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The paper presents the current state of an on-going research aimed at characterizing the seismic response of Mallorca cathedral. Mallorca cathedral is an audacious Gothic structure built in the island of Mallorca during 14th-16th centuries, characterized for its large dimensions and slender structural members. So far, experimental and numerical modal analysis, in addition to tentative model updating and seismic analysis, have been performed. The dynamic identification tests have been carried out by ambient vibration testing, while the frequency domain decomposition (FDD) technique has been used to obtain the modal parameters. A 3D Finite Element (FE) model has been used to determine the vibration modes. The model has been updated by modifying some structural parameters to improve the matching between experimental and numerical modal parameters. Once updated, the model has been utilized to study the seismic response of the cathedral using non-linear static pushover analysis. Conclusions on the possible collapse mechanisms and the seismic performance of the structure are presented.

Copper concentrations in drinking-water is very important to be monitored which can cause cancer if it exceed about 10 mg/liter. In the present work, we have developed a simple, low laser power method to improve the detection limits of laser induced plasma spectroscopy LIBS for copper in aqueous solutions with different concentrations. In this method a medium density fiberboard (MDF) wood have been used as a substrate that absorbs the liquid sample to transform laser liquid interaction to laser solid interaction. Using the fundamental wavelength of Nd:YAG laser, the constructed plasma emissions were monitored for elemental analysis. The signal-to-noise ratio SNR was optimized using low laser fluence of 32 J cm-2, and detector (CDD camera) gate delay of 0.5 μs. Both the electron temperature and density of the induced plasma were determined using Boltzmann plot and the FWHM of the Cu at 324.7 nm, respectively. The plasma temperature was found to be 1.197 eV, while the plasma density was about 1.66 x 1019 cm-3. The detection limits for Cu at 324.7 nm is found to be 131 ppb comparable to the results by others using complicated system. © 2014 SPIE.

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In this paper, a system for aquarium family fish species identification is proposed. It identifies eight family fish species along with 191 sub-species. The proposed system is built using deep convolutional neural networks (CNN). It consists of four layers, two convolutional and two fully connected layers. A comparative result is presented against other CNN architectures such as AlexNet and VggNet according to four parameters (number of convolution and fully connected layers, the number of epochs in training phase to achieve 100{%} accuracy, validation accuracy, and testing accuracy). Through the paper, it is proven that the proposed system has competitive results against the other architectures. It achieved 85.59{%} testing accuracy while AlexNet achieves 85.41{%} over untrained benchmark dataset. Moreover, the proposed system has less trained images, less memory, less computational complexity in training, validation, and testing phases.