PREPARATION AND PHOTOLUMINESCENCE PROPERTIES OF RF-SPUTTERED ZnO FILMS

ZnO/Si films were prepared by radio frequency (RF) magnetron sputtering at room temperature. By optimizing the heat treatment conditions, we obtained a good quality film annealed at 700 ºC for longer 60 minutes. This process was monitored carefully by Raman scattering spectroscopy, and X-ray diffraction. The photoluminescence study on this film revealed that only ultraviolet emissions due to donor-acceptor pair (DAP), neutral acceptor-bound exciton (AºX) and donor-bound exciton (DºX) were observed. The intensity and peak position of these emissions depend on the measurement temperature and excitation power density

PREPARATION AND PHOTOLUMINESCENCE PROPERTIES OF RF-SPUTTERED ZnO FILMS

ZnO/Si films were prepared by radio frequency (RF) magnetron sputtering at room temperature. By optimizing the heat treatment conditions, we obtained a good quality film annealed at 700 ºC for longer 60 minutes. This process was monitored carefully by Raman scattering spectroscopy, and X-ray diffraction. The photoluminescence study on this film revealed that only ultraviolet emissions due to donor-acceptor pair (DAP), neutral acceptor-bound exciton (AºX) and donor-bound exciton (DºX) were observed. The intensity and peak position of these emissions depend on the measurement temperature and excitation power density

Linear Query Approximation Algorithms for Non-monotone Submodular Maximization under Knapsack Constraint

This work, for the first time, introduces two constant factor approximation algorithms with linear query complexity for non-monotone submodular maximization over a ground set of size $n$ subject to a knapsack constraint, $\mathsf{DLA}$ and $\mathsf{RLA}$. $\mathsf{DLA}$ is a deterministic algorithm that provides an approximation factor of $6+\epsilon$ while $\mathsf{RLA}$ is a randomized algorithm with an approximation factor of $4+\epsilon$. Both run in $O(n \log(1/\epsilon)/\epsilon)$ query complexity. The key idea to obtain a constant approximation ratio with linear query lies in: (1) dividing the ground set into two appropriate subsets to find the near-optimal solution over these subsets with linear queries, and (2) combining a threshold greedy with properties of two disjoint sets or a random selection process to improve solution quality. In addition to the theoretical analysis, we have evaluated our proposed solutions with three applications: Revenue Maximization, Image Summarization, and Maximum Weighted Cut, showing that our algorithms not only return comparative results to state-of-the-art algorithms but also require significantly fewer queries

Effect of Dzyaloshinskii–Moriya interaction on Heisenberg antiferromagnetic spin chain in a longitudinal magnetic field

Using functional integral method for the Heisenberg antiferromagnetic spin chain with the added Dzyaloshinskii-Moriya Interaction in the presence of the longitudinal magnetic field, we find out expression for free energy of the spin chain via spin fluctuations, from which quantities characterize the antiferromagnetic order and phase transition such as staggered and total magnetizations derived. From that, we deduce the significant effect of the Dzyaloshinskii-Moriya interaction on the reduction of the antiferromagnetic order and show that the total magnetization can be deviated from the initial one under the influence of canting of the spins due to a combination of the Dzyaloshinskii-Moriya interaction and the magnetic field. Besides, the remarkable role of the transverse spin fluctuations due to the above factors on the antiferromagnetic behaviours of the spin chain is also indicated. &nbsp

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Treatment efficiency and membrane fouling of a lab-scale anaerobic membrane bioreactor treating dilute municipal wastewater

The study aims to investigate the application of anaerobic processes coupled with membrane filtration for treating dilute municipal wastewater in Hanoi city