Second-generation (2G) GdBCO-coated conductors (CCs) are promising for superconducting magnet applications because of their high critical current (I c ) density, low dependency of the I c on the external magnetic field, good mechanical properties and reasonable cost, which offer opportunities to develop ultra-high-field magnets. However, they have not been used in hightemperature superconducting (HTS) applications with persistent current mode (PCM) operation such as nuclear magnetic resonance/magnetic resonance imaging magnets owing to unavailability of fabrication techniques for proper joining and contacts. Here we report a resistance-free joint, termed a 'superconducting joint', for 2G GdBCO CCs that forms a direct connection to establish a superconducting closed loop for PCM operation. The I c value of the joined CCs is identical to that of the parent conductors in a liquid nitrogen bath (77 K). Moreover, the initially induced magnetic field of a model GdBCO coil containing a superconducting joint is maintained without decreasing, indicating the complete absence of electrical resistance. Thus, this fabrication method is a unique practical solution for lengthening the 2G HTS CCs and, more importantly, achieving PCM operation in 2G HTS magnet applications, including ultra-high-field nuclear magnetic resonance/magnetic resonance imaging magnets generating more than 1 GHz.

Objectives Quantitative ultrasound estimates such as the frequency-dependent backscatter coefficient (BSC) have the potential to enhance noninvasive tissue characterization and to identify tumors better than traditional B-mode imaging. Thus, investigating system independence of BSC estimates from multiple imaging platforms is important for assessing their capabilities to detect tissue differences. Methods Mouse and rat mammary tumor models, 4T1 and MAT, respectively, were used in a comparative experiment using 3 imaging systems (Siemens, Ultrasonix, and VisualSonics) with 5 different transducers covering a range of ultrasonic frequencies. Results Functional analysis of variance of the MAT and 4T1 BSC-versus-frequency curves revealed statistically significant differences between the two tumor types. Variations also were found among results from different transducers, attributable to frequency range effects. At 3 to 8 MHz, tumor BSC functions using different systems showed no differences between tumor type, but at 10 to 20 MHz, there were differences between 4T1 and MAT tumors. Fitting an average spline model to the combined BSC estimates (3–22 MHz) demonstrated that the BSC differences between tumors increased with increasing frequency, with the greatest separation above 15 MHz. Confining the analysis to larger tumors resulted in better discrimination over a wider bandwidth. Conclusions Confining the comparison to higher ultrasonic frequencies or larger tumor sizes allowed for separation of BSC-versus-frequency curves from 4T1 and MAT tumors. These constraints ensure that a greater fraction of the backscattered signals originated from within the tumor, thus demonstrating that statistically significant tumor differences were detected.

No abstract

No abstract

Irregular functional data in which densely sampled curves are observed over different ranges pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in applications. Motivated by applications in quantitative ultrasound signal analysis, this paper investigates a class of robust M-estimators for partially observed functional data including functional location and quantile estimators. Consistency of the estimators is established under general conditions on the partial observation process. Under smoothness conditions on the class of M-estimators, asymptotic Gaussian process approximations are established and used for large sample inference. The large sample approximations justify a bootstrap approximation for robust inferences about the functional response process.The performance is demonstrated in simulations and in the analysis of irregular functional data from quantitative ultrasound analysis.

A robust probabilistic classifier for functional data is developed to predict class membership based on functional input measurements and to provide a reliable probability estimates for class membership. The method combines a Bayes classifier and semi-parametric mixed effects model with robust tuning parameter to make the method robust to outlying curves, and to improve the accuracy of the risk or uncertainty estimates, which is crucial in medical diagnostic applications. The approach applies to functional data with varying ranges and irregular sampling without making parametric assumptions on the within-curve covariance. Simulation studies evaluate the proposed method and competitors in terms of sensitivity to heavy tailed functional distributions and outlying curves. Classification performance is evaluated by both error rate and logloss, the latter of which imposes heavier penalties on highly confident errors than on less confident errors. Runtime experiments on the R implementation indicate that the proposed method scales well computationally. Illustrative applications include data from quantitative ultrasound analysis and phoneme recognition.

This study explores depictions of Sannō in the Keiran shūyōshū, a collection of orally transmitted teachings on Mt. Hiei compiled in the early fourteenth century. Originally a conglomeration of protective kami, Sannō rose in prominence to become the primary deity of the mountain and, by extension, the divine representation of the Tendai teachings. Based on the medieval hermeneutic of source-trace, Sannō was posited as the embodiment of Tendai esoteric doctrine. This article demonstrates that the Sannō deity of Mt. Hiei, as constructed in the Keiran, represents a concerted effort among Tendai scholastics in medieval Japan to specify an orthodox esoteric Buddhist tradition by associating the fundamental doctrines of their school and consolidating competing interpretations into the guise of a singular deity.

No abstract