UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

最优国有化水平与教育领域混合所有制办学:基于双寡头垄断模型的研究

混合所有制办学是教育领域发展的重要方向,国有化水平至关重要。构建由两家混合所有制教育机构组成的双寡头垄断模型,通过动态序贯博弈探索最优的国有化水平,发现最优国有化水平是动态变化的,受教育产品外部性以及国有和非国有教育资本效率的影响,完全的国有化和混合所有制都可以是最优的。以此为理论依据,对中国不同层次教育领域的混合所有制办学路径进行因地制宜的探索,解析其发展面临的诸多障碍,并提出试点先行、分类推进教育领域混合所有制办学改革;大力完善教育资本市场,促进资本高效流动;完善法律法规,提供制度依据;优化学校治理结构,探索多元化的办学模式等对策建议。

Impact of product heterogeneity and soft budget constraint on excess capacity in Chinese energy industry based on the duopoly model

Chinese energy industries are facing serious problems such as excess capacity,homogeneous product,and soft budget constraint.This paper provides a duopoly model to investigate the influence of heterogeneity and soft budget constraint on production capacity decision and internal action mechanism,respectively,under Cournot and Bertrand competitions,which reveals the formation mechanism of excess capacity.We conclude that excess capacity would exist when the products are not wholly heterogeneous under Cournot competition,and the higher level of the soft budget constraint or the more homogeneous the products are,the worse the excess capacity will be.The insufficient capacity would exist provided that products are not wholly heterogeneous under Bertrand competition,and the higher level of soft budget constraint or the more homogeneous the products are,the more insufficient capacity will be.Both soft budget constraint and product heterogeneity mutually affect to decision-making of capacity and output.

High micropore-utilization carbon aerogel with controlled nanostructures via adjusting aggregation state of polyacrylonitrile for energy storage systems

Designing and optimizing the pore structure of porous carbon electrodes is essen-tial for diverse energy storage systems.In this study,an innovative approach spray phase-inversion strategy was developed for the rapid and efficient fabri-cation of controlled porous carbon aerogel.Moreover,the aggregation structure of polyacrylonitrile is controlled by adjusting the Hansen’s solubility parameter,thereby regulating the electrode material structure.Furthermore,the theoretical analysis of the spray phase-inversion process revealed that this regulation pro-cess is jointly regulated by solvent hydrodynamic diameter and phase-inversion kinetics.Through optimization,a novel porous carbon material was obtained that exhibited excellent performance as an electrode material.When utilized in supercapacitors for energy storage,it demonstrated a high specific capacitance of 373.1 F g^(-1) in a 6 M KOH electrolyte solution.Simultaneously,it has been observed that the preparation strategy for porous electrodes offers notable advan-tages in terms of excellent designability,broad universality,simplicity,and high efficiency,thereby holding promise for large-scale fabrication of diverse porous electrode materials and various types of electrodes for diverse energy storage applications.