On the Magnetic Field produced by Electric Tramways

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The effects of a magnetic barrier and a nonmagnetic spacer in tunnel structures

The spin-polarized transport is investigated in a new type of magnetic tunnel junction which consists of two ferromagnetic electrodes separated by a magnetic barrier and a nonmagnetic metallic spacer. Based on the transfer matrix method and the nearly-free-electron-approximation the dependence of the tunnel magnetoresistance (TMR) and electron-spin polarization on the nonmagnetic layer thickness and the applied bias voltage are studied theoretically. The TMR and spin polarization show an oscillatory behavior as a function of the spacer thickness and the bias voltage. The oscillations originate from the quantum well states in the spacer, while the existence of the magnetic barrier gives rise to a strong spin polarization and high values of the TMR. Our results may be useful for the development of spin electronic devices based on coherent transport.Comment: 15 pages, 5 figure

Low-cost Management Aspects for Developing, Producing and Operating Future Space Transportation Systems

Abstract It is believed that a potential means for further significant reduction of the recurrent launch cost, which results also in a stimulation of launch rates of small satellites, is to make the launcher reusable, to increase its reliability and to make it suitable for new markets such as mass space tourism. Therefore, not only launching small satellites with expendable rockets on nonregular flights but also with reusable rockets on regular flights should be considered for the long term. However, developing, producing and operating reusable rockets require a fundamental change in the current "business as usual" philosophy. Under current conditions, it might not be possible to develop, to produce or to operate a reusable vehicle fleet economically. The favorite philosophy is based on "smart business" processes adapted by the authors using cost engineering techniques. In the following paper, major strategies for reducing costs are discussed, which are applied for a representative program proposal

Test beam measurement of the first prototype of the fast silicon pixel monolithic detector for the TT-PET project

The TT-PET collaboration is developing a PET scanner for small animals with 30 ps time-of-flight resolution and sub-millimetre 3D detection granularity. The sensitive element of the scanner is a monolithic silicon pixel detector based on state-of-the-art SiGe BiCMOS technology. The first ASIC prototype for the TT-PET was produced and tested in the laboratory and with minimum ionizing particles. The electronics exhibit an equivalent noise charge below 600 e- RMS and a pulse rise time of less than 2 ns, in accordance with the simulations. The pixels with a capacitance of 0.8 pF were measured to have a detection efficiency greater than 99% and, although in the absence of the post-processing, a time resolution of approximately 200 ps

Bayesian models for aggregate and individual patient data component network meta-analysis.

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics

Predictive Value of \u3csup\u3e18\u3c/sup\u3eF-Florbetapir and \u3csup\u3e18\u3c/sup\u3eF-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia

© 2020 by the Society of Nuclear Medicine and Molecular Imaging. The present study examined the predictive values of amyloid PET, 18F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset (n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using 18F-florbetapir (pattern expression score of an amyloid-β AD conversion-related pattern, constructed by principle-components analysis); 18F-FDG PET (pattern expression score of a previously defined 18F-FDG-based AD conversion-related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); 18F-FDG PET + amyloid PET; amyloid PET + nonimaging; 18F-FDG PET + nonimaging; and amyloid PET + 18F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the 18F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion:18F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials