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制造业企业的数字化转型成为全球趋势和经济发展的关键路径。然而，我国制造业企业在数字化转型过程中却面临着核心技术受制于人、转型动力不足等现实困境。现有数字化转型障碍研究多聚焦单一维度障碍分析，缺乏跨维度、分阶段的动态分析。为此，本文基于TOE理论框架，通过文献研究法梳理59篇中英文文献，系统识别制造业数字化转型各阶段的差异化障碍。研究发现，障碍因素呈现"基础能力薄弱→协同效能受限→生态价值断层"的递进式传导规律，数据治理能力、流程穿透能力与生态协同能力分别构成各阶段核心决定因素，并据此提出分阶段治理策略。研究结果为制造业企业分阶段实施数字化转型提供理论支撑与实践参考。

面对老龄化给工作场所带来的深刻影响，如何充分开发和利用年长劳动力资源成为组织的当务之急。年长员工向年轻同事寻求知识(简称年长员工知识寻求)，作为年长员工实现工作中成功老龄化的重要方式，逐渐受到了学界和业界的关注，但是对于年长员工知识寻求的前因及其驱动机制尚缺乏系统性的探讨。鉴于此，本文首先明确年长员工作为知识寻求者的行为主体角色，并在梳理总结年长员工知识寻求前因的基础上，结合计划行为理论，尝试构建年长员工知识寻求前因及驱动机制的理论框架：分别提出以寻求态度、主观规范与行为控制为核心的驱动机制，以及员工个体因素和组织情境因素的约束条件。未来研究需立足于年长员工的行为主体角色，结合先进研究方法深化驱动机制探索，进而拓展组织中代际知识转移的研究视角和理论成果。

Precise knowledge of causal directed acyclic graphs (DAGs) is assumed for standard approaches towards valid adjustment set selection for unbiased estimation, but in practice, the DAG is often inferred from data or expert knowledge, introducing uncertainty. We present techniques to identify valid adjustment sets despite potential errors in the estimated causal graph. Specifically, we assume that only the skeleton of the DAG is known. Under a Bayesian framework, we place a prior on graphs and wish to sample graphs and compute the posterior probability of each set being valid; however, directly doing so is inefficient as the number of sets grows exponentially with the number of nodes in the DAG. We develop theory and techniques so that a limited number of sets are tested while the probability of finding valid adjustment sets remains high. Empirical results demonstrate the effectiveness of the method.

We study quantum differential privacy (QDP) by defining a notion of the order of informativeness between two