29,895 research outputs found
Work Function of Single-wall Silicon Carbide Nanotube
Using first-principles calculations, we study the work function of single
wall silicon carbide nanotube (SiCNT). The work function is found to be highly
dependent on the tube chirality and diameter. It increases with decreasing the
tube diameter. The work function of zigzag SiCNT is always larger than that of
armchair SiCNT. We reveal that the difference between the work function of
zigzag and armchair SiCNT comes from their different intrinsic electronic
structures, for which the singly degenerate energy band above the Fermi level
of zigzag SiCNT is specifically responsible. Our finding offers potential
usages of SiCNT in field-emission devices.Comment: 3 pages, 3 figure
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An agent-based DDM for high level architecture
The Data Distribution Management (DDM) service is one of the six services provided in the Runtime Infrastructure (RTI) of High Level Architecture (HLA). Its purpose is to perform data filtering and reduce irrelevant data communicated between federates. The two DDM schemes proposed for RTI, region based and grid based DDM, are oriented to send as little irrelevant data to subscribers as possible, but only manage to filter part of this information and some irrelevant data is still being communicated. Previously (G. Tan et al., 2000), we employed intelligent agents to perform data filtering in HLA, implemented an agent based DDM in RTI (ARTI) and compared it with the other two filtering mechanisms. The paper reports on additional experiments, results and analysis using two scenarios: the AWACS sensing aircraft simulation and the air traffic control simulation scenario. Experimental results show that compared with other mechanisms, the agent based approach communicates only relevant data and minimizes network communication, and is also comparable in terms of time efficiency. Some guidelines on when the agent based scheme can be used are also give
Equation of state of a superfluid Fermi gas in the BCS-BEC crossover
We present a theory for a superfluid Fermi gas near the BCS-BEC crossover,
including pairing fluctuation contributions to the free energy similar to that
considered by Nozieres and Schmitt-Rink for the normal phase. In the strong
coupling limit, our theory is able to recover the Bogoliubov theory of a weakly
interacting Bose gas with a molecular scattering length very close to the known
exact result. We compare our results with recent Quantum Monte Carlo
simulations both for the ground state and at finite temperature. Excellent
agreement is found for all interaction strengths where simulation results are
available.Comment: 7 pages, 4 figures, published version in Europhysics Letters, a long
preprint with details will appear soo
Teleoperation experiments with a Utah/MIT hand and a VPL DataGlove
A teleoperation system capable of controlling a Utah/MIT Dextrous Hand using a VPL DataGlove as a master is presented. Additionally the system is capable of running the dextrous hand in robotic (autonomous) mode as new programs are developed. The software and hardware architecture used is presented and the experiments performed are described. The communication and calibration issues involved are analyzed and applications to the analysis and development of automated dextrous manipulations are investigated
Sorption Speciation of Nickel(II) onto Ca-Montmorillonite: Batch, EXAFS Techniques and Modeling
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
QUANTITATIVE MONITORING OF CEFRADINE IN HUMAN URINE USING A LUMINOL/SULFOBUTYLETHER-beta-CYCLODEXTRIN CHEMILUMINESCENCE SYSTEM
In this paper, a sensitive, rapid, and simple flow-injection chemiluminescence (FI-CL) technique is described for determining cefradine in human urine and capsule samples at the picogram level. The results show that cefradine within 0.1-100.0 nmol/L quantitatively quenches the CL intensity of the luminol/sulfo butylether-beta-cyclodextrin (SBE-beta-CD) system, with a relative correlation coefficient r of 0.9931. Subsequently, the possible mechanism for the quenching phenomenon is discussed in detail using the FI-CL and molecular docking methods. The proposed CL method, with a detection limit of 0.03 nmol/L (3 sigma) and relative standard deviations < 3.0% (N = 7), is then implemented to monitor the excretion of cefradine in human urine. After orally administration, the cefradine reaches a maximum value of 1.37 +/- 0.02 mg/mL at 2.0 h in urine, and the total excretion is 4.41 +/- 0.03 mg/mL within 8.0 h. The absorption rate constant k(a), the elimination rate constant k(e), and the half-life t(1/2) are 0.670 +/- 0.008 h(-1), 0.744 +/- 0.005 h(-1), and 0.93 +/- 0.05 h, respectively
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