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本文立足文旅新时代五大核心变革特征，剖析当下游客兼具搜寻、共创、表演、生产、评审等多重动态角色，提出 “游客即产品” 核心理念。该理念指出游客不再是单纯消费终端，其 UGC 内容、互动行为、社交传播共同构成文旅核心价值资产。文章从市场调研、产品、设施、商业模式、运营预演五大维度，阐述该理念在景区规划策划阶段的落地路径，主张规划从空间建造转向游客体验剧本设计，搭建激发游客价值共创的开放场景，同时点明过度表演化、数据隐私等现实风险，为数字化文旅规划、运营提供全新理论范式与实践思路。

细胞骨架在调节膜功能方面发挥着重要作用。膜联蛋白是一个进化保守的多基因家族，广泛分布于真核生物中，主要与细胞膜相互作用，同时也参与众多的蛋白质-蛋白质相互作用。膜联蛋白与细胞骨架肌动蛋白之间的相互作用在细胞多种功能中均发挥着重要调控作用。本文系统综述了膜联蛋白与细胞骨架肌动蛋白相互作用的分子基础，总结了膜联蛋白A1、A2、A3、A5、A6等家族成员与肌动蛋白骨架的互作模式及其在细胞迁移、膜转运、信号转导、屏障维持及质膜修复等过程中的功能，并展望了该领域未来的研究方向。本综述指出，膜联蛋白作为连接钙信号与细胞骨架动态的关键分子枢纽，在调节及稳定膜-细胞骨架连接中发挥着不可替代的重要作用。

Topological data analysis (TDA) provides a powerful framework for extracting information about the shape of complex, unstructured data, but the classical cost of computing high dimensional topological features limits its application. Quantum algorithms for TDA offer a route around this bottleneck, yet existing approaches typically focus on exact or high precision Betti number estimation, making the regime for practical quantum advantage appear narrow. Here, we instead frame quantum TDA as a feature-extraction method for downstream data analysis by extracting low-order spectral information from the combinatorial Laplacian as a proxy for high-dimensional topology. We support this perspective from both the application and algorithmic sides. First, we show that higher-order TDA features improve predictive performance in two time-series applications: functional MRI analysis for neurodegenerative disease classification and financial time-series analysis for identifying market instability. Second, we develop a moment-based quantum algorithm and show that low-order moments, including the relative trace, are strongly correlated with high-dimensional Betti information, even when the relative Betti number is small. Finally, we present circuit constructions, resource estimates, quantum-classical crossover projections, and experimental results from a Barium development system similar to the forthcoming IonQ Tempo line, extracting Laplacian-derived observables from graph instances and quantitatively comparing them with exact Betti information. Together, these results establish quantum TDA as a practical approach for extracting topological features from classically challenging data

Pre-training followed by fine-tuning has become the dominant recipe for learning performant policies, and in value-based reinforcement learning (RL) this raises a natural question: given a pretrained policy, should the Q-function be pretrained on offline data too? Conventional wisdom suggests it should, but recent results show that online RL with a randomly-initialized Q-function can result in highly performant and reliable policies without needing to pretrain the Q-function. In this paper, we systematically study whether pretraining the Q-function actually helps when fine-tuning on top of a pretrained base policy. We find, surprisingly, that naive Q-function pretraining often provides little benefit over random initialization. We show this stems from a fundamental mismatch: the Q-function learned during pretraining targets the pretrained policy's Q-function, not the Q-function that online fine-tuning converges to, and this gap persists even after offline value maximization. Motivated by this finding, we propose Initialization via Policy Ensemble (IPE), a simple method that trains multiple diverse policies and uses their pooled rollouts to bootstrap the Q-function learning in online RL. Across a suite of challenging continuous control benchmarks, IPE yields an average 1.26x improvement in fine-tuning performance over naive Q-function pre-training.

Recently, a fit of charmless $B\to PP$ decays ($B \in \{B^0, B^+, B_s^0\}$, $P \in \{ π, K, η, η' \}$) to the latest data was performed under the assumption of flavour SU(3) symmetry [SU(3)$_F$]. It was found that there is a $4.1σ$ disagreement with the SU(3)$_F$ limit of the Standard Model [$\rm SM_{SU(3)_F}$]. In this paper, we extend this analysis to charmless $B \to VV$ decays ($V \in \{ρ, K^*, ϕ, ω\}$). The fit examining $B \to ρK^*$ decays, assuming only isospin symmetry, is found to be acceptable. When we fit to $B \to VV$ decays with $V \in \{ ρ, K^* \}$ within SU(3)$_F$, we find a $5.2σ$ discrepancy with SM$_{SU(3)_F}$. Finally, when $B \to VV$ decays with $V \in \{ ρ, K^*, ϕ, ω\}$ are considered, the discrepancy grows to $>7σ$. The theoretical input in this analysis is modest, so our results are quite rigorous, group theoretically, and hold almost exactly in the SU(3)$_F$ limit. Although it seems unlikely that the introduction of $\sim 30$% SU(3)$_F$-breaking effects can account for this discrepancy, this must be verified.