Crystallization and characterization of Y2O3-SiO2 glasses

Glasses in the yttria-silica system with 20 to 40 mol pct Y2O3 were subjected to recrystallization studies after melting at 1900 to 2100 C in W crucibles in 1 and 50 atm N2. The TEM and XRD results obtained indicate the presence of the delta, gamma, gamma prime, and beta-Y2Si2O7 crystalline phases, depending on melting and quenching conditions. Heat treatment in air at 1100 to 1600 C increased the amount of crystallization, and led to the formation of Y2SiO5, cristabalite, and polymorphs of Y2Si2O7. Also investigated were the effects of 5 and 10 wt pct zirconia additions

IRMA via SDN: Intrusion Response and Monitoring Appliance via Software-Defined Networking

Recent approaches to network intrusion prevention systems (NIPSs) use software-defined networking (SDN) to take advantage of dynamic network reconfigurability and programmability, but issues remain with system component modularity, network size scalability, and response latency. We present IRMA, a novel SDN-based NIPS for enterprise networks, as a network appliance that captures data traffic, checks for intrusions, issues alerts, and responds to alerts by automatically reconfiguring network flows via the SDN control plane. With a composable, modular, and parallelizable service design, we show improved throughput and less than 100 ms average latency between alert detection and response.Roy J. Carver FellowshipOpe

Algorithms for Performance, Dependability, and Performability Evaluation using Stochastic Activity Networks

Modeling tools and technologies are important for aerospace development. At the University of Illinois, we have worked on advancing the state of the art in modeling by Markov reward models in two important areas: reducing the memory necessary to numerically solve systems represented as stochastic activity networks and other stochastic Petri net extensions while still obtaining solutions in a reasonable amount of time, and finding numerically stable and memory-efficient methods to solve for the reward accumulated during a finite mission time. A long standing problem when modeling with high level formalisms such as stochastic activity networks is the so-called state space explosion, where the number of states increases exponentially with size of the high level model. Thus, the corresponding Markov model becomes prohibitively large and solution is constrained by the the size of primary memory. To reduce the memory necessary to numerically solve complex systems, we propose new methods that can tolerate such large state spaces that do not require any special structure in the model (as many other techniques do). First, we develop methods that generate row and columns of the state transition-rate-matrix on-the-fly, eliminating the need to explicitly store the matrix at all. Next, we introduce a new iterative solution method, called modified adaptive Gauss-Seidel, that exhibits locality in its use of data from the state transition-rate-matrix, permitting us to cache portions of the matrix and hence reduce the solution time. Finally, we develop a new memory and computationally efficient technique for Gauss-Seidel based solvers that avoids the need for generating rows of A in order to solve Ax = b. This is a significant performance improvement for on-the-fly methods as well as other recent solution techniques based on Kronecker operators. Taken together, these new results show that one can solve very large models without any special structure

Dependency-Based Decomposition of Systems Involving Rare Events

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryIBM Ph.D. Fellowshi