Effect of 0.1 wt pct Dy Addition on Morphology and Compressive Behavior of Cast NiA1-28Cr-5.SMo-0.2Hf

Effect of 0.1 wt pct Dy addition on microstructure and compressive behavior of NiAl-28Cr-5.8Mo-0.2 Hf eutectic alloy was investigated. The results showed that remarkable lamellar refinement can be achieved with the addition of 0.1 wt pct Dy. The Dy addition results in the decrease in Young＇s modulus of alloy and the.enhancement of the compressive strength and ductility of alloy at all testing temperatures. The lamellar refinement, the increased dislocation networks located at the interfaces of NiAl/Cr（Mo） and the strengthening of cell boundary are benefical to the improvement of compressive properties of the alloy.

Synthesis,phase transformation and applications of CeCO_(3)OH:A review

CeCO_(3)OH has a unique crystal structure and excellent optical,electronic and catalytic properties,which has been widely investigated for many applications.Interestingly,ceria obtained from CeCO_(3)OH has a morphology that is similar to that of the precursor,and the CeO_(2)-based products obtained from CeCO_(3)OH exhibit outstanding properties,such as catalytic performances,owing to their designed morphology and oxygen vacancies(OVs).To introduce CeCO_(3)OH into a wider range of potential researchers,we first systematically review the physico-chemical properties,synthesis,reaction and morphology tuning mechanism of CeCO_(3)OH,and summarize the conversion behavior from CeCO_(3)OH to ceria.Then,we thoroughly survey the applications of CeCO_(3)OH and its conversion products.Suggestions for further investigations of CeCO_(3)OH are also made in this review.It is hoped that the exhaustive co mpilation of the valuable properties and considerable potential investigations of CeCO_(3)OH will promote further applications of CeCO_(3)OH and CeO_(2)-based functional materials.

Adaptive cache policy optimization through deep reinforcement learning in dynamic cellular networks

We explore the use of caching both at the network edge and within User Equipment(UE)to alleviate traffic load of wireless networks.We develop a joint cache placement and delivery policy that maximizes the Quality of Service(QoS)while simultaneously minimizing backhaul load and UE power consumption,in the presence of an unknown time-variant file popularity.With file requests in a time slot being affected by download success in the previous slot,the caching system becomes a non-stationary Partial Observable Markov Decision Process(POMDP).We solve the problem in a deep reinforcement learning framework based on the Advantageous Actor-Critic(A2C)algorithm,comparing Feed Forward Neural Networks(FFNN)with a Long Short-Term Memory(LSTM)approach specifically designed to exploit the correlation of file popularity distribution across time slots.Simulation results show that using LSTM-based A2C outperforms FFNN-based A2C in terms of sample efficiency and optimality,demonstrating superior performance for the non-stationary POMDP problem.For caching at the UEs,we provide a distributed algorithm that reaches the objectives dictated by the agent controlling the network,with minimum energy consumption at the UEs,and minimum communication overhead.

Distributionally Robust Learning Based on Dirichlet Process Prior in Edge Networks

In order to meet the real-time performance requirements,intelligent decisions in Internet of things applications must take place right here right now at the network edge.Pushing the artificial intelligence frontier to achieve edge intelligence is nontrivial due to the constrained computing resources and limited training data at the network edge.To tackle these challenges,we develop a distributionally robust optimization(DRO)-based edge learning algorithm,where the uncertainty model is constructed to foster the synergy of cloud knowledge and local training.Specifically,the cloud transferred knowledge is in the form of a Dirichlet process prior distribution for the edge model parameters,and the edge device further constructs an uncertainty set centered around the empirical distribution of its local samples.The edge learning DRO problem,subject to these two distributional uncertainty constraints,is recast as a single-layer optimization problem using a duality approach.We then use an Expectation-Maximization algorithm-inspired method to derive a convex relaxation,based on which we devise algorithms to learn the edge model.Furthermore,we illustrate that the meta-learning fast adaptation procedure is equivalent to our proposed Dirichlet process prior-based approach.Finally,extensive experiments are implemented to showcase the performance gain over standard approaches using edge data only.