Magnetic resonance elastography (MRE) offers a noninvasive solution to visualize the mechanical properties of soft tissue, but the study suffers from expensive magnetic resonance scanning. Moreover, translating MRE wave images into soft tissue elasticity is a nontrivial issue for clinical professionals and healthcare practitioners. An interactive system-OpenMRE-is thus developed with the aid of ImageJ for numerical MRE study. It is comprised of two comparatively independent toolkits, namely MREA for simulation and MREP for interpretation. MREA mainly deals with the forward problem of MRE, and provides a numerical platform to determine the propagation and distribution of specially designed elastic wave. It is possible to numerically study some state-of-the-art paradigms including multisource and multifrequency MRE. The resultant wave images are interpretable in MREP that is designed for the inverse problem of MRE. It consists of the algorithms for phase unwrapping, directional filtering, and elasticity reconstruction. In a word, OpenMRE offers the MRE community a convenient and well-functioning system for interactive MRE study.

Scalable and comprehensive testing of mobile apps is extremely challenging. Every test input needs to be run with a variety of contexts, such as: device heterogeneity, wireless network speeds, locations, and unpredictable sensor inputs. The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way. It incorporates key techniques to make app testing more tractable, including a context test space prioritizer to quickly discover failure scenarios for each app. We have implemented Caiipa on a cluster of VMs and real devices that can each emulate various combinations of contexts for tablet and phone apps. We evaluate Caiipa by testing 265 commercially available mobile apps based on a comprehensive library of real-world conditions. Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).

The first (or last) few alternatives need to be identified in some cases, such as recruiting (or dismissing) the first (or last) few candidates in a firm. In such cases, we analyze the impact of attitudinal character on the multicriteria decision making with ordered weighted averaging (OWA) operators. First, we propose a linear programming model to determine the best and worst ranking orders for each alternative to identify if it can be one of the potential first (or last) few alternatives. Second, we determine the attitudinal character's range for each alternative, in which it can be one of the potential alternatives. Furthermore, under the specified subintervals of attitudinal character, we identify which alternatives can be the potential ones by the analysis result obtained in the second step. According to the impact of attitudinal character analyzed by the proposed method, the set of alternatives can be reduced, when the range of attitudinal character is specified and the best and worst ranges of attitudinal character for each alternative can be identified in self-evaluation. © 2012 Wiley Periodicals, Inc.