University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Thermoelectric properties of monolayer MSe2 (M = Zr, Hf): low lattice thermal conductivity and a promising figure of merit.

Abstract: Monolayer transition-metal dichalcogenides (TMDCs) MX2 (M = Mo, W, Zr, Hf, etc; X = S, Se, Te) have become well-known in recent times for their promising applications in thermoelectrics and field effect transistors. In this work, we perform a systematic study on the thermoelectric properties of monolayer ZrSe2 and HfSe2 using first-principles calculations combined with Boltzmann transport equations. Our results point to a competitive thermoelectric figure of merit (close to 1 at optimal doping) in both monolayer ZrSe2 and HfSe2, which is markedly higher than previous explored monolayer TMDCs such as MoS2 and MoSe2. We also reveal that the higher figure of merits arise mainly from their low lattice thermal conductivity, and this is partly due to the strong coupling of acoustic modes with low frequency optical modes. It is found that the figure of merits can be better optimized in n-type than in p-type. In particular, the performance of HfSe2 is superior to ZrSe2 at a higher temperature. Our results suggest that monolayer ZrSe2 and HfSe2 with lower lattice thermal conductivity than usual monolayer TMDCs are promising candidates for thermoelectric applications.

Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

Abstract: Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users’ privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

The impact of power structure on the retail service supply chain with an O2O mixed channel

Abstract: While the Internet has provided a new means for retailers to reach consumers, it has fundamentally changed the dynamic of competition in the retail service supply chain. The mix of offline and online channels adds a new dimension of competition, and one central issue of this competition is the pricing strategy between the two channels. How to set prices for both online and offline channels? What is the impact of the supply chain power structure on pricing decisions and the performance? This research aims to address these questions by focusing on a retail service supply chain with an online-to-offline (O2O) mixed dual-channel. From the Supplier-Stackelberg, Retailer-Stackelberg, and Nash game theoretical perspectives, we obtain the optimal prices and maximum profits for both the retailer and supplier under different power structures. The analysis result provides important managerial implications, which will be beneficial to retailers to develop proper pricing strategies.

Ranking in evolving complex networks

Abstract: Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Well-established ranking algorithms (such as the popular Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. The recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of real network traffic, prediction of future links, and identification of highly-significant nodes.