Protactinium-231: A new radionuclide for AMS

For the first time, an AMS system was used to determine concentrations of the actinide Protactinium-231. ²³¹Pa has widespread applications in the earth sciences. It can be used for U-series disequilibrium dating, and ²³¹Pa is an important tracer in Paleoceanography. The Pa isotopes were measured with the compact ETH/PSI-AMS facility TANDY operating at about 300 kV. The linearity of the AMS is documented with two standard-dilution experiments and first measurements of natural samples from very different climate archives are presented. Our results show that it is possible to determine ²³¹Pa amounts in the lower femtogram (1 fg = 10⁻¹⁵ g) range with the TANDY. In natural samples, ²³¹Pa concentrations between 1 and 4 pg/g (1 pg = 10⁻¹² g) were measured with an average total error of 3% (one sigma). The average over all reproducibility of these first measurements was better than 2% with clear potential for improvement.     

Prediction of Speech Quality Based on Resilient Backpropagation Artificial Neural Network

The paper presents a system for monitoring and assessment the speech quality in the IP telephony infrastructures using modular probes. The probes are placed at key nodes in the network where aggregating packet loss data. The system dynamically measures speech quality and results are collected on a central server. For data analysis we applied four-state Markov model for modeling the impact of network impairments on speech quality, afterwards, the resilient back propagation (Rprop) algorithm was used to train a neural network. Information about the speech quality are displayed in the form of automatically generated graphs and tables. The proposed solution has been tested with selected codecs and further generalizes the already presented concepts of the speech quality estimation in the IP environment.

     

Double-blind, placebo-controlled study of the effects of carvedilol in patients with moderate to severe heart failure. The PRECISE Trial. Prospective Randomized Evaluation of Carvedilol on Symptoms and Exercise

BACKGROUND: Carvedilol has improved the symptomatic status of patients with moderate to severe heart failure in single-center studies, but its clinical effects have not been evaluated in large, multicenter trials. METHODS AND RESULTS: We enrolled 278 patients with moderate to severe heart failure (6-minute walk distance, 150 to 450 m) and a left ventricular ejection fraction < or = 0.35 at 31 centers. After an open-label, run-in period, each patient was randomly assigned (double-blind) to either placebo (n = 145) or carvedilol (n = 133; target dose, 25 to 50 mg BID) for 6 months, while background therapy with digoxin, diuretics, and an ACE inhibitor remained constant. Compared with placebo, patients in the carvedilol group had a greater frequency of symptomatic improvement and lower risk of clinical deterioration, as evaluated by changes in the NYHA functional class (P = .014) or by a global assessment of progress judged either by the patient (P = .002) or by the physician (P < .001). In addition, treatment with carvedilol was associated with a significant increase in ejection fraction (P < .001) and a significant decrease in the combined risk of morbidity and mortality (P = .029). In contrast, carvedilol therapy had little effect on indirect measures of patient benefit, including changes in exercise tolerance or quality-of-life scores. The effects of the drug were similar in patients with ischemic heart disease or idiopathic dilated cardiomyopathy as the cause of heart failure. CONCLUSIONS: These findings indicate that, in addition to its favorable effects on survival, carvedilol produces important clinical benefits in patients with moderate to severe heart failure treated with digoxin, diuretics, and an ACE inhibitor.     

Increased aggressive responding in male volunteers following the administration of gradually increasing doses of testosterone cypionate

Traumatic vertebral artery injuries are relatively rare. Until recently, insufficient neurodiagnostic technology and a lack of normative data for the population of USAF aviators prevented recommending flying waivers following such vascular injuries. We report the case of an aviator who experienced a traumatic right vertebral artery occlusion with distal embolization. Timely access to a state-of-the-art neurodiagnostic evaluation, which included time-of-flight magnetic resonance angiography (MRA), and standard contrast angiography, provided anatomic imaging at the time of injury and during the recuperative period. Sophisticated neurologic testing protocols at the Armstrong Laboratory's Aeromedical Clinical Sciences Division, Brooks Air Force Base, TX, assessed in-depth this individual's functional status. Consult Service evaluators then compared his performance data with current data in the Head Injury Study in Aviators (HISA) data base. MRA, in comparison with standard contrast angiography, demonstrated anatomic stability of his vascular and neurologic lesions. He demonstrated a functional capacity consistent with normal neurologic functioning in aviators which permitted recommending an occupational (flying) waiver. This case exemplified application of sophisticated ACS neurologic testing protocols to aeromedical evaluations and the use of MRA technology as a vascular screening tool during medical followup.     

Parametric Probabilistic Routing in Sensor Networks

Motivated by realistic sensor network scenarios that have mis-in-formed nodes and variable network topologies, we propose an approach to routing that combines the best features of limited-flooding and information-sensitive path-finding protocols into a reliable, low-power method that can make delivery guarantees independent of parameter values or information noise levels. We introduce Parametric Probabilistic Sensor Network Routing Protocols, a family of light-weight and robust multi-path routing protocols for sensor networks in which an intermediate sensor decides to forward a message with a probability that depends on various parameters, such as the distance of the sensor to the destination, the distance of the source sensor to the destination, or the number of hops a packet has already traveled. We propose two protocol variants of this family and compare the new methods to other probabilistic and deterministic protocols, namely constant-probability gossiping, uncontrolled flooding, random wandering, shortest path routing (and a variation), and a load-spreading shortest-path protocol inspired by (Servetto and Barrenechea, 2002). We consider sensor networks where a sensor’s knowledge of the local or global information is uncertain (parametrically noised) due to sensor mobility, and investigate the trade-off between robustness of the protocol as measured by quality of service (in particular, successful delivery rate and delivery lag) and use of resources (total network load). Our results for networks with randomly placed nodes and realistic urban networks with varying density show that the multi-path protocols are less sensitive to misinformation, and suggest that in the presence of noisy data, a limited flooding strategy will actually perform better and use fewer resources than an attempted single-path routing strategy, with the Parametric Probabilistic Sensor Network Routing Protocols outperforming other protocols. Our results also suggest that protocols using network information perform better than protocols that do not, even in the presence of strong noise. Christopher L. Barrett is leader of the Basic and Applied Simulation Science Group of the Computing and Computational Sciences Division at Los Alamos National Laboratory. His Group is a simulation science and technology (S&T) invention organization of 30 scientists devoted to providing large-scale, high performance methods for systems analysis and simulation-based assisted reasoning. His Group engages in fundamental mathematical, algorithmic, and complex systems analysis research. Current applied research is focused on interdependent simulation and analysis tools for complex, socio-technical systems like transportation, communications, public health and other critical infrastructure areas. His scientific experience is in simulation, scientific computation, algorithm theory and development, system science and control, engineering science, bio-systems analysis, decision science, cognitive human factors, testing and training. His applied science and engineering achievements include, for example, development of large-scale, high performance simulation systems (e.g., Transportation Analysis Simulation System, TRANSIMS) and development of a distributed computing approach for detailed simulation-based study of mobile, packet switched digital communications systems (Self Organizing Stochastic Rebroadcast Relay, SORSRER). He has a M.S. and Ph.D. in Bio-information Systems from California Institute of Technology. He is a decorated Navy veteran having served in both the submarine service and as a pilot. He has been awarded three Distinguished Service Awards from Los Alamos National Laboratory, one from the Alliance for Transportation Research, one from the Royal Institute of Technology, Stockholm, and one from Artificial Life and Robotics, Oita University, Japan. Stephan J. Eidenbenz is a technical staff member in the Basic and Applied Simulation Science group (CCS-5) at Los Alamos National Laboratory (LANL). He received an M.Sc. in Computer Science from the Swiss Federal Institute of Technology (ETH) in Zurich in 1997 and a Ph.D. in Computer Science from ETH in 2000; he also obtained a Bachelor’s degree in business administration from GSBA in Zurich in 1999. Stephan has worked for McKinsey & Co. in Switzerland, where he received training in business administration and microeconomics. He has held a postdoctoral position at ETH and he has been a postdoctoral fellow at LANL. Stephan’s more than 30 publications cover a wide range of subjects such as approximability and inapproximability properties of visibility problems in polygons and terrains, error modeling in sequencing problems for computation biology, and designing communication protocols robust against selfish behavior. His current research interests include selfish networking, algorithmic game theory, network modeling and simulation, network design, and network optimization. Lukas Kroc is a student of M.Sc. program in Computer Science at Charles University in Prague. In 2003, he was a Graduate Research Assistant at the Basic and Applied Simulation Science group (CCS-5) at Los Alamos National Laboratory. His research interests include simulation, wireless networking and artificial intelligence. Madhav V. Marathe is a Team Leader for Mathematics and Computer Science in the Basic and Applied Simulation Science group, Computer and Computational Sciences (CCS-5) at the Los Alamos National Laboratory. He obtained his B.Tech in 1989 in Computer Science and Engg. from IIT Madras, India and his Ph.D. in 1994 in Computer Science, from University at Albany. His team focuses on developing mathematical and computational tools for design and analysis of large scale simulations of socio-technical and critical infrastructure systems. His research interests are in modeling and simulations of large socio-technical systems, design and analysis of algorithms, computational complexity theory, theory of parallel, distributed and mobile computing and communication systems. He has published over 100 research articles in peer reviewed journals and conferences. He is an adjunct faculty in the Computer Science Department at the University of New Mexico. James P. Smith is a technical staff member in the Basic and Applied Simulation Science Group of the Computing and Computational Sciences Division at Los Alamos National Laboratory. His principal interest is in high performance computing applied to modeling, simulation and analysis of socio-technical systems. His current research applies to national infrastructure, especially telecommunication/computing, public health, and transportation. He has scientific experience in high performance computing and parallel processing applied to large-scale microscopic simulations, including original software design and debugging of very large, evolving systems of inter-operable computational systems, and efficient analysis and synthesis of massive data produced by multi-scale complex environments. Before attending graduate school he worked for a short time in nuclear theory, and had several publications in experimental biophysics from the Pennsylvania Muscle Institute and Bockus Research Institute. During graduate school he took a one year hiatus to start a company to work in analytic finance, and then spent time doing theoretical space physics at LANL. His graduate work eventually included theoretical and experimental fusion research, but concentrated on computational space plasma physics. He has publications in biophysics, analytic finance, education, space plasma physics and computer science, and is a co-inventor on the TRANSIMS patent. He has a Ph.D. in Theoretical Plasma Physics from the University of Texas at Austin.This revised version was published online in August 2005 with a corrected cover date.