Impact of Quantization Noise on the Quality of Ultrasonic Signal Deconvolution

In materials characterization, a material’s transfer function is calculated from experimental data. Sometimes, the transfer function of the measurement device, such as an ultrasonic transducer, must be known for this calculation. One of the common methods of obtaining a material transfer function is to measure an elastic waveform before and after it propagates through a material. Then, the transfer function is calculated by deconvolving the two waveforms. We illustrate this process in Fig. 1, where two transducers are placed with a section of plate material between them. The material transfer function is measured by deconvolving the signal at the first transducer from the signal at the second identical transducer. Ideally, this deconvolution would remove the transducer effects. Likewise, if the transducers were placed at the same location (d=0), the transfer function of one transducer could be calculated using a reference transducer

Religion and forgiveness from a Hong Kong Chinese perspective

This study investigated the relationship between religion and forgiveness in a sample of Hong Kong Chinese teachers (n=230) and students (n=714). Findings indicated some influence from Chinese cultural values in the conceptualization of forgiveness. Religious affiliation was the strongest predictor of concepts of forgiveness, whereas religious practice predicted attitudes toward "forgivingness" and the practice of forgiveness. No significant difference in forgiveness between believers and non-believers in real life situations was reported. Implications for future research on forgiveness are discussed. © Springer Science+Business Media, Inc. 2006.link_to_subscribed_fulltex

Isolation, characterization and expression analysis of a hypoxia-responsive glucose transporter gene from the grass carp, Ctenopharyngodon idellus

Glucose transporters (GLUTs) have been implicated in adaptive and survival responses to hypoxic stress in mammals. In fish, the expression and regulation of GLUT in relation to hypoxia remains unexplored. Here we describe the identification of a hypoxia-responsive glucose transporter gene (gcGLUT) and the corresponding full-length cDNA from the grass carp. The gene spans ≈ 11 kb of genomic sequence and consists of 12 exons and 11 introns, and an open reading frame (ORF) of 1599 bp encoding a polypeptide of 533 amino acids, with a predicted molecular mass of ≈ 57 kDa and a pI of 8.34. BLASTX analysis showed that the ORF shared high sequence identity with the GLUT1 (57-59%), GLUT3 (59-60%) and GLUT4 (55-59%) proteins from different vertebrates. Comparative analysis of GLUT genomic structures showed that the arrangement of exons and position of split codons are highly conserved amongst members of the class I GLUTs suggesting that these genes share a common ancestor. Phylogenetic analysis indicated that gcGLUT is most closely related to the GLUT3 proteins. Northern blot analysis showed that the 3.1-kb gcGLUT transcript was most abundantly expressed and responsive to hypoxia in kidney. Up-regulated expression by hypoxia was also evident in eye and gill, but differential patterns of expression were observed. Low expression levels detected in brain, heart, liver and muscle were not responsive to hypoxic stress.link_to_subscribed_fulltex

Identifying transcriptional regulatory elements directing hypertrophic chondrocyte-specific expression of Col10a1

Acoustic emission (AE) analysis is a useful method for tracking the course of damage development in fiber reinforced composites. The analysis of the AE data is simple as long as the AE measurement is only used as an indicator that there is something going on in the material. Further analysis of AE data with respect to location and characterization of the AE sources must take into account the effects of the sample geometry. For unbounded media where bulk waves dominate wave propagation, AE sources can be located by determining arrival times at different sensors [1] and characterized by applying moment tensor inversion techniques [2]. However, components and laboratory test coupons are thin-walled in most cases and the analysis methods described above usually cannot be applied. In such samples, wave propagation in the far field is dominated by guided wave modes which show dispersion. During the past two decades many investigators have tried to classify the different source mechanisms in bounded samples by using simple parameters (like maximum amplitude, rise time, energy, counts or duration) extracted from waveforms. The reported data (see e.g. [3–7] and references therein) are highly inconsistent because the measured parameters depend of course not only on the AE source characteristics but also on the specimen shape and material