VO2 Kinetics During Heavy and Severe Exercise in Swimming

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

Niobium Pentachloride Catalyzed Multicomponent Povarov Reaction

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

Structural integrity identification based on smart materials and neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically>30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, two sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with an experimental example, an investigation on a massive quarter scale model of a steel bridge section, in order to verify the performance of this proposed methodology.

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.

Resposta de cultivares de trigo ao nitrogênio em relação às fontes e épocas de aplicação sob plantio direto e irrigação por aspersão

A adubação nitrogenada é de suma importância para a cultura do trigo, já que o nitrogênio constitui um dos nutrientes mais exigidos por essa cultura e o rendimento desta é função direta da quantidade de nutrientes acumulados pela planta. Foram testados os adubos nitrogenados: sulfonitrato de amônio com inibidor de nitrificação, sulfato de amônio e uréia, na dose de 70 kg de N ha-1; em duas épocas de aplicação, na linha de semeadura ou em cobertura, além da testemunha que não recebeu nitrogênio como tratamento, em quatro cultivares de trigo irrigado: Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) 21, Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) 22, Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) 42 e IAC 370. O experimento foi conduzido sob irrigação por aspersão em dois anos (2005 e 2006) em área experimental pertencente à Faculdade de Engenharia de Ilha Solteira UNESP/São Paulo - Brasil. As fontes de nitrogênio sulfonitrato de amônio com inibidor de nitrificação, sulfato de amônio e uréia não diferiram entre si, porém foram superiores à testemunha, em relação à produtividade de grãos. A aplicação do N todo em cobertura proporcionou aumento na produtividade de grãos. O comportamento das cultivares quanto aos componentes de produção e produtividade foram dependentes do ano em estudo.

Lapachol Induces Clastogenic Effects in Rats

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

Influence of protein intake and muscle mass on survival in chronic dialysis patients

Introduction: Some studies suggest that high body mass index (BMI) confers survival advantage in dialysis patients, but BMI does not differentiate muscle from fat mass, and the survival advantage conferred by its increase seems to be limited to patients with high muscle mass. Thus, discriminating body components when evaluating nutritional status and survival is highly important. This study evaluated the influence of nutritional parameters on survival in patients on chronic dialysis. Subjects and methods: Anthropometry, bioimpedance, biochemistry, and dietary recall were used to investigate the influence of nutritional parameters on survival in 79 prevalent patients on chronic dialysis. Results: Protein intake <1.2 g/kg/day and creatinine <9.7 mg/dL were independent predictors of mortality in all patients. Regarding dialysis method, protein intake <1.2 g/kg/ day was predictive of mortality among hemodialysis patients, and percent standard mid-arm muscle circumference <80% was identified as a risk factor among peritoneal dialysis patients. Conclusion: Higher muscle mass, possibly favored by a higher protein intake, conferred survival advantage in dialysis patients.

Supraphysiological Triiodothyronine Doses Diminish Leptin and Adiponectin Gene Expression, but do not Alter Resistin Expression in Calorie Restricted Obese Rats

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

A Simple Approach to Assess VT During a Field Walk Test

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