Magnets are the principal market for superconductors, but making attractive conductors out of the high-temperature cuprate superconductors (HTSs) has proved difficult because of the presence of high-angle grain boundaries that are generally believed to lower the critical current density, J(c). To minimize such grain boundary obstacles, HTS conductors such as REBa2Cu3O(7-x) and (Bi, Pb)2Sr2Ca2Cu3O(10-x) are both made as tapes with a high aspect ratio and a large superconducting anisotropy. Here we report that Bi2Sr2CaCu2O(8-x) (Bi-2212) can be made in the much more desirable isotropic, round-wire, multifilament form that can be wound or cabled into arbitrary geometries and will be especially valuable for high-field NMR magnets beyond the present 1 GHz proton resonance limit of Nb3Sn technology. An appealing attribute of this Bi-2212 conductor is that, being without macroscopic texture, it contains many high-angle grain boundaries but nevertheless attains a very high J(c) of 2,500 A mm(-2) at 20 T and 4.2 K. The large potential of the conductor has been demonstrated by building a small coil that generated almost 2.6 T in a 31 T background field. This demonstration that grain boundary limits to high Jc can be practically overcome underlines the value of a renewed focus on grain boundary properties in non-ideal geometries.

Compromised machines are one of the key security threats on the Internet; they are often used to launch various security attacks such as DDoS, spamming, and identity theft. In this thesis we address this issue by investigating effective solutions to automatically identify compromised machines in a network. Given that spamming provides a key economic incentive for attackers to recruit the large number of compromised machines, we focus on the subset of compromised machines that are involved in the spamming activities, commonly known as spam zombies. We develop an effective spam zombie detection system named SPOT by monitoring outgoing messages of a network. SPOT is designed based on a powerful statistical tool called Sequential Probability Ratio Test, which has bounded false positive and false negative error rates. Our evaluation studies based on a two-month email trace collected in a large U.S. campus network show that SPOT is an effective and efficient system in automatically detecting compromised machines in a network. For example, among the 440 internal IP addresses observed in the email trace, SPOT identifies 132 of them as being associated with compromised machines. Out of the 132 IP addresses identified by SPOT, 126 can be either independently confirmed (110) or highly likely (16) to be compromised.