Damage detection in beams by using artificial neural networks and dynamical parameters

In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.

Solvent-Resistant Polymeric Sensors for Adulteration Detection in Liquid Fuels

Polymeric sensors with improved resistance to organic solvents were produced via the layer-by-layer thin film deposition followed by chemical cross-linking. According to UV-vis spectroscopy, the mass loss of polyaniline/poly(vinyl alcohol) and polyaniline/novolac-type resin based films deposited onto glass slides was less than 20% when they were submitted to successive immersions (up to 3,000 immersion cycles) into commercially available ethanol and gasoline fuel samples. Polyallylamine hydrochloride/nickel tetrasulfonated phthalocyanine films presented similar stability. The electrical responses assessed by impedance spectroscopy of films deposited onto Au-interdigitated microelectrodes were relatively unaffected after continuous or cyclic immersions into both fuels. After these studies, an array including these polymeric sensors was employed to detect adulteration in ethanol and gasoline samples. After principal component analysis, it was possible to conclude that the proposed sensor array is capable to discriminate with remarkable reproducibility ethanol samples containing different amounts of water or else gasoline samples containing different amounts of ethanol. In both examples, more than 90% of data variance was retained in the first principal component. For each type of sample, ethanol and gasoline, it was found a linear correlation between one of the principal components and the sample's composition. These findings allow one to conclude that these films present great potential for the development of reliable and low-cost sensors for fuel analysis in liquid phase.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.