Comparing methodologies for structural identification and fatigue life prediction of a highway bridge

Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an L∞-norm-based Gaussian likelihood function), and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are compared. In the modified Bayesian model updating methodology, a correction is used in the likelihood function to account for the effect of a finite number of measurements on posterior probability–density functions. The application of these data-interpretation methodologies for condition assessment and fatigue life prediction is illustrated on a highway steel–concrete composite bridge having four spans with a total length of 219 m. A detailed 3D finite-element plate and beam model of the bridge and weigh-in-motion data are used to obtain the time–stress response at a fatigue critical location along the bridge span. The time–stress response, presented as a histogram, is compared to measured strain responses either to update prior knowledge of model parameters using residual minimization and Bayesian methodologies or to obtain candidate model instances using the EDMF methodology. It is concluded that the EDMF and modified Bayesian model updating methodologies provide robust prediction of fatigue life compared with residual minimization and traditional Bayesian model updating in the presence of correlated non-Gaussian uncertainty. EDMF has additional advantages due to ease of understanding and applicability for practicing engineers, thus enabling incremental asset-management decision making over long service lives. Finally, parallel implementations of EDMF using grid sampling have lower computations times than implementations using adaptive sampling

Use of thiopurines in inflammatory bowel disease : an update

Inflammatory bowel disease (IBD), once considered a disease of the Western hemisphere, has emerged as a global disease. As the disease prevalence is on a steady rise, management of IBD has come under the spotlight. 5-Aminosalicylates, corticosteroids, immunosuppressive agents and biologics are the backbone of treatment of IBD. With the advent of biologics and small molecules, the need for surgery and hospitalization has decreased. However, economic viability and acceptability is an important determinant of local prescription patterns. Nearly one-third of the patients in West receive biologics as the first/initial therapy. The scenario is different in developing countries where biologics are used only in a small proportion of patients with IBD. Increased risk of reactivation of tuberculosis and high cost of the therapy are limitations to their use. Thiopurines hence become critical for optimal management of patients with IBD in these regions. However, approximately one-third of patients are intolerant or develop adverse effects with their use. This has led to suboptimal use of thiopurines in clinical practice. This review article discusses the clinical aspects of thiopurine use in patients with IBD with the aim of optimizing their use to full therapeutic potential.Peer reviewe

Change in Mental Models of ADAS in Relation to Quantity and Quality of Exposure

69A3551747131Given the importance of mental models towards safe interaction with Advanced Driver Assistance Systems (ADAS) and the various human factors challenges regarding ADAS such as miscalibrated trust and the effect on workload, it is important to understand how different types of driving experiences and exposures affect drivers\u2019 mental models about ADAS. The objective of this study was to examine how the frequency and quality of exposure (exposure defined as driving through events or situations that have some bearing on the functions of the Adaptive Cruise Control (ACC)) affect drivers\u2019 mental models about ACC, their trust, workload, and their use of the systems as measured by their behaviors around disengaging ACC