Relaxation effects on the negatively charged Mg impurity in zincblende GaN

The electronic structure of Mg impurity in zincblende (c-)GaN is investigated by using the ab initio full potential linear-augmented plane-wave method and the local density-functional approximation. Full geometry optimization calculations, including nearest and next-nearest neighbor displacements, are performed for the impurity in the neutral and negatively charged states. A value of 190 ± 10 meV was obtained for the Franck-Condon shift to the thermal energy, which is in good agreement with that observed in recent low temperature photoluminescence and Hall-effect measurements. We conclude that the nearest and next-nearest neighbors of the Mg impurity replacing Ga in C-GaN undergo outward relaxations which play an important role in the determination of the center acceptor energies.

Analytic perturbation theory for bound states in the transfer matrix spectrum of weakly correlated lattice ferromagnetic spin systems

We consider general d-dimensional lattice ferromagnetic spin systems with nearest neighbor interactions in the high temperature region ('beta' << 1). Each model is characterized by a single site apriori spin distribution taken to be even. We also take the parameter 'alfa' = ('S POT.4') - 3 '(S POT.2') POT.2' > 0, i.e. in the region which we call Gaussian subjugation, where ('S POT.K') denotes the kth moment of the apriori distribution. Associated with the model is a lattice quantum field theory known to contain a particle of asymptotic mass -ln 'beta' and a bound state below the two-particle threshold. We develop a 'beta' analytic perturbation theory for the binding energy of this bound state. As a key ingredient in obtaining our result we show that the Fourier transform of the two-point function is a meromorphic function, with a simple pole, in a suitable complex spectral parameter and the coefficients of its Laurent expansion are analytic in 'beta'.

Energy-level statistics of interacting trapped bosons

It is a well-established fact that statistical properties of energy-level spectra are the most efficient tool to characterize nonintegrable quantum systems. The statistical behavior of different systems such as complex atoms, atomic nuclei, two-dimensional Hamiltonians, quantum billiards, and noninteracting many bosons has been studied. The study of statistical properties and spectral fluctuations in interacting many-boson systems has developed interest in this direction. We are especially interested in weakly interacting trapped bosons in the context of Bose-Einstein condensation (BEC) as the energy spectrum shows a transition from a collective nature to a single-particle nature with an increase in the number of levels. However this has received less attention as it is believed that the system may exhibit Poisson-like fluctuations due to the existence of an external harmonic trap. Here we compute numerically the energy levels of the zero-temperature many-boson systems which are weakly interacting through the van der Waals potential and are confined in the three-dimensional harmonic potential. We study the nearest-neighbor spacing distribution and the spectral rigidity by unfolding the spectrum. It is found that an increase in the number of energy levels for repulsive BEC induces a transition from a Wigner-like form displaying level repulsion to the Poisson distribution for P(s). It does not follow the Gaussian orthogonal ensemble prediction. For repulsive interaction, the lower levels are correlated and manifest level-repulsion. For intermediate levels P(s) shows mixed statistics, which clearly signifies the existence of two energy scales: external trap and interatomic interaction, whereas for very high levels the trapping potential dominates, generating a Poisson distribution. Comparison with mean-field results for lower levels are also presented. For attractive BEC near the critical point we observe the Shnirelman-like peak near s = 0, which signifies the presence of a large number of quasidegenerate states.

Estudio de los factores que condicionan, a escala local, la presencia de Epidendrum rhopalostele (Orchidaceae): Implicaciones para su conservación

El análisis de los factores que determinan el establecimiento y supervivencia de orquídeas epífitas, incluyen: a) las condiciones microambientales de los bosques que las mantienen, b) preferencias por las características de los hospederos donde crecen, c) limitación en la dispersión de semillas, d) interacciones planta-planta, y e) asociaciones micorrízicas para la germinación y resultan esenciales para el desarrollo de estrategias para la conservación y manejo de este grupo de plantas. Este trabajo ha evaluado la importancia de estos factores en Epidendrum rhopalostele, orquídea epífita del bosque de niebla montano, a través de los análisis de los patrones espaciales de los árboles que la portan y de la propia orquídea, a escala de población, estudios de asociación y métodos moleculares. Estos últimos han consistido en el uso de marcadores AFLP para el análisis de la estructura genética de la orquídea y en la secuenciación-clonación de la región ITS para la identificación de los hongos micorrízicos asociados. El objetivo de esta tesis es, por tanto, una mejor comprensión de los factores que condicionan la presencia de orquídeas epífitas en los remanentes de bosque de niebla montano y una evaluación de las implicaciones para la conservación y mantenimiento de sus hábitats y la permanencia de sus poblaciones. El estudio fue realizado en un fragmento de bosque de niebla montano de sucesión secundaria situado al este de la Cordillera Real, en los Andes del sur de Ecuador, a 2250 m.s.n.m y caracterizado por una pendiente marcada, temperatura media anual de 20.8°C y precipitación anual de 2193 mm. En este fragmento se mapearon, identificaron y caracterizaron todos los árboles presentes con DBH > 1 cm y todos los individuos de Epidendrum rhopalostele. Así mismo se tomaron muestras de hoja para obtener ADN de todas las orquídeas registradas y muestras de raíces de individuos con flor de E. rhopalostele, uno por cada forófito, para el análisis filogenético de micorrizas. Análisis espaciales de patrones de puntos basados en la K de Ripley y la distancia al vecino más cercano fueron usados para los árboles, forófitos y la población de E. rhopalostele. Se observó que la distribución espacial de árboles y forófitos de E. rhopalostele no es aleatoria, ya que se ajusta a un proceso agregado de Poisson. De ahí se infiere una limitación en la dispersión de las semillas en el fragmento estudiado y en el establecimiento de la orquídea. El patrón de distribución de la población de E. rhopalostele en el fragmento muestra un agrupamiento a pequeña escala sugiriendo una preferencia por micro-sitios para el establecimiento de la orquídea con un kernel de dispersión de las semillas estimado de 0.4 m. Las características preferentes del micro-sitio como tipos de árboles (Clusia alata y árboles muertos), tolerancia a la sombra, corteza rugosa, distribución en los dos primeros metros sugieren una tendencia a distribuirse en el sotobosque. La existencia de una segregación espacial entre adultos y juveniles sugiere una competencia por recursos limitados condicionada por la preferencia de micro-sitio. La estructura genética de la población de E. rhopalostele analizada a través de Structure y PCoA evidencia la presencia de dos grupos genéticos coexistiendo en el fragmento y en los mismos forófitos, posiblemente por eventos de hibridización entre especies de Epidendrum simpátricas. Los resultados del análisis de autocorrelación espacial efectuados en GenAlex confirman una estructura genético-espacial a pequeña escala que es compatible con un mecanismo de dispersión de semillas a corta distancia ocasionada por gravedad o pequeñas escorrentías, frente a la dispersión a larga distancia promovida por el viento generalmente atribuida a las orquídeas. Para la identificación de los micobiontes se amplificó la región ITS1-5.8S-ITS2, y 47 secuencias fueron usadas para el análisis filogenético basado en neighborjoining, análisis bayesiano y máximum-likelihood que determinó que Epidendrum rhopalostele establece asociaciones micorrízicas con al menos dos especies diferentes de Tulasnella. Se registraron plantas que estaban asociadas con los dos clados de hongos encontrados, sugiriendo ausencia de limitación en la distribución del hongo. Con relación a las implicaciones para la conservación in situ resultado de este trabajo se recomienda la preservación de todo el fragmento de bosque así como de las interacciones existentes (polinizadores, micorrizas) a fin de conservar la diversidad genética de esta orquídea epífita. Si fuere necesaria una reintroducción se deben contemplar distancias entre los individuos en cada forófito dentro de un rango de 0.4 m. Para promover el reclutamiento y regeneración de E. rhopalostele, se recomienda que los forófitos correspondan preferentemente a árboles muertos o caídos y a especies, como Clusia alata, que posean además corteza rugosa, sean tolerantes a la sombra, y en el área del sotobosque con menor luminosidad. Además es conveniente que las