Refractive indices of layers and optical simulations of Cu(In,Ga)Se2 solar cells

Cu(In,Ga)Se₂-based solar cells have reached efficiencies close to 23%. Further knowledge-driven improvements require accurate determination of the material properties. Here, we present refractive indices for all layers in Cu(In,Ga)Se₂ solar cells with high efficiency. The optical bandgap of Cu(In,Ga)Se₂ does not depend on the Cu content in the explored composition range, while the absorption coefficient value is primarily determined by the Cu content. An expression for the absorption spectrum is proposed, with Ga and Cu compositions as parameters. This set of parameters allows accurate device simulations to understand remaining absorption and carrier collection losses and develop strategies to improve performances.     

On fluidization of discrete event models: observation and control of continuous Petri nets

As a preliminary overview, this work provides first a broad tutorial on the fluidization of discrete event dynamic models, an efficient technique for dealing with the classical state explosion problem. Even if named as continuous or fluid, the relaxed models obtained are frequently hybrid in a technical sense. Thus, there is plenty of room for using discrete, hybrid and continuous model techniques for logical verification, performance evaluation and control studies. Moreover, the possibilities for transferring concepts and techniques from one modeling paradigm to others are very significant, so there is much space for synergy. As a central modeling paradigm for parallel and synchronized discrete event systems, Petri nets (PNs) are then considered in much more detail. In this sense, this paper is somewhat complementary to David and Alla (2010). Our presentation of fluid views or approximations of PNs has sometimes a flavor of a survey, but also introduces some new ideas or techniques. Among the aspects that distinguish the adopted approach are: the focus on the relationships between discrete and continuous PN models, both for untimed, i.e., fully non-deterministic abstractions, and timed versions; the use of structure theory of (discrete) PNs, algebraic and graph based concepts and results; and the bridge to Automatic Control Theory. After discussing observability and controllability issues, the most technical part in this work, the paper concludes with some remarks and possible directions for future research.     

Fault detection and isolation in robotic manipulators via neural networks: A comparison among three architectures for residual analysis

In this article we discuss artificial neural networks‐based fault detection and isolation (FDI) applications for robotic manipulators. The artificial neural networks (ANNs) are used for both residual generation and residual analysis. A multilayer perceptron (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so‐called residual vector. The residuals, when properly analyzed, provides an indication of the status of the robot (normal or faulty operation). Three ANNs architectures are employed in the residual analysis. The first is a radial basis function network (RBFN) which uses the residuals of position and velocity to perform fault identification. The second is again an RBFN, except that it uses only the velocity residuals. The third is an MLP which also performs fault identification utilizing only the velocity residuals. The MLP is trained with the classical back‐propagation algorithm and the RBFN is trained with a Kohonen self‐organizing map (KSOM). We validate the concepts discussed in a thorough simulation study of a Puma 560 and with experimental results with a 3‐joint planar manipulator. © 2001 John Wiley & Sons, Inc.     

Urinary proteomic analysis of chronic allograft nephropathy

The pathogenesis of progressive renal allograft injury, which is termed chronic allograft nephropathy (CAN), remains obscure and is currently defined by histology. Prospective protocol-biopsy trials have demonstrated that clinical and standard laboratory tests are insufficiently sensitive indicators of the development and progression of CAN. The study aim was to determine if CAN could be characterized by urinary proteomic data and identify the proteins associated with disease. The urinary proteome of 75 renal transplant recipients and 20 healthy volunteers was analyzed using surface enhanced laser desorption and ionization MS. Patients could be classified into subgroups with normal histology and Banff CAN grades 2-3 with a sensitivity of 86% and a specificity of 92% by applying the classification algorithm Adaboost to urinary proteomic data. Several urinary proteins associated with advanced CAN were identified including α1-microglobulin, β2-microglobulin, prealbumin, and endorepellin, the antiangiogenic C-terminal fragment of perlecan. Increased urinary endorepellin was confirmed by ELISA and increased tissue expression of the endorepellin/perlecan ratio by immunofluoresence analysis of renal biopsies. In conclusion, analysis of urinary proteomic data has further characterized the more severe CAN grades and identified urinary endorepellin, as a potential biomarker of advanced CAN.     

A co-evolutionary differential evolution algorithm for solving min–max optimization problems implemented on GPU using C-CUDA

Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of graphics processing units (GPU) and the compute unified device architecture (CUDA) platform. In case of evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of a co-evolutionary differential evolution (DE) algorithm in C-CUDA for solving min–max problems. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced and scalability is improved using C-CUDA. As far as we know, this is the first implementation of a co-evolutionary DE algorithm in C-CUDA.     

Using roadmaps to classify regions of space for autonomous robot navigation

The roadmap approach to robot path planning is one of the earliest methods. Since then, many different algorithms for building roadmaps have been proposed and widely implemented in mobile robots but their use has always been limited to planning in static, totally known environments. In this paper we combine the use of dynamic analogical representations of the environment with an efficient roadmap extraction method, to guide the robot navigation and to classify the different regions of space in which the robot moves. The paper presents the general reference architecture for the robotic system and then focuses on the algorithms for the construction of the roadmap, the classification of the regions of space and their use in robot navigation. Experimental results indicate the applicability and robustness of this approach in real situations.     

DISCRETE WAVELET TRANSFORM FOR DENOISING RAMAN SPECTRA OF HUMAN SKIN TISSUES USED IN A DISCRIMINANT DIAGNOSTIC ALGORITHM

In this work, we applied the discrete wavelet transform (DWT) method as a denoising tool for dispersive Raman spectra of skin samples, and we compared the results obtained with the low-order polynomial fitting in a discriminating model based on principal components analysis (PCA). We used a set of 50 Raman spectra of skin tissue fragments diagnosed as normal (N) (25 spectra) and basocellular cell carcinoma (BCC) (25 spectra). A denoising procedure using DWT and its inverse was employed, and the resulting spectra were compared to denoising using low-order polynomial fitting and adjacent averaging smoothing. The tissue spectral profile showed changes in the intensity of bands below 1400 cm^?1 for DWT compared to the denoising by polynomial and smoothing. By applying PCA and Mahalanobis distance in both groups processed, we verified that the filtering method does not alter significantly the discrimination of N and BCC tissues. However, the DWT denoising presented an interesting result, which showed the main components after decomposition of the Raman signal used in the reconstruction.     

A theoretical evaluation of the effect of water on the electronic properties of low density amorphous carbon nanoparticles

Monte Carlo simulations combined with first-principles calculations were carried out to investigate the structure and electronic properties of amorphous carbon nanoparticles containing between 287 and 467 atoms, isolated and in an aqueous environment. Carbon interactions were described in terms of the semiemperical Tersoff potential and the interactions of the nanoparticles with water were described by an all-atom Lennard–Jones potential. The nanostructures were generated from low mass density amorphous carbon phases simulated under ambient conditions. Our study has indicated that the formation energy of these nanomaterials is slightly affected by the initial configuration of the amorphous phase. The large number of dangling bonds present in the nanostructures leads to huge magnetic moments, in a 31–42 μ_B range, calculated for the amorphous nanostructures containing distinct sp²/sp³ ratios. Also, these nanoparticles exhibit strong interaction energies with the aqueous environment, which are in agreement with the high reactivity expected for these amorphous systems. Most importantly, the calculated magnetism appears to be reduced between 1% and 14% in the presence of water, depending on both the nanoparticle density and surface.     

Anisotropic molecular packing of soluble C60 fullerenes in hexagonal nanocrystals obtained by solvent vapor annealing

We describe the self-assembly of soluble, chemically modified fullerene [6,6]-phenyl C₆₁ butyric acid methyl ester (PCBM) into a new crystalline phase where the C₆₀ moieties are arranged in parallel layers. Minimum C₆₀ center-to-center distance is 10 Å within the layers, and up to 15 Å perpendicular to the layers. Highly anisotropic, mesoscopic hexagonal crystals of this material, with a lateral size of many microns and a thickness below 1 μm, are obtained from chloroform solution by solvent vapor annealing, and characterized by optical microscopy and X-ray diffraction. The crystalline structure is deduced combining experimental data with molecular modeling and ab initio calculations. The large difference in C₆₀–C₆₀ spacing indicates a high anisotropy in electrical and charge transport properties of this new phase.     

Response of Corn Seedlings (Zea mays L.) to Different Concentrations of Nitrogen in Absence and Presence of Silicon

Silicon - Corn plants are highly demanding of nitrogen and the application of silicon has been studied because it minimizes stress from different natures, and for the better utilization of some...