Ultrasonic tomography is an emerging technology that shows promise for quality assurance/quality control (QA/QC) during construction or for rehabilitation decisions about concrete pavements. However, the benefits of this emerging technology have not yet been fully captured for widespread use in highway infrastructure management. Verification of a state-of-the-art ultrasonic tomography device, MIRA, is presented through multiple field trials involving typical pavement constructability and rehabilitation issues. Field trials indicate that although the device is a useful portable pavement diagnostic alternative capable of consistent thickness measurement, reinforcement location, and distress evaluation, significant efforts and user expertise are required for measurement and data interpretation of large-scale application. Software was developed for a more productive, objective signal interpretation method with auto-mated analysis of reinforcement location in continuously reinforced concrete pavement. This type of automation for multiple applications shows promise for the use of ultrasonic tomography to improve large-scale pavement QA/QC and rehabilitation projects in the future. Nevertheless, the research in the paper shows ultrasonic tomography to be an accurate, reliable, and convenient alternative or supplement to traditional techniques that can be used for a wide variety of small-scale pavement diagnostics applications.

Mucus hypersecretion is commonly observed in many chronic airway infl ammatory diseases. Mucin 5AC (MUC5AC) is a major airway mucin because of its high expression in goblet cells. Here, the authors identifi ed a gene called SAM domain -containing prostate-derived Ets factor (SPDEF) that was induced by interleukin (IL)-13. Their results showed that specifi c knockdown of SPDEF reduced IL-13-induced MUC5AC expression in human airway epithelial cells. This fi nding was associated with decreased expression of anterior gradient 2 (AGR2) and Ca 2 ϩ -activated Cl Ϫ channel (CLCA1), which regulate IL-13-mediated MUC5AC overproduction. Furthermore, transfection with SPDEF siRNA enhanced expression of forkhead box a2 (Foxa2), a key transcription factor that is known to prevent mucus production. The authors also demonstrated that the repression of STAT6 inhibited expression of SPDEF and MUC5AC induced by IL-13. These results show that SPDEF plays a critical role in regulating a transcriptional network mediating IL-13-induced MUC5AC synthesis dependent on STAT6.

The structural response of jointed plain concrete pavement slabs was evaluated using data obtained from instrumented slabs. The instrumented slabs were a part of newly constructed jointed plain concrete overlay that was constructed on existing asphalt concrete pavement on I–70 in Colorado, near the Kansas–Colorado border. The instrumentation consisted of dial gauges for measuring curling deflections at the slab corner and longitudinal edge and surface-mounted strain gauges for measuring load strains at the longitudinal edge at midslab. The through-thickness temperature profiles in the pavement slabs were also measured at 30-min intervals during the field test. Analysis of the field data showed that the instrumented slabs had a considerable amount of built-in upward curling and that concrete slabs on a stiff base can act completely independent of the base or monolithically with the base, depending on the loading condition. The built-in upward curling of the slabs has the same effect as negative temperature gradients. These findings suggest that the effects of temperature gradients on the critical edge stresses may not be as great as previously thought and that the corner loading, in some cases, may produce more critical conditions for slab cracking. Another important finding of this study is that a physical bond between pavement layers is not required to obtain a bonded response from concrete pavements.

Pearson's correlation measure is only able to model linear dependence between random variables. Hence, conventional principal component analysis (PCA) based on Pearson's correlation measure is not suitable for application to modern industrial processes where process variables are often nonlinearly related. To address this problem, a nonparametric PCA model is proposed based on nonlinear correlation measures, including Spearman's and Kendall tau's rank correlation. These two correlation measures are also less sensitive to outliers comparing to Pearson's correlation, making the proposed PCA a robust feature extraction technique. To reveal meaningful patterns from process data, a generalized iterative deflation method is applied to the robust correlation matrix of the process data to sequentially extract a set of leading sparse pseudoeigenvectors. For online fault diagnosis, the T 2 and SPE statistics are computed and analyzed with respect to the subspace spanned by the extracted pseudoeigenvectors. The proposed method is applied to two industrial case studies. Its process monitoring performance is demonstrated to be superior to that of the conventional PCA and is comparable to those of Kernel PCA and kernel independent component analysis at a lower computational cost. The proposed PCA is also more robust in sparse feature extraction from contaminated process data.