One pot auto-ignition based synthesis of novel Sr2CeO4: Ho3+ nanophosphor for photoluminescent applications

Ho3+ (0.25-7 mol%) doped Sr2CeO4 nanophosphors were synthesized by solution combustion method using urea as fuel. The structural properties of the nanophosphors were investigated by powder X-ray diffraction studies (PXRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) techniques. UV-Visible and photoluminescence (PL) spectroscopic techniques were used for analysing the optical properties of the nanoparticles. PXRD and TEM results revealed the formation of Sr2CeO4: Ho3+ nanocrystalline particles with orthorhombic crystal structure. From the UV-Vis studies the optical band gap energy found to decrease from 5.9 to 5.74 eV with increase in dopant concentration. The PL spectra exhibit the broad excitation band from 200 to 400 nm which concurs well with the commercial near UV LED. The PL spectra vary with the dopant content due to energy transfer from the host to the activator. In this present work we demonstrate that color tuning of phosphor can be achieved by merely varying the Ho3+ ions concentration. The CIE and CCT chromaticity coordinates suggests Sr2CeO4: Ho3+ nanophosphors may be potentially applicable as promising single - phased phosphors for lighting applications. (C) 2015 Elsevier B.V. All rights reserved.

High tuning range AlSi RF MEMS capacitors fabricated with sacrificial amorphous silicon surface micromachining

This paper reports the fabrication and electrical characterization of high tuning range AlSi RF MEMS capacitors. We present experimental results obtained by a surface micromachining process that uses dry etching of sacrificial amorphous silicon to release Al-1%Si membranes and has a low thermal budget (<450 °C) being compatible with CMOS post-processing. The proposed silicon sacrificial layer dry etching (SSLDE) process is able to provide very high Si etch rates (3-15 μm/min, depending on process parameters) with high Si: SiO2 selectivity (>10,000:1). Single- and double-air-gap MEMS capacitors, as well as some dedicated test structures needed to calibrate the electro-mechanical parameters and explore the reliability of the proposed technology, have been fabricated with the new process. S-parameter measurements from 100 MHz up to 2 GHz have shown a capacitance tuning range higher than 100% with the double-air-gap architecture. The tuning range can be enlarged with a proper DC electrical bias of the capacitor electrodes. Finally, the reported results make the proposed MEMS tuneable capacitor a good candidate for above-IC integration in communications applications. © 2004 Elsevier B.V. All rights reserved.

Implementação distribuída de auto-cura em redes inteligentes de distribuição de energia elétrica utilizando árvores de extensão mínima.

A propriedade de auto-cura, em redes inteligente de distribuição de energia elétrica, consiste em encontrar uma proposta de reconfiguração do sistema de distribuição com o objetivo de recuperar parcial ou totalmente o fornecimento de energia aos clientes da rede, na ocorrência de uma falha na rede que comprometa o fornecimento. A busca por uma solução satisfatória é um problema combinacional cuja complexidade está ligada ao tamanho da rede. Um método de busca exaustiva se torna um processo muito demorado e muitas vezes computacionalmente inviável. Para superar essa dificuldade, pode-se basear nas técnicas de geração de árvores de extensão mínima do grafo, representando a rede de distribuição. Porém, a maioria dos estudos encontrados nesta área são implementações centralizadas, onde proposta de reconfiguração é obtida por um sistema de supervisão central. Nesta dissertação, propõe-se uma implementação distribuída, onde cada chave da rede colabora na elaboração da proposta de reconfiguração. A solução descentralizada busca uma redução no tempo de reconfiguração da rede em caso de falhas simples ou múltiplas, aumentando assim a inteligência da rede. Para isso, o algoritmo distribuído GHS é utilizado como base na elaboração de uma solução de auto-cura a ser embarcada nos elementos processadores que compõem as chaves de comutação das linhas da rede inteligente de distribuição. A solução proposta é implementada utilizando robôs como unidades de processamento que se comunicam via uma mesma rede, constituindo assim um ambiente de processamento distribuído. Os diferentes estudos de casos testados mostram que, para redes inteligentes de distribuição compostas por um único alimentador, a solução proposta obteve sucesso na reconfiguração da rede, indiferentemente do número de falhas simultâneas. Na implementação proposta, o tempo de reconfiguração da rede não depende do número de linhas nela incluídas. A implementação apresentou resultados de custo de comunicação e tempo dentro dos limites teóricos estabelecidos pelo algoritmo GHS.

1.3μm Si-Based MOEMS Optical Tunable Filter with a Tuning Range of 90nm

Optical filters capable of single control parameter-based wide tuning are implemented and studied. A prototype surface micromachined 1.3μm Si-based MOEMS (micro-opto-electro-mechanical-systems) tunable filter exhibits a continuous and large tuning range of 90 nm at 50 V tuning voltage. The filter can be integrated with Si-based photodetector in a low-cost component for coarse wavelength division multiplexing systems operating in the 1.3μm band.

Integrated structure selection and parameter optimisation for eng-genes neural models

The eng-genes concept involves the use of fundamental known system functions as activation functions in a neural model to create a 'grey-box' neural network. One of the main issues in eng-genes modelling is to produce a parsimonious model given a model construction criterion. The challenges are that (1) the eng-genes model in most cases is a heterogenous network consisting of more than one type of nonlinear basis functions, and each basis function may have different set of parameters to be optimised; (2) the number of hidden nodes has to be chosen based on a model selection criterion. This is a mixed integer hard problem and this paper investigates the use of a forward selection algorithm to optimise both the network structure and the parameters of the system-derived activation functions. Results are included from case studies performed on a simulated continuously stirred tank reactor process, and using actual data from a pH neutralisation plant. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes when compared to conventional neural models. (c) 2007 Elsevier B.V. All rights reserved.

Two-stage RBF network construction based on particle swarm optimization

The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.

La governance degli enti locali sulle aziende di gestione dei servizi pubblici. Strumenti di programmazione e controllo in ottica di gruppo

Lo scopo del presente lavoro è delineare un nuovo modello inerente l'organizzazione, i processi e gli strumenti di programmazione e controllo a supporto della governance degli enti locali sulle loro aziende di gestione dei servizi pubblici, con particolare attenzione per la variabile strumentale. E' stata adottata una metodologia mista, deduttivo-induttiva. Nella fase deduttiva è stata analizzata la normativa italiana nonché la dottrina economico aziendale nazionale ed internazionale in tema di gestione dei servizi pubblici locali: in tal modo è stato estrapolato un modello normativo-dottrinale inerente l'organizzazione, i processi e gli strumenti di programmazione e controllo a supporto della governance degli enti locali sulle loro aziende di gestione dei servizi pubblici. Nella fase induttiva è stata realizzata un'indagine empirica che ha coinvolto i comuni capoluogo di Emilia-Romagna e Toscana, in modo tale da testare il livello di utilizzo del modello normativo-dottrinale precedentemente estrapolato Nella fase di feedback sono stati delineati i punti di forza e di debolezza del succitato modello emergenti dalla ricerca. Si è così cercato di proporre un nuovo modello, con particolare attenzione per la variabile strumentale, in grado di porre rimedio ai punti di debolezza e di potenziare i punti di forza del modello normativo-dottrinale.

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligence

Experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

Modélisation de l'interprétation des pianistes & applications d'auto-encodeurs sur des modèles temporels

Ce mémoire traite d'abord du problème de la modélisation de l'interprétation des pianistes à l'aide de l'apprentissage machine. Il s'occupe ensuite de présenter de nouveaux modèles temporels qui utilisent des auto-encodeurs pour améliorer l'apprentissage de séquences. Dans un premier temps, nous présentons le travail préalablement fait dans le domaine de la modélisation de l'expressivité musicale, notamment les modèles statistiques du professeur Widmer. Nous parlons ensuite de notre ensemble de données, unique au monde, qu'il a été nécessaire de créer pour accomplir notre tâche. Cet ensemble est composé de 13 pianistes différents enregistrés sur le fameux piano Bösendorfer 290SE. Enfin, nous expliquons en détail les résultats de l'apprentissage de réseaux de neurones et de réseaux de neurones récurrents. Ceux-ci sont appliqués sur les données mentionnées pour apprendre les variations expressives propres à un style de musique. Dans un deuxième temps, ce mémoire aborde la découverte de modèles statistiques expérimentaux qui impliquent l'utilisation d'auto-encodeurs sur des réseaux de neurones récurrents. Pour pouvoir tester la limite de leur capacité d'apprentissage, nous utilisons deux ensembles de données artificielles développées à l'Université de Toronto.

