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Recently the authors developed an output-only (or blind) identification technique for the modal identification of structures under ambient/free vibrations. In the present study, this method is extended to incorporate nonstationary unknown inputs-a feature that existing output-only methods are unable to replicate. The method employs a so-called PARAllel FACtor technique to decompose the third-order tensor that is built using the spatial time-frequency matrices of the response signals in order to find the mode shapes and modal coordinates' auto time-frequency distribution (TFD). The proposed method is first verified using a synthetic data set and then used to extract the modal parameters from vibration data recorded during shaking table tests on the International Institute of Earthquake Engineering and Seismology (IIEES) test structure. The accuracy of the blind identification results is assessed through comparisons with a well-established input-output identification method, namely, a well-known combination of the eigensystem realization algorithm and the observer/Kalman filter identification (ERA/OKID) method. Next, rigidities of the connections of the IIEES Test Structure are identified through a finite-element (FE) model updating procedure. Finally, the proposed method is validated through comparisons between responses predicted using the updated FE model and those directly recorded in additional shaking table tests that were not used in model updating studies. [ABSTRACT FROM AUTHOR]

SUMMARY Herein, we propose a method based on the existing second-order blind identification of underdetermined mixtures technique for identifying the modal characteristics-namely, natural frequencies, damping ratio, and real-valued partial mode shapes of all contributing modes-of structures with a limited number of sensors from recorded free/ambient vibration data. In the second-order blind identification approach, second-order statistics of recorded signals are used to recover modal coordinates and mode shapes. Conventional versions of this approach require the number of sensors to be equal to or greater than the number of active modes. In the present study, we first employ a parallel factor technique to decompose the covariance tensor into rank-one tensors so that the partial mode shapes at the recording locations (sensors) can be estimated. The mode shape matrix identified in this manner is not square, which precludes the use of a simple inversion to extract the modal coordinates. As such, the natural frequencies are identified from the recovered modal coordinates' Autocovariances. The damping ratios are extracted using a least-squares technique from modal free vibrations, as they are not directly recoverable because of the inherent smearing produced by windowing processes. Finally, a Bayesian model updating approach is employed to complete the partial mode shapes-that is, to extract the mode shapes at the DOFs without sensors. We use simulated and physical data for verifying and validating this new identification method, and explore optimal sensor distribution in multistory structures for a given (limited) number of sensors. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: The mucous membrane of the maxillary sinus is very sensitive to foreign bodies and infections. Any triggering may lead to mucous membrane thickening (MMT). Residual bone thickness (RBT) is the remaining bone apical to the periodontal defect until the floor of the maxillary sinus acts as a barrier for the periodontal infections to reach the sinus. The aim of our current study was to evaluate the minimal RBT to prevent periodontal infection to reach Schneiderian membrane using cone-beam computed tomography (CBCT)., Materials and Methods: In this descriptive study, 144 maxillary sinus exposure records of 100 patients were collected retrospectively. Patients with minimum one sinus exposure were considered. MMT and RBT were calculated with the CBCT assistance. Statistical analysis was done using Mann-Whitney U-test, Kruskal-Wallis and Chi-square test. ( P < 0.05) was considered as statistically significant., Results: Significant difference ( P < 0.001) was observed in MMT among three RBT groups. Significantly, higher mean MMT was observed with <2 mm and 2-4 mm RBT groups. The prevalence of MMT with >4 mm group is less (7%) compared to <2 mm group and 2-4 mm RBT groups (91.2% and 90.2%, respectively). Furthermore, there is no significant difference in MMT between angular and furcation defects ( P = 0.890)., Conclusion: Probability of MMT was increased if RBT is <4 mm. Early detection and prompt periodontal treatments associated with regenerative procedures can be instituted wherever possible to improve RBT and to reduce MMT. Further, microbiological studies are required to confirm the analysis.

Background: The effect of probiotic bacteria (Lactobacillus acidophilus and L. casei) as safe organisms was examined on fungal growth and aflatoxin gene regulation in Aspergillus parasiticus., Methods: The fungus was cultured in presence of two different concentrations of L. acidophilus and L. casei in MRS broth medium. Mycelia dry weight is indicated as criteria to evaluate fungal growth. Besides, investigation of aflR gene expression by Real Time PCR was performed for analysis of gene regulatory effects in aflatoxin biosynthetic pathway., Results: Both Lactobacillus strongly inhibited fungal growth in the concentrations of 1. 5×10(2), [Formula: see text] . Expression analysis of aflatoxin genes pathway by real time PCR showed inhibitory effect of L. acidophilus and L. casei on expression of aflR gene. The gene expression revealed to be reduced at the approximate rates of 99. 7% and 98% respectively by L. acidopholus and L. casei in concentrations of [Formula: see text] and more., Conclusion: L. acidophilus and L. casei may be used successfully as suitable candidates in controlling of A. parasiticus growth on food and feed as well as reducing of aflatoxin contamination.