Effect of Decoherence on the Dynamics of Bose-Einstein Condensates in a Double-well Potential

We study the dynamics of a Bose-Einstein condensate in a double-well potential in the mean-field approximation. Decoherence effects are considered by analyzing the couplings of the condensate to environments. Two kinds of coupling are taken into account. With the first kind of coupling dominated, the decoherence can enhance the self-trapping by increasing the damping of the oscillations in the dynamics, while the decoherence from the second kind of condensate-environment coupling leads to spoiling of the quantum tunneling and self-trapping.Comment: for color figures, see PR

THE EFFECT OF DIFFERENT EXTERNAL ELASTIC COMPRESSION ON MUSCLE STRENGTH, FATIGUE, EMG AND MMG ACTIVITY

The purpose of this study was to quantify the effects of three different compression conditions on (a) performance of muscle strength/power and fatigue in lower extremity, and (b) the responses of electromyography (EMG) and mechanomyography (MMG) of rectus femoris (RF) under repeated concentric muscle actions. All subjects (N=12) performed maximal voluntary contractions (MVC) and consecutive, maximal isokinetic knee extension movements at 60°/s & 300°/s velocities with three different compression conditions. The results indicated that local elastic compression of lower extremity, while not significant in improving isokinetic strength in short period, may have a positive effect on fatigue by helping maintain long-term force production through altering muscle activity in high-velocity of locomotion

High-frequency shoot regeneration of nodal explants from Tetrastigma hemsleyanum Diels et Gilg: A valuable medicinal plant

This paper describes the shoot regeneration of nodal segments from a medicinal plant, Tetrastigma hemsleyanum Diels et Gilg (Vitaceae). The highest number of shoots (7.27 shoots per explant) was observed in MS medium supplemented with 4 mg/l BA after six weeks of inoculation. 2 mg/l BA in combination with 0.1 mg/l NAA not only induced shoot proliferation but also increased shoot length. Well-developed shoots were rooted on half strength MS medium supplemented with 2 mg/l IBA with 100% rooting and 85% of the regenerated plantlets survived before been transferred to field conditions.Key words: Medicinal plant, nodal explants, shoot regeneration, Tetrastigma hemsleyanum

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

Modelling and control of the flame temperature distribution using probability density function shaping

This paper presents three control algorithms for the output probability density function (PDF) control of the 2D and 3D flame distribution systems. For the 2D flame distribution systems, control methods for both static and dynamic flame systems are presented, where at first the temperature distribution of the gas jet flames along the cross-section is approximated. Then the flame energy distribution (FED) is obtained as the output to be controlled by using a B-spline expansion technique. The general static output PDF control algorithm is used in the 2D static flame system, where the dynamic system consists of a static temperature model of gas jet flames and a second-order actuator. This leads to a second-order closed-loop system, where a singular state space model is used to describe the dynamics with the weights of the B-spline functions as the state variables. Finally, a predictive control algorithm is designed for such an output PDF system. For the 3D flame distribution systems, all the temperature values of the flames are firstly mapped into one temperature plane, and the shape of the temperature distribution on this plane can then be controlled by the 3D flame control method proposed in this paper. Three cases are studied for the proposed control methods and desired simulation results have been obtained