Synthesis of titanate nanofibers co-sensitized with ZnS and Bi2S3 nanocrystallites and their application on pollutants removal

The synthesis of nanocomposite materials combining titanate nanofibers (TNF) with nanocrystalline ZnS and Bi2S3 semiconductors is described in this work. The TNF were produced via hydrothermal synthesis and sensitized with the semiconductor nanoparticles, through a single-source precursor decomposition method. ZnS and Bi2S3 nanoparticles were successfully grown onto the TNF's surface and Bi2S3-ZnS/TNF nanocomposite materials with different layouts. The samples' photocatalytic performance was first evaluated through the production of the hydroxyl radical using terephthalic acid as probe molecule. All the tested samples show photocatalytic ability for the production of this oxidizing species. Afterwards, the samples were investigated for the removal of methylene blue. The nanocomposite materials with best adsorption ability were the ZnS/TNF and Bi2S3ZnS/TNF. The dye removal was systematically studied, and the most promising results were obtained considering a sequential combination of an adsorption-photocatalytic degradation process using the Bi2S3ZnS/TNF powder as a highly adsorbent and photocatalyst material. (C) 2015 Elsevier Ltd. All rights reserved.

Cloud-assisted Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for medical and non-medical applications. WBANs consist of a number of miniaturized, portable, and autonomous sensor nodes that are used for long-term health monitoring of patients. These sensor nodes continuously collect information of patients, which are used for ubiquitous health monitoring. In addition, WBANs may be used for managing catastrophic events and increasing the effectiveness and performance of rescue forces. The huge amount of data collected by WBAN nodes demands scalable, on-demand, powerful, and secure storage and processing infrastructure. Cloud computing is expected to play a significant role in achieving the aforementioned objectives. The cloud computing environment links different devices ranging from miniaturized sensor nodes to high-performance supercomputers for delivering people-centric and context-centric services to the individuals and industries. The possible integration of WBANs with cloud computing (WBAN-cloud) will introduce viable and hybrid platform that must be able to process the huge amount of data collected from multiple WBANs. This WBAN-cloud will enable users (including physicians and nurses) to globally access the processing and storage infrastructure at competitive costs. Because WBANs forward useful and life-critical information to the cloud – which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Both the cloud providers and the users must take strong security measures to protect the storage infrastructure.

Toroid inductor development for a SIC DC-DC converter up to 150kW, based on finite element method

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Multiobjective optimization for node adaptation in the analysis of composite plates using a meshless collocation method

The bending of simply supported composite plates is analyzed using a direct collocation meshless numerical method. In order to optimize node distribution the Direct MultiSearch (DMS) for multi-objective optimization method is applied. In addition, the method optimizes the shape parameter in radial basis functions. The optimization algorithm was able to find good solutions for a large variety of nodes distribution.

Multiobjective optimization of viscoelastic laminated sandwich structures using the Direct MultiSearch method

A multiobjective approach for optimization of passive damping for vibration reduction in sandwich structures is presented in this paper. Constrained optimization is conducted for maximization of modal loss factors and minimization of weight of sandwich beams and plates with elastic laminated constraining layers and a viscoelastic core, with layer thickness and material and laminate layer ply orientation angles as design variables. The problem is solved using the Direct MultiSearch (DMS) solver for derivative-free multiobjective optimization and solutions are compared with alternative ones obtained using genetic algorithms.

Design and performance evaluation of turbo FDE receivers

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Estrogen receptor dependent genetic and epigenetic factors of tamoxifen resistance

