Numerial calculations in the general dynamical theory of gravitional time dialation

It is well known that, Einsten’s Geometrical Principles and Laws of Gravitation may be used to construct a corresponding theory of Gravitational Time Dilation. In (Howusu, 1991) paper, it was shown how to extend Newton’s Dynamical Principles and Laws based upon the experimental facts of inertia, active and passive masses available today. In this paper, we apply this extended Dynamical Principles and Laws and compare to construct a corresponding theory of Gravitational Time Dilation and compute the ratio of the coordinate time to proper time for both general and dynamical theory of gravitation. Key words: Dynamic, Gravitational Time Dilation, Einstein, Numerical, Theor

Multi-dimensional laser spectroscopy of exciton-polaritons with spatial light modulators

We describe an experimental system that allows one to easily access the dispersion curve of exciton-polaritons in a microcavity. Our approach is based on two spatial light modulators (SLM), one for changing the excitation angles (momenta), and the other for tuning the excitation wavelength. We show that with this setup, an arbitrary number of states can be excited accurately and that re-configuration of the excitation scheme can be done at high speed.Comment: 4 pages, 5 figure

Some issues on toughening, fire retardancy, and wear/scratch damage in polyamide-based nanocomposites

Addition of small percentage of nanoclay layers to polymers significantly improves many of their mechanical, physical and transport properties. Despite these improvements, some issues remain with the resultant nanocomposites and include concerns on fracture toughness, flame retardancy (and thermal stability), and scratch-wear resistance. It is the inadequacy of these specified properties that has curtailed potential applications of this class of new materials. Here, we present the efforts and approaches that were made to understand some facets of these issues in achieving a balance between different mechanical and physical properties, with particular emphasis on our recent and current research findings

Slice Admission control based on Reinforcement Learning for 5G Networks

Network slicing empowers service providers to deploy diverse network slice architectures within a shared physical infrastructure. This technology enables the provision of differentiated services that cater for specific Quality of Service (QoS) requirements of different use cases which need to be adequately supported in 5G networks. By leveraging Network Slicing, operators can effectively meet these diverse requirements and provide customized services to different tenants in a flexible and efficient manner. However, infrastructure providers face a challenging dilemma of the slice admission control regarding whether to accept or reject slice requests. From one perspective, they strive to optimize the utilization of network resources through accepting a significant number of network slices. From another perspective, the availability of network resources is restricted, and it is crucial to fulfil the QoS requirements specified by the network slices. In this research, an Admission Control (AC) Algorithm founded upon Reinforcement Learning mechanisms, specifically Q-Learning (QL), Double Q-Learning (Double-QL), and a proposed mechanism based on Double QL is obtained to overcome this challenge. This algorithm is applied in order to make informed decisions regarding network slice requests. The simulation results demonstrate that the AC algorithm, leveraging the suggested mechanism, surpasses the Double-QL and QL mechanisms in relation to gained profit with average of 8% and 26%, respectively. In case of the acceptance ratio of slice requests, it achieves average of 13% and 28% higher than Double-QL and QL mechanisms, respectively. Finally, it obtains the maximum resource utilization, surpassing Double-QL and QL by 9% and 20%, respectively

Slice Admission control based on Reinforcement Learning for 5G Networks

Network slicing empowers service providers to deploy diverse network slice architectures within a shared physical infrastructure. This technology enables the provision of differentiated services that cater for specific Quality of Service (QoS) requirements of different use cases which need to be adequately supported in 5G networks. By leveraging Network Slicing, operators can effectively meet these diverse requirements and provide customized services to different tenants in a flexible and efficient manner. However, infrastructure providers face a challenging dilemma of the slice admission control regarding whether to accept or reject slice requests. From one perspective, they strive to optimize the utilization of network resources through accepting a significant number of network slices. From another perspective, the availability of network resources is restricted, and it is crucial to fulfil the QoS requirements specified by the network slices. In this research, an Admission Control (AC) Algorithm founded upon Reinforcement Learning mechanisms, specifically Q-Learning (QL), Double Q-Learning (Double-QL), and a proposed mechanism based on Double QL is obtained to overcome this challenge. This algorithm is applied in order to make informed decisions regarding network slice requests. The simulation results demonstrate that the AC algorithm, leveraging the suggested mechanism, surpasses the Double-QL and QL mechanisms in relation to gained profit with average of 8% and 26%, respectively. In case of the acceptance ratio of slice requests, it achieves average of 13% and 28% higher than Double-QL and QL mechanisms, respectively. Finally, it obtains the maximum resource utilization, surpassing Double-QL and QL by 9% and 20%, respectively

Quitting smoking and mortality: risk of all-cause mortality decreased sharply in 5-9 years after quitting smoking among Chinese

e-PosterLi Ka Shing Faculty of Medicine Frontiers SeriesConference Theme: MOOCs in Postmodern Asia (Oct 27, 2014) and Big Data and Precision Medicine (Oct 28, 2014)postprin

A CrC^{r} Closing Lemma for a Class of Symplectic Diffeomorphisms

We prove a $C^r$ closing lemma for a class of partially hyperbolic symplectic diffeomorphisms. We show that for a generic $C^r$ symplectic diffeomorphism, $r =1, 2, ...,$, with two dimensional center and close to a product map, the set of all periodic points is dense