A importância das classes socias na relação médico-paciente: adesão ao pré-natal

This article is focused in the understanding of how can social classes influence in prenatal, throughout the patient medical relationship as well as the many aspects surrounding. In the first chapter, reflected about the adherence to prenatal and considerations in gestational period when dealing with public health treatment offer by SUS. Next chapter, patient medical relationship is addressed as a relationship classes, over questioning how this relationship use to be in front of disadvantaged extracts, focused in prenatal. In the third chapter, the patient medical relationship is analyzed throughout the patient vision, pointing the many factors that can induce the success of a therapeutic. In the last chapter, there are reflections about whereby health professionals upgrading, as well as the improve of basic health care networks are necessary to a larger prenatal adherence.

Redes de atenção à saúde para gestantes usuárias de álcool e/ou outras drogas

Pós-graduação em Saúde Coletiva - FMB

O cuidado em saúde mental e o agir em rede: como se agencia um caso de intensa complexidade?

Pós-graduação em Psicologia - FCLAS

Impedance-based structural health monitoring with artificial 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, multiple 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 experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.

Design and implementation of wireless sensor networks for impedance-based structural health monitoring using ZigBee and Global System for Mobile Communications

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.

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.

Gene flow networks among American Aedes aegypti populations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Smart structures health monitoring using artificial neural network

This paper presents a non-model based technique to detect and locate structural damage with the use of artificial neural networks. This method utilizes high frequency structural excitation (typically greater than 30 kHz) through a surface-bonded piezoelectric sensor/actuator to detect changes in structural point impedance due to the presence of damage. Two sets of artificial neural networks were developed in order to detect, locate and characterize structural damage by examining changes in the measured impedance curves. A simulation beam model was developed to verify the proposed method. An experiment was successfully performed in detecting damage on a 4-bay structure with bolted-joints, where the bolts were progressively released.

Leakage detection in water pipe networks using electromechanical impedance (EMI) based technique

The electromechanical impedance (EMI) technique has been successfully used in structural health monitoring (SHM) systems on a wide variety of structures. The basic concept of this technique is to monitor the structural integrity by exciting and sensing a piezoelectric transducer, usually a lead zirconate titanate (PZT) wafer bonded to the structure to be monitored and excited in a suitable frequency range. Because of the piezoelectric effect, there is a relationship between the mechanical impedance of the host structure, which is directly related to its integrity, and the electrical impedance of the PZT transducer, obtained by a ratio between the excitation and the sensing signals.This work presents a study on damage (leaks) detection using EMI based method. Tests were carried out in a rig water system built in a Hydraulic Laboratory for different leaks conditions in a metallic pipeline. Also, it was evaluated the influence of the PZT position bonded to the pipeline. The results show that leaks can effectively be detected using common metrics for damage detection such as RMSD and CCDM. Further, it was observed that the position of the PZT bonded to the pipes is an important variable and has to be controlled.