Resumo: A decisão da terapêutica hormonal no tratamento do cancro da mama baseiase na determinação do receptor de estrogénio alfa por imunohistoquímica (IHC). Contudo, a presença deste receptor não prediz a resposta em todas as situações, em parte devido a limitações do método IHC. Investigámos se a expressão dos genes ESR1 e ESR2, bem como a metilação dos respectivos promotores, pode estar relacionada com a evolução desfavorável de uma proporção de doentes tratados com tamoxifeno assim como com a perda dos receptores de estrogénio alfa (ERα) e beta (ERß). Amostras de 211 doentes com cancro da mama diagnosticado entre 1988 e 2004, fixadas em formalina e preservadas em parafina, foram utilizadas para a determinação por IHC da presença dos receptores ERα e ERß. O mRNA total do gene ESR1 e os níveis específicos do transcrito derivado do promotor C (ESR1_C), bem como dos transcritos ESR2_ß1, ESR2_ß2/cx, and ESR2_ß5 foram avaliados por Real-time PCR. Os promotores A e C do gene ESR1 e os promotores 0K e 0N do gene ESR2 foram investigados por análise de metilação dos dinucleotidos CpG usando bisulfite-PCR para análise com enzimas de restrição, ou para methylation specific PCR. Atendendo aos resultados promissores relacionados com a metilação do promotor do gene ESR1, complementamos o estudo com um método quantitativo por matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) suportado pelo software Epityper para a medição da metilação nos promotores A e C. Fez-se a avaliação da estabilidade do mRNA nas linhas celulares de cancro da mama MCF-7 e MDA-MB-231 tratadas com actinomicina D. Baixos níveis do transcrito ESR1_C associaram-se a uma melhor sobrevivência global (p = 0.017). Níveis elevados do transcrito ESR1_C associaram-se a uma resposta inferior ao tamoxifeno (HR = 2.48; CI 95% 1.24-4.99), um efeito mais pronunciado em doentes com tumores de fenótipo ERα/PgR duplamente positivo (HR = 3.41; CI 95% 1.45-8.04). A isoforma ESR1_C mostrou ter uma semi-vida prolongada, bem como uma estrutura secundária da região 5’UTR muito mais relaxada em comparação com a isoforma ESR1_A. A análise por Western-blot mostrou que ao nível da 21 proteína, a selectividade de promotores é indistinguivel. Não se detectou qualquer correlação entre os níveis das isoformas do gene ESR2 ou entre a metilação dos promotores do gene ESR2, e a detecção da proteína ERß. A metilação do promotor C do gene ESR1, e não do promotor A, foi responsável pela perda do receptor ERα. Estes resultados sugerem que os níveis do transcrito ESR1_C sejam usados como um novo potencial marcador para o prognóstico e predição de resposta ao tratamento com tamoxifeno em doentes com cancro da mama. Abstract: The decision of endocrine breast cancer treatment relies on ERα IHC-based assessment. However, ER positivity does not predict response in all cases in part due to IHC methodological limitations. We investigated whether ESR1 and ESR2 gene expression and respective promoter methylation may be related to non-favorable outcome of a proportion of tamoxifen treated patients as well as to ERα and ERß loss. Formalin-fixed paraffin-embedded breast cancer samples from 211 patients diagnosed between 1988 and 2004 were submitted to IHC-based ERα and ERß protein determination. ESR1 whole mRNA and promoter C specific transcript levels, as well as ESR2_ß1, ESR2_ß2/cx, and ESR2_ß5 transcripts were assessed by real-time PCR. ESR1 promoters A and C, and ESR2 promoters 0N and 0K were investigated by CpG methylation analysis using bisulfite-PCR for restriction analysis, or methylation specific PCR. Due to the promising results related to ESR1 promoter methylation, we have used a quantification method by matrixassisted laser desorption/ionization time-of-flight mass spectrometry (MALDITOF MS) together with Epityper software to measure methylation at promoters A and C. mRNA stability was assessed in actinomycin D treated MCF-7 and MDA-MB-231 cells. ERα protein was quantified using transiently transfected breast cancer cells. Low ESR1_C transcript levels were associated with better overall survival (p = 0.017). High levels of ESR1_C transcript were associated with non-favorable response in tamoxifen treated patients (HR = 2.48; CI 95% 1.24-4.99), an effect that was more pronounced in patients with ERα/PgR double-positive tumors (HR = 3.41; CI 95% 1.45-8.04). The ESR1_C isoform had a prolonged mRNA half-life and a more relaxed 5’UTR structure compared to ESR1_A isoform. Western-blot analysis showed that at protein level, the promoter selectivity is undistinguishable. There was no correlation between levels of ESR2 isoforms or ESR2 promoter methylation and ERß protein staining. ESR1 promoter C CpG methylation and not promoter A was responsible for ERα loss. We propose ESR1_C levels as a putative novel marker for breast cancer prognosis and prediction of tamoxifen response.

Local Fractional Variational Iteration Method for Local Fractional Poisson Equations in Two Independent Variables

The local fractional Poisson equations in two independent variables that appear in mathematical physics involving the local fractional derivatives are investigated in this paper. The approximate solutions with the nondifferentiable functions are obtained by using the local fractional variational iteration method.

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

Road drivers performance appraisal using a MCDA method: a case study in the road transport company

Performance evaluation increasingly assumes a more important role in any organizational environment. In the transport area, the drivers are the company’s image and for this reason it is important to develop and increase their performance and commitment to the company goals. This evaluation can be used to motivate driver to improve their performance and to discover training needs. This work aims to create a performance appraisal evaluation model of the drivers based on the multi-criteria decision aid methodology. The MMASSI (Multicriteria Methodology to Support Selection of Information Systems) methodology was adapted by using a template supporting the evaluation according to the freight transportation company in study. The evaluation process involved all drivers (collaborators being evaluated), their supervisors and the company management. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used.

Parallel hyperspectral compressive sensing method on GPU

Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

Finding a school technological partner: a multicriteria method for school information system producer selection

The choice of an information systems is a critical factor of success in an organization's performance, since, by involving multiple decision-makers, with often conflicting objectives, several alternatives with aggressive marketing, makes it particularly complex by the scope of a consensus. The main objective of this work is to make the analysis and selection of a information system to support the school management, pedagogical and administrative components, using a multicriteria decision aid system – MMASSITI – Multicriteria Method- ology to Support the Selection of Information Systems/Information Technologies – integrates a multicriteria model that seeks to provide a systematic approach in the process of choice of Information Systems, able to produce sustained recommendations concerning the decision scope. Its application to a case study has identi- fied the relevant factors in the selection process of school educational and management information system and get a solution that allows the decision maker’ to compare the quality of the various alternatives.

Mn-II and Cu-II complexes with arylhydrazones of active methylene compounds as effective heterogeneous catalysts for solvent- and additive-free microwave-assisted peroxidative oxidation of alcohols

A one-pot template reaction of sodium 2-(2-(dicyanomethylene) hydrazinyl) benzenesulfonate (NaHL1) with water and manganese(II) acetate tetrahydrate led to the mononuclear complex [Mn(H2O)(6)](HL1a)(2)center dot 4H(2)O (1), where (HL1a) -= 2-(SO3-)C6H4(NH)=N=C(C N) (CONH2) is the carboxamide species derived from nucleophilic attack of water on a cyano group of (HL1) . The copper tetramer [Cu-4(H2O)(10)(-) (1 kappa N: kappa O-2: kappa O, 2 kappa N: k(O)-L-2)(2)]center dot 2H(2)O (2) was obtained from reaction of Cu(NO3)(2)center dot 2.5H(2)O with sodium 5-(2( 4,4-dimethyl-2,6-dioxocyclohexylidene) hydrazinyl)-4-hydroxybenzene-1,3-disulfonate (Na2H2L2). Both complexes were characterized by elemental analysis, IR spectroscopy, ESI-MS and single crystal X-ray diffraction. They exhibit a high catalytic activity for the solvent-and additive-free microwave (MW) assisted oxidation of primary and secondary alcohols with tert-butylhydroperoxide, leading to yields of the oxidized products up to 85.5% and TOFs up to 1.90 x 103 h(-1) after 1 h under low power (5-10 W) MW irradiation. Moreover, the heterogeneous catalysts are easily recovered and reused, at least for three consecutive cycles, maintaining 89% of the initial activity and a high selectivity.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.