Monocouches peptidiques auto-assemblées et applications dans le domaine des biocapteurs de résonance de plasmon de surfaces

Ces travaux visent à étendre les applications de la résonance de plasmons de surface (SPR) L’objectif est d’offrir des outils diagnostics plus rapides, efficaces et simple d’utilisation pour diagnostiquer ou effectuer le suivi de conditions cliniques. Pour se faire, un nouveau type d’instrumentation SPR basé sur l’utilisation d’un prisme d’inversion (dove) a permis d’atteindre une limite de détection (LOD) de 10-6 unité d’indice de réfraction (RIU), une valeur comparable aux instruments commerciaux complexes tout en demeurant peu dispendieux, robuste et simple d’utilisation. Les travaux présentés dans cet ouvrage visent, dans un second temps, à réduire les interactions nonspécifiques (NSB) entre la surface des biocapteurs SPR et les composants de la matrice biologique complexe telles que: l’urine, le lysat cellulaire, le sérum et le sang. Ces dernières induisent des réponses empêchant l’utilisation de biocapteurs SPR en milieux complexes. Les acides aminés (AA) offrent une grande variété de propriétés physico-chimiques permettant la mise au point de monocouches auto-assemblées (SAM) aux propriétés diverses. Initialement, 19 des 20 acides aminés naturels ont été attachés à l’acide 3-mercaptopropionique (3-MPA) formant des SAMs peptidomimétiques. La quantité d’interactions nonspécifiques engendrées par ces différentes surfaces a été mesurée en exposant ces surfaces au sérum sanguin bovin complet variant de 400 ng/cm² jusqu’à 800 ng/cm². La détection à l’aide de ces surfaces de la β-lactamase (une enzyme responsable de la résistance aux antibiotiques au niveau μM) a démontré la possibilité d’employer ces surfaces pour bâtir des biocapteurs SPR. Des peptides de longueur allant de 2 à 5 résidus attachés à 3-MPA ont été synthétisés sur support solide. Cette étude a démontré que l’augmentation de la longueur des peptides formés d’AA résistants aux NBS accroit leur résistance jusqu’à 5 résidus. Le composé le plus performant de ce type (3-MPA-(Ser)5-OH) a permis d’atteindre 180 ng/cm². Cette valeur est similaire à celle des meilleures surfaces disponibles commercialement, notamment les surfaces de polyethylène glycol (PEG) à 100 ng/cm². Des surfaces de 3-MPA-(Ser)5-OH ont permis l’étalonnage de la β-lactamase et sa quantification directe dans un lysat cellulaire. La LOD pour ces biocapteurs est de 10 nM. Une troisième génération de surfaces peptidiques binaires a permis la réduction de la NSB jusqu’à un niveau de 23±10 ng/cm² une valeur comparable aux meilleures surfaces disponibles. Ces surfaces ont permis l’étalonnage d’un indicateur potentiel du cancer la metalloprotéinase-3 de matrice (MMP-3). Les surfaces formées de peptides binaires (3-MPA-H3D2-OH) ont permis la quantification directe de la MMP-3 dans le sérum sanguin complet. Une quatrième génération de surfaces peptidiques a permis de réduire davantage le niveau de NSB jusqu’à une valeur de 12 ± 11 ng/cm². Ces surfaces ont été modifiées en y attachant une terminaison de type acide nitriloacétique (NTA) afin d’y attacher des biomolécules marquées par six résidus histidines terminaux. Ces surfaces ont permis le développement d’une méthode rapide de balayage des ligands ciblant le « cluster of differenciation-36 » (CD36). L’étude d’électroformation des monocouches de peptide a permis de déterminer les conditions de formation optimales d’une couche de 3-MPA-HHHDD-OH permettant ainsi la formation de monocouches résistantes au NSB en moins de 6 minutes en appliquant un potentiel de formation de 200mV vs Ag/AgCl.