orquídeas en su distribución vertical estén ubicadas en los primeros metros. En conclusión, la limitación en la dispersión, las características del micro-sitio, las interacciones intraespecíficas y con especies congenéricas simpátricas y las preferencias micorrízicas condicionan la presencia de esta orquídea epífita en este tipo de bosque. ABSTRACT The analysis of factors that determine the establishment and survival of epiphytic depends on factors such as a) microenvironmental conditions of forest, b) preference for host characteristics where orchids grow, c) seed dispersal limitation, d) plant-plant interaction, e) priority mycorrhizal associations for germination, are essential for the development of strategies for management and conservation. This work evaluated the importance of these factors in Epidendrum rhopalostele, an epiphytic orchid of montane cloud forest through the analysis of spatial patterns of host trees and the orchid, in a more specific scale, with association studies and molecular methods, including AFLPs for orchid population genetic structure and the sequencing of the ITS region for associated mycorrhizal fungi. The aim of this thesis is to understand the factors that condition the presence of epiphytic orchids in the remnants of montane cloud forest and to assess the implications for the conservation and preservation of their habitats and the persistence of the orchid populations. The study was carried out in a fragment of montane cloud forest of secondary succession on the eastern slope of Cordillera Real in the Andes of southern Ecuador, located at 2250 m a.s.l. characterized by a steep slope, mean annual temperature of 20.8°C and annual precipitation of 2193 mm. All trees with DBH > 1 cm were mapped, characterized and identified. All E. rhopalostele individuals present were counted, marked, characterized and mapped. Leaf samples of all orchid individuals were collected for DNA analysis. Root samples of flowering E. rhopalostele individuals were collected for phylogenetic analysis of mycorrhizae, one per phorophyte. Spatial point pattern analysis based on Ripley`s K function and nearest neighbor function was used for trees, phorophytes and orchid population. We observed that spatial distribution of trees and phorophytes is not random, as it adjusts to a Poisson cluster process. This suggests a limitation for seed dispersal in the study fragment that is affecting orchid establishment. Furthermore, the small-scale spatial pattern of E. rhopalostele evidences a clustering that suggests a microsite preference for orchid establishment with a dispersal kernel of 0.4 m. Microsite features such as types of trees (dead trees or Clusia alata), shade tolerance trees, rough bark, distribution in the first meters suggest a tendency to prefer the understory for their establishment. Regarding plant-plant interaction a spatial segregation between adults and juveniles was present suggesting competition for limited resources conditioned for a microsite preference. Analysis of genetic structure of E. rhopalostele population through Structure and PCoA shows two genetic groups coexisting in this fragment and in the same phorophyte, possibly as a result of hybridization between sympatric species of Epidendrum. Our results of spatial autocorrelation analysis develop in GenAlex confirm a small-scale spatial-genetic structure within the genetic groups that is compatible with a short-distance dispersal mechanism caused by gravity or water run-off, instead of the long-distance seed dispersal promoted by wind generally attributed to orchids. For mycobionts identification ITS1-5.8S-ITS2 rDNA region was amplified. Phylogenetic analysis was performed with neighborjoining, Bayesian likelihood and maximum-likelihood for 47 sequences yielded two Tulasnella clades. This orchid establishes mycorrhizal associations with at least two different Tulasnella species. In some cases both fungi clades were present in same root, suggesting no limitation in fungal distribution. Concerning the implications for in situ conservation resulting from this work, the preservation of all forest fragment and their interactions (pollinators, mycorrhiza) is recommended to conserve the genetic diversity of this species. If a reintroduction were necessary, distances between individuals in each phorophyte within a range of 0.4 m, are recommended. To promote recruitment and regeneration of E. rhopalostele it is recommended that phorophytes correspond to dead or fallen trees or species, such as Clusia alata. Trees that have rough bark and are shade tolerant are also recommended. Furthermore, regarding vertical distribution, it is also convenient that orchids are located in the first meter (in understory, area with less light). In conclusion, limitation on seed dispersal, microsite characteristics, plant-plant interactions or interaction with cogeneric sympatric species and mycorrhizal preferences conditioned the presence of this epiphytic orchid in this fragment forest.

Surface k-NN query processing

A k-NN query finds the k nearest-neighbors of a given point from a point database. When it is sufficient to measure object distance using the Euclidian distance, the key to efficient k-NN query processing is to fetch and check the distances of a minimum number of points from the database. For many applications, such as vehicle movement along road networks or rover and animal movement along terrain surfaces, the distance is only meaningful when it is along a valid movement path. For this type of k-NN queries, the focus of efficient query processing is to minimize the cost of computing distances using the environment data (such as the road network data and the terrain data), which can be several orders of magnitude larger than that of the point data. Efficient processing of k-NN queries based on the Euclidian distance or the road network distance has been investigated extensively in the past. In this paper, we investigate the problem of surface k-NN query processing, where the distance is calculated from the shortest path along a terrain surface. This problem is very challenging, as the terrain data can be very large and the computational cost of finding shortest paths is very high. We propose an efficient solution based on multiresolution terrain models. Our approach eliminates the need of costly process of finding shortest paths by ranking objects using estimated lower and upper bounds of distance on multiresolution terrain models.

Optimization of PCB component placements for the collect-and-place machines

This paper presents two hybrid genetic algorithms (HGAs) to optimize the component placement operation for the collect-and-place machines in printed circuit board (PCB) assembly. The component placement problem is to optimize (i) the assignment of components to a movable revolver head or assembly tour, (ii) the sequence of component placements on a stationary PCB in each tour, and (iii) the arrangement of component types to stationary feeders simultaneously. The objective of the problem is to minimize the total traveling time spent by the revolver head for assembling all components on the PCB. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method, the nearest neighbor heuristic, and the neighborhood frequency heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different population sizes. It is proved that the performance of HGA2 is superior to HGA1 in terms of the total assembly time.

Procesos hopping a través del modelo difusional en materiales nanocristalinos usados para aplicaciones fotovoltaicas

Se presentan los modelos de hopping de rango variable (variable range hopping; VRH), vecinos cercanos (nearest neighbor hopping; NNH) y barreras de potencial presentes en las fronteras de grano; como mecanismos de transporte eléctrico predominantes en los materiales semiconductores para aplicaciones fotovoltaicas. Las medidas de conductividad a oscuras en función de temperatura fueron realizadas para región de bajas temperaturas entre 120 y 400 K con Si y compuestos Cu3BiS2 y Cu2ZnSnSe4. Siguiendo la teoría de percolación, se obtuvieron parámetros hopping y la densidad de estados cerca del nivel de Fermi, N(EF), para todas las muestras. A partir de los planteamientos dados por Mott para VRH, se presentó el modelo difusional, que permitió establecer la relación entre la conductividad y la densidad de estados de defecto o estados localizados en el gap del material. El análisis comparativo entre modelos, evidenció, que es posible obtener mejora hasta de un orden de magnitud en valores para cada uno de los parámetros hopping que caracterizan el material.

Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics

The molecular and metal profile fingerprints were obtained from a complex substance, Atractylis chinensis DC—a traditional Chinese medicine (TCM), with the use of the high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) techniques. This substance was used in this work as an example of a complex biological material, which has found application as a TCM. Such TCM samples are traditionally processed by the Bran, Cut, Fried and Swill methods, and were collected from five provinces in China. The data matrices obtained from the two types of analysis produced two principal component biplots, which showed that the HPLC fingerprint data were discriminated on the basis of the methods for processing the raw TCM, while the metal analysis grouped according to the geographical origin. When the two data matrices were combined into a one two-way matrix, the resulting biplot showed a clear separation on the basis of the HPLC fingerprints. Importantly, within each different grouping the objects separated according to their geographical origin, and they ranked approximately in the same order in each group. This result suggested that by using such an approach, it is possible to derive improved characterisation of the complex TCM materials on the basis of the two kinds of analytical data. In addition, two supervised pattern recognition methods, K-nearest neighbors (KNNs) method, and linear discriminant analysis (LDA), were successfully applied to the individual data matrices—thus, supporting the PCA approach.

Performance evaluation of an adaptive travel time prediction model

This paper presents a travel time prediction model and evaluates its performance and transferability. Advanced Travelers Information Systems (ATIS) are gaining more and more importance, increasing the need for accurate, timely and useful information to the travelers. Travel time information quantifies the traffic condition in an easy to understand way for the users. The proposed travel time prediction model is based on an efficient use of nearest neighbor search. The model is calibrated for optimal performance using Genetic Algorithms. Results indicate better performance by using the proposed model than the presently used naïve model.

Cell invasion with proliferation mechanisms motivated by time-lapse data

Cell invasion involves a population of cells which are motile and proliferative. Traditional discrete models of proliferation involve agents depositing daughter agents on nearest- neighbor lattice sites. Motivated by time-lapse images of cell invasion, we propose and analyze two new discrete proliferation models in the context of an exclusion process with an undirected motility mechanism. These discrete models are related to a family of reaction- diffusion equations and can be used to make predictions over a range of scales appropriate for interpreting experimental data. The new proliferation mechanisms are biologically relevant and mathematically convenient as the continuum-discrete relationship is more robust for the new proliferation mechanisms relative to traditional approaches.

Graph rigidity for near-coplanar structure from motion

Recent algorithms for monocular motion capture (MoCap) estimate weak-perspective camera matrices between images using a small subset of approximately-rigid points on the human body (i.e. the torso and hip). A problem with this approach, however, is that these points are often close to coplanar, causing canonical linear factorisation algorithms for rigid structure from motion (SFM) to become extremely sensitive to noise. In this paper, we propose an alternative solution to weak-perspective SFM based on a convex relaxation of graph rigidity. We demonstrate the success of our algorithm on both synthetic and real world data, allowing for much improved solutions to marker less MoCap problems on human bodies. Finally, we propose an approach to solve the two-fold ambiguity over bone direction using a k-nearest neighbour kernel density estimator.

Scene invariant multi camera crowd counting

Automated crowd counting has become an active field of computer vision research in recent years. Existing approaches are scene-specific, as they are designed to operate in the single camera viewpoint that was used to train the system. Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap. This paper proposes a novel scene invariant crowd counting algorithm that is designed to operate across multiple cameras. The approach uses camera calibration to normalise features between viewpoints and to compensate for regions of overlap. This compensation is performed by constructing an 'overlap map' which provides a measure of how much an object at one location is visible within other viewpoints. An investigation into the suitability of various feature types and regression models for scene invariant crowd counting is also conducted. The features investigated include object size, shape, edges and keypoints. The regression models evaluated include neural networks, K-nearest neighbours, linear and Gaussian process regresion. Our experiments demonstrate that accurate crowd counting was achieved across seven benchmark datasets, with optimal performance observed when all features were used and when Gaussian process regression was used. The combination of scene invariance and multi camera crowd counting is evaluated by training the system on footage obtained from the QUT camera network and testing it on three cameras from the PETS 2009 database. Highly accurate crowd counting was observed with a mean relative error of less than 10%. Our approach enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system.

Using image quality metrics to detect smoke-affected laser data

Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.

Collective motion of dimers

We consider a discrete agent-based model on a one-dimensional lattice and a two-dimensional square lattice, where each agent is a dimer occupying two sites. Agents move by vacating one occupied site in favor of a nearest-neighbor site and obey either a strict simple exclusion rule or a weaker constraint that permits partial overlaps between dimers. Using indicator variables and careful probability arguments, a discrete-time master equation for these processes is derived systematically within a mean-field approximation. In the continuum limit, nonlinear diffusion equations that describe the average agent occupancy of the dimer population are obtained. In addition, we show that multiple species of interacting subpopulations give rise to advection-diffusion equations. Averaged discrete simulation data compares very well with the solution to the continuum partial differential equation models. Since many cell types are elongated rather than circular, this work offers insight into population-level behavior of collective cellular motion.