Synthesis of single-walled carbon nanotubes using hemoglobin-based iron catalyst

Hemoglobin (Hb) was used as a catalyst for the growth of single-walled carbon nanotubes (SWCNTs). Hb was deposited onto a hydrophilic treated substrate by spin coating method. After oxidation at 800 °C, protein chains were decomposed and iron oxide nanoparticles remained with an average diameter of 2.29 nm. High quality SWCNTs were synthesized with an average diameter of 1.22 nm. The protein chains prevent iron atoms aggregation and so the size of the nanoparticles is smaller than that from ferritin-like proteins.     

Novel acylflavones from Sideritis syriaca ssp. syriaca

In the framework of the detailed phytochemical analysis of the aerial parts of Sideritis syriaca, two novel acylflavones were isolated together with three acetylated flavone glycosides and acylated flavone glycosides. The novel acylflavones were identified as isoscutellarein 7-trans-p-coumarate and apigenin 7-,4′-bis(trans-p-coumarate). Their structures were elucidated by means of UV, 1D and 2D NMR, LC–DAD–MS and confirmed by HR-MS spectroscopy.     

Cluster Monte Carlo and numerical mean field analysis for the water liquid-liquid phase transition

Using Wolff's cluster Monte Carlo simulations and numerical minimization within a mean field approach, we study the low temperature phase diagram of water, adopting a cell model that reproduces the known properties of water in its fluid phases. Both methods allow us to study the thermodynamic behavior of water at temperatures, where other numerical approaches - both Monte Carlo and molecular dynamics - are seriously hampered by the large increase of the correlation times. The cluster algorithm also allows us to emphasize that the liquid-liquid phase transition corresponds to the percolation transition of tetrahedrally ordered water molecules.     

Promoting quality improvement research

The authors show how an internal grant program can stimulate quality improvement research by providing technical and financial support to clinicians and employees.     

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.     

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.