Long-time asymptotics of the Navier-Stokes and vorticity equations on R(3)

We use the vorticity formulation to study the long-time behaviour of solutions to the Navier-Stokes equation on R(3). We assume that the initial vorticity is small and decays algebraically at infinity. After introducing self-similar variables, we compute the long-time asymptotics of the rescaled vorticity equation up to second order. Each term in the asymptotics is a self-similar divergence-free vector field with Gaussian decay at infinity, and the coefficients in the expansion can be determined by solving a finite system of ordinary differential equations. As a consequence of our results, we are able to characterize the set of solutions for which the velocity field satisfies ||u(.,t)||(L(2)) = o(t(-5/4)) as t-->+ infinity. In particular, we show that these solutions lie on a smooth invariant submanifold of codimension 11 in our function space.     

Criteria for global minimum of sum of squares in nonlinear regression

Demidenko [2000. Is this the least squares estimate? Biometrika 87, 437-452] has established the relationship between the curvature of nonlinear regression and the local convexity of a sum of squares: the Hessian matrix is positive definite if the sum of squares is less than the minimum squared radius of the full curvature. In this paper, we continue developing the criteria for the global minimum of the sum of squares in nonlinear regression. In particular, the concept of the local unimodality is introduced; a function is called locally unimodal on a level set if it has a unique local minimum in each component of that level set. We show that the level of the local unimodality of the sum of squares is equal to the minimum squared radius of the intrinsic curvature of the nonlinear regression function. A new class of unidirected nonlinear regression models is introduced with an interpretation in terms of differential geometry. The criteria are illustrated by several popular nonlinear regression models.     

Semiconductor Yield Analysis and Multi-Chip Package(MCP) Dic Pairing Optimization using Machine Learning

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Achieving dynamic, multi-commander, multi-mission planning and execution

The Multi-Agent Distributed Goal Satisfaction (MADGS) system facilitates distributed mission planning and execution in complex dynamic environments with a focus on distributed goal planning and satisfaction and mixed-initiative interactions with the human user. By understanding the fundamental technical challenges faced by our commanders on and off the battlefield, we can help ease the burden of decision-making. MADGS lays the foundations for retrieving, analyzing, synthesizing, and disseminating information to commanders. In this paper, we present an overview of the MADGS architecture and discuss the key components that formed our initial prototype and testbed. Eugene Santos, Jr. received the B.S. degree in mathematics and Computer science and the M.S. degree in mathematics (specializing in numerical analysis) from Youngstown State University, Youngstown, OH, in 1985 and 1986, respectively, and the Sc.M. and Ph.D. degrees in computer science from Brown University, Providence, RI, in 1988 and 1992, respectively. He is currently a Professor of Engineering at the Thayer School of Engineering, Dartmouth College, Hanover, NH, and Director of the Distributed Information and Intelligence Analysis Group (DI²AG). Previously, he was faculty at the Air Force Institute of Technology, Wright-Patterson AFB and the University of Connecticut, Storrs, CT. He has over 130 refereed technical publications and specializes in modern statistical and probabilistic methods with applications to intelligent systems, multi-agent systems, uncertain reasoning, planning and optimization, and decision science. Most recently, he has pioneered new research on user and adversarial behavioral modeling. He is an Associate Editor for the IEEE Transactions on Systems, Man, and Cybernetics: Part B and the International Journal of Image and Graphics. Scott DeLoach is currently an Associate Professor in the Department of Computing and Information Sciences at Kansas State University. His current research interests include autonomous cooperative robotics, adaptive multiagent systems, and agent-oriented software engineering. Prior to coming to Kansas State, Dr. DeLoach spent 20 years in the US Air Force, with his last assignment being as an Assistant Professor of Computer Science and Engineering at the Air Force Institute of Technology. Dr. DeLoach received his BS in Computer Engineering from Iowa State University in 1982 and his MS and PhD in Computer Engineering from the Air Force Institute of Technology in 1987 and 1996. Michael T. Cox is a senior scientist in the Intelligent Distributing Computing Department of BBN Technologies, Cambridge, MA. Previous to this position, Dr. Cox was an assistant professor in the Department of Computer Science & Engineering at Wright State University, Dayton, Ohio, where he was the director of Wright State’s Collaboration and Cognition Laboratory. He received his Ph.D. in Computer Science from the Georgia Institute of Technology, Atlanta, in 1996 and his undergraduate from the same in 1986. From 1996 to 1998, he was a postdoctoral fellow in the Computer Science Department at Carnegie Mellon University in Pittsburgh working on the PRODIGY project. His research interests include case-based reasoning, collaborative mixed-initiative planning, intelligent agents, understanding (situation assessment), introspection, and learning. More specifically, he is interested in how goals interact with and influence these broader cognitive processes. His approach to research follows both artificial intelligence and cognitive science directions.     

Effects of Nutrient Source and Supply on Crude Oil Biodegradation in Continuous-Flow Beach Microcosms

Ammonium and nitrate were used as nitrogen sources to support microbial biodegradation of crude oil in continuous-flow beach microcosms to determine whether either nutrient was more effective in open systems, such as intertidal shorelines. No differences in the rate or extent of oil biodegradation were observed, regardless of whether these nutrients were provided continuously or intermittently. Nutrients were provided once every two weeks to intermittent-input microcosms and washed out within four to five days. In continuous-input microcosms, ammonium and nitrate were assimilated as quickly as they were provided during the first week, but both accumulated to greater than 10?mg?N/L thereafter. The sensitivity of the oil mineralization rate to nutrient input decreased rapidly as the extent of oil degradation increased, and after about two weeks the rate of oil-mineralization appeared to be independent of nutrient input. Therefore, there may be little value in maintaining a long-term supply of nutrients in contact with oil-contaminated sediments. The rates of microbial assimilation of ammonium and nitrate followed similar trends. Both compounds were assimilated more slowly as the extent of oil biodegradation increased, and the nitrate uptake rates approached zero after about two weeks. Ammonium assimilation continued at a low rate throughout the six-week experiment, but this did not appear to affect the rate of oil mineralization. Assimilation of ammonium resulted in a sharp decrease in the pH of the synthetic seawater that was pumped continuously through the microcosms, but nitrate had a much smaller effect on pH. The magnitude of the ammonium-associated pH change was never as large as was observed in previous studies involving oil biodegradation in batch reactors, however, and did not affect the oil-biodegradation rate.     

Autonet: a high-speed, self-configuring local area network usingpoint-to-point links

Autonet is a self-configuring local area network composed of switches interconnected by 100 Mb/s, full-duplex, point-to-point links. The switches contain 12 ports that are internally connected by a full crossbar. Switches use cut-through to achieve a packet forwarding latency as low as 2 ms/switch. Any switch port can be cabled to any other switch port or to a host network controller. A processor in each switch monitors the network's physical configuration. A distributed algorithm running on the switch processor computes the routes packets are to follow and fills in the packet forwarding table in each switch. With Autonet, distinct paths through the set of network links can carry packets in parallel, allowing many pairs of hosts to communicate simultaneously at full link bandwidth. A 30-switch network with more than 100 hosts has been the service network for Digital's Systems Research Center since February 1990     

A high-risk method for studying psychosocial antecedents of chronic pain: The prospective investigation of herpes zoster.

Although patients with chronic pain (CP) are often psychologically distressed, it has been difficult to determine whether this distress is an antecedent of CP or whether it is caused by the experience of living with CP. This investigation aimed to develop a method that would allow individuals who are at risk for the development of CP to be studied before the pain has become chronic. Patients with acute herpes zoster (HZ) were assessed with demographic, medical, pain, and psychosocial measures. Pain was assessed in follow-up interviews at 6 wks and 3, 5, 8, and 12 mo after these initial assessments. There were no significant differences between Ss who developed short-term HZ pain and Ss who did not develop short-term pain for any of the measures at the initial assessment, except for 1 measure of pain intensity. Ss who developed HZ CP, however, had significantly greater pain intensity, higher state and trait anxiety, greater depression, lower life satisfaction, and greater disease conviction at the initial assessment than Ss who did not develop CP. In discriminant analyses, disease conviction, pain intensity, and state anxiety each made a unique contribution to discriminating Ss who did and did not develop CP. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mean-value analysis of multi-facility blocking models with state-dependent arrivals

A new mean-value type of algorithm is developed for analyzing multi-facility blocking models with state-dependent arrival rates. It can be applied to a broad class of blocking systems with simultaneous resource possession including, for example, circuit-switched networks. The underlying recursion is cast in terms of blocking probabilities and marginal state probabilities. The developments made here generalize previous results that were restricted to the case of constant arrival rates.     

ECOWARM: News and information

ECOWARM: News and information