Close-Packing of Clusters: Application to Al_100

The lowest energy configurations of close-packed clusters up to N=110 atoms with stacking faults are studied using the Monte Carlo method with Metropolis algorithm. Two types of contact interactions, a pair-potential and a many-atom interaction, are used. Enhanced stability is shown for N=12, 26, 38, 50, 59, 61, 68, 75, 79, 86, 100 and 102, of which only the sizes 38, 75, 79, 86, and 102 are pure FCC clusters, the others having stacking faults. A connection between the model potential and density functional calculations is studied in the case of Al_100. The density functional calculations are consistent with the experimental fact that there exist epitaxially grown FCC clusters starting from relatively small cluster sizes. Calculations also show that several other close-packed motifs existwith comparable total energies.Comment: 9 pages, 7 figure

Strain Effects on Point Defects and Chain-Oxygen Order-Disorder Transition in 123-Structure Cuprate Superconductors

The energetics of Schottky defects in 123 cuprate superconductor series, $\rm REBa_2Cu_3O_7$ (where RE = lanthandies) and $\rm YAE_2Cu_3O_7$ (AE = alkali-earths), were found to have unusual relations if one considers only the volumetric strain. Our calculations reveal the effect of non-uniform changes of interatomic distances within the RE-123 structures, introduced by doping homovalent elements, on the Schottky defect formation energy. The energy of formation of Frenkel Pair defects, which is an elementary disordering event, in 123 compounds can be substantially altered under both stress and chemical doping. Scaling the oxygen-oxygen short-range repulsive parameter using the calculated formation energy of Frenkel pair defects, the transition temperature between orthorhombic and tetragonal phases is computed by quasi-chemical approximations (QCA). The theoretical results illustrate the same trend as the experimental measurements in that the larger the ionic radius of RE, the lower the orthorhombic/tetragonal phase transition temperature. This study provides strong evidence of the strain effects on order-disorder transition due to oxygens in the CuO chain sites.Comment: In print Phys Rev B (2004

Determining collagen distribution in articular cartilage using contrast-enhanced micro-computed tomography

Objective: Collagen distribution within articular cartilage (AC) is typically evaluated from histological sections, e.g., using collagen staining and light microscopy (LM). Unfortunately, all techniques based on histological sections are time-consuming, destructive, and without extraordinary effort, limited to two dimensions. This study investigates whether phosphotungstic acid (PTA) and phosphomolybdic acid (PMA), two collagen-specific markers and X-ray absorbers, could (1) produce contrast for AC X-ray imaging or (2) be used to detect collagen distribution within AC. Method: We labeled equine AC samples with PTA or PMA and imaged them with micro-computed tomography (micro-CT) at pre-defined time points 0, 18, 36, 54, 72, 90, 180, 270 h during staining. The micro-CT image intensity was compared with collagen distributions obtained with a reference technique, i.e., Fourier-transform infrared imaging (FTIRI). The labeling time and contrast agent producing highest association (Pearson correlation, BlandeAltman analysis) between FTIRI collagen distribution and micro-CT -determined PTA distribution was selected for human AC. Results: Both, PTA and PMA labeling permitted visualization of AC features using micro-CT in non-calcified cartilage. After labeling the samples for 36 h in PTA, the spatial distribution of X-ray attenuation correlated highly with the collagen distribution determined by FTIRI in both equine (mean +/- S.D. of the Pearson correlation coefficients, r = 0.96 +/- 0.03, n = 12) and human AC (r = 0.82 +/- 0.15, n = 4). Conclusions: PTA-induced X-ray attenuation is a potential marker for non-destructive detection of AC collagen distributions in 3D. This approach opens new possibilities in development of non-destructive 3D histopathological techniques for characterization of OA. (C) 2015 The Authors. Published by Elsevier Ltd and Osteoarthritis Research Society International.Peer reviewe

Dictionary learning for fast classification based on soft-thresholding.

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in scenarios where computational power is restricted. We consider in this paper a simple yet efficient alternative to sparse coding for feature extraction. We study a classification scheme that applies the soft-thresholding nonlinear mapping in a dictionary, followed by a linear classifier. A novel supervised dictionary learning algorithm tailored for this low complexity classification architecture is proposed. The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver. We conduct experiments on several datasets, and show that our learning algorithm that leverages the structure of the classification problem outperforms generic learning procedures. Our simple classifier based on soft-thresholding also competes with the recent sparse coding classifiers, when the dictionary is learned appropriately. The adopted classification scheme further requires less computational time at the testing stage, compared to other classifiers. The proposed scheme shows the potential of the adequately trained soft-thresholding mapping for classification and paves the way towards the development of very efficient classification methods for vision problems

Growth of nanostructures by cluster deposition : a review

This paper presents a comprehensive analysis of simple models useful to analyze the growth of nanostructures obtained by cluster deposition. After detailing the potential interest of nanostructures, I extensively study the first stages of growth (the submonolayer regime) by kinetic Monte-Carlo simulations. These simulations are performed in a wide variety of experimental situations : complete condensation, growth with reevaporation, nucleation on defects, total or null cluster-cluster coalescence... The main scope of the paper is to help experimentalists analyzing their data to deduce which of those processes are important and to quantify them. A software including all these simulation programs is available at no cost on request to the author. I carefully discuss experiments of growth from cluster beams and show how the mobility of the clusters on the surface can be measured : surprisingly high values are found. An important issue for future technological applications of cluster deposition is the relation between the size of the incident clusters and the size of the islands obtained on the substrate. An approximate formula which gives the ratio of the two sizes as a function of the melting temperature of the material deposited is given. Finally, I study the atomic mechanisms which can explain the diffusion of the clusters on a substrate and the result of their mutual interaction (simple juxtaposition, partial or total coalescence...)Comment: To be published Rev Mod Phys, Oct 99, RevTeX, 37 figure

Identification and diagnosis of a photovoltaic module based on outdoor measurements

Photovoltaic modules may be subject to significant ageing during their lifetime. This is evident especially through the value of the series resistance, which is one of the five parameters appearing in the single diode model of the module. The identification of the series resistance value drift on the basis of on-field measurements is not trivial, because of the measurement noise and limitations in reaching the open circuit voltage conditions. In this paper, an approach to the identification of the operating parameters of the module through outdoor measurements is proposed. The method shows interesting features in view of its application to detecting module ageing phenomena during its lifetime

Quantifying Complex Micro-Topography of Degenerated Articular Cartilage Surface by Contrast-Enhanced Micro-Computed Tomography and Parametric Analyses

One of the earliest changes in osteoarthritis (OA) is a surface discontinuity of the articular cartilage (AC), and these surface changes become gradually more complex with OA progression. We recently developed a contrast enhanced micro-computed tomography (mu CT) method for visualizing AC surface in detail. The present study aims to introduce a mu CT analysis technique to parameterize these complex AC surface features and to demonstrate the feasibility of using these parameters to quantify degenerated AC surface. Osteochondral plugs (n = 35) extracted from 19 patients undergoing joint surgery were stained with phosphotungstic acid and imaged using mu CT. The surface micro-topography of AC was analyzed with developed method. Standard root mean square roughness (R-q) was calculated as a reference, and the Area Under Curve (AUC) for receiver operating characteristic analysis was used to compare the acquired quantitative parameters with semi-quantitative visual grading of mu CT image stacks. The parameters quantifying the complex micro-topography of AC surface exhibited good sensitivity and specificity in identifying surface continuity (AUC: 0.93, [0.80 0.99]), fissures (AUC: 0.94, [0.83 0.99]) and fibrillation (AUC: 0.98, [0.88 1.0]). Standard R-q was significantly smaller compared with the complex roughness (CRq) already with mild surface changes with all surface reference parameters - continuity, fibrillation, and fissure sum. Furthermore, only CRq showed a significant difference when comparing the intact surface with lowest fissure sum score. These results indicate that the presented method for evaluating complex AC surfaces exhibit potential to identify early OA changes in superficial AC and is dynamic throughout OA progression. (c) 2019 The Authors. Journal of Orthopaedic Research (R) Published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society. Society. 9999:1-12, 2019.Peer reviewe

Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification

The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise