Evaluation of curcumin and cisplatin-induced DNA damage in PC12 cells by the alkaline comet assay

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Assessment of DNA damage induced by extracts, fractions and isolated compounds of Davilla nitida and Davilla elliptica (Dilleniaceae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

In vivo assessment of DNA damage and protective effects of extracts from Miconia species using the comet assay and micronucleus test

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Trypanocidal action of eupomatenoid-5 is related to mitochondrion dysfunction and oxidative damage in Trypanosoma cruzi

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Determination of the numerical parameters of a continuous damage model for the structural analysis of clay brick masonry

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

Influence of the grinding wheel's dive speed on the damage of rectified hardened steels

The world tendency is the increase of the productivity and the production of pieces more and more sophisticated, with high degree of geometric and dimensional tolerances, with good surface finish and low cost. Rectification is responsible for the final finish in the machining process of a material. However, damages generated in this production phase affect all the resources used in the previous processes. Great part of the problems happennig in the rectification process is due to the enormous temperature generated in this activity because of the machining conditions. The dive speed, which is directly related to the productivity, is considered responsible for the damages that occur during rectification, limiting its values to those that do not cause such damages. In this work, through the variation of the dive speed in the process of cylindrical grinding of type ABNT D6 steel, rationalizing the application of two cutting fluids and using a CBN (cubic boron nitrate) abrasive wheel with vitrified blond, the influence of the dive speed on the surface damages of hardened steels was evaluated. The results allowed to say that the dive speed, associated to an efficient cooling and lubrication, didn't provoke thermal damages (including heated zones, cracks and tension stresses) to the material. Residual stresses and the roughness of rectified materials presented a correlation with the machining conditions. The work concluded that it is possible to increase the productivity without provoking damages in the rectified components.

Assessment of cumulative damage by using ultrasonic C-scan on carbon fiber/epoxy composites under thermal cycling

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Experimental damage detection grammian matrix

Structural Health Monitoring (SHM) has diverse potential applications, and many groups work in the development of tools and techniques for monitoring structural performance. These systems use arrays of sensors and can be integrated with remote or local computers. There are several different approaches that can be used to obtain information about the existence, location and extension of faults by non destructive tests. In this paper an experimental technique is proposed for damage location based on an observability grammian matrix. The dynamic properties of the structure are identified through experimental data using the eigensystem realization algorithm (ERA). Experimental tests were carried out in a structure through varying the mass of some elements. Output signals were obtained using accelerometers.

Structural FRF acquisition via electric impedance measurement applied to damage location

Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward Variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Damage Detection in Flexible Plate Using Lamb Waves

This paper presents an experimental technique for structural health monitoring (SHM) based on Lamb waves approach in an aluminum plate using piezoelectric material as actuators and sensors. Lamb waves are a form of elastic perturbation that remains guided between two parallel free surfaces, such as the upper and lower surfaces of a plate, beam or shelf. Lamb waves are formed when the actuator excites the surface of the structure with a pulse after receiving a signal. Two PZTs were placed in the plate surface and one of them was used to send a predefined wave through the structure. Thus, the other PZT (adjacent) becomes the sensor. Using this methodology, this paper presents one case of damage detection considering the aluminum plate in the free-free-free-free boundary condition. The damage was simulated by adding additional mass on the plate. It is proposed two damage detection indexes obtained from the experimental signal, involving the Fast Fourier Transform (FFT) and the power spectral density (PSD) that were computed using the output signal. The results show the viability of the presented methodology to damage detection in smart structures

Structural damage identification and location using grammian matrices

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)