Electron-capture branch of 100Tc and tests of nuclear wave functions for double-beta decays

We present a measurement of the electron-capture branch of $^{100}$Tc. Our value, $B(\text{EC}) = (2.6 \pm 0.4) \times 10^{-5}$, implies that the $^{100}$Mo neutrino absorption cross section to the ground state of $^{100}$Tc is roughly one third larger than previously thought. Compared to previous measurements, our value of $B(\text{EC})$ prevents a smaller disagreement with QRPA calculations relevant to double-$\beta$ decay matrix elements

Effect of Locally Delivered Minocycline Microspheres on Markers of Bone Resorption

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141419/1/jper0835.pd

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming