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

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

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