HTTP/3 (H3) has experienced significant growth and extensive adoption in various scenarios, especially in Content Delivery Networks (CDNs). Over the past few years, there have been numerous insightful studies on its deployment in industrial CDNs. However, these studies often separately analyze H3 and CDN, overlooking their synergistic integration. In this work, we explore the applicability of H3 in CDN from a holistic perspective. We analyze 325 websites hosted by seven CDN providers and identify three key characteristics where CDN align perfectly with H3's strengths. Firstly, CDN resources dominate the composition of webpages, where enabling H3 can amplify H3's benefits in connection acceleration. Secondly, CDN providers also exhibit a dominant characteristic, with the majority of CDN resources hosted by a few large providers. This phenomenon makes different webpages share the same provider. When browsing consecutively, H3 helps to skip the connection phase by resuming the connections to the same CDN provider across pages. Thirdly, H3 mitigates the congestion problem on webpages serving multiple CDN resources. This work provides a deeper insight into the applicability of H3 in large-scale distributed systems like CDNs, holding promise for informing the development and optimization of industrial H3.

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Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on an entire social network's information. However, for privacy-conscious users or newly-registered users, such information is not easily accessible. To address this issue, we present DeepPick, a novel framework that considers both the generalization and specialization in the detection task of OUs. For generalization, we introduce deep neural networks to capture dynamic features of the users. For specialization, we leverage the traditional descriptive features to make use of public information about users. Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art.@en

The red afterglow of current rare-earth-activated long persistent luminescence (LPL) phosphors is largely still less than 6 h, in contrast to the 20 or 30 h long blue- or green-emitting ones, becoming the main obstacle to realize their multiscenario applications in practice. Herein, we report a rare-earth-free red-emitting LPL phosphor SrGa₂O₄:Cu²⁺that can exhibit an afterglow at about 622 nm lasting over 30 h, which can largely match with the luminance of current blue- or green-emitting LPL phosphors. We find that the Cu²⁺ion could be charged by ultraviolet light from 280 to 420 nm, and the emission has a very broad band with a full width at half-maximum of about 150 nm. Combining the thermoluminescence measurement and the first-principles calculation, we find that the O vacancies and the -1 charged Ga vacancies could store the holes and contribute to the LPL of SrGa₂O₄:Cu²⁺. Our results may dramatically promote and expand its potential applications and stimulate the research of the multicolor LPL phosphors in future.

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