization of the tomographic experiment, the nanocolumn axis has been aligned parallel to the rotation axis of the electron microscope goniometer. The tomographic reconstruction allows for the determination of the three-dimensional indium distribution inside the nanocolumns. This distribution is strongly interrelated with the nanocolumn morphology and faceting. The (In,Ga)N layer thickness and the indium concentration differ between crystallographically equivalent and non-equivalent facets. The largest thickness and the highest indium concentration are found at the nanocolumn apex parallel to the basal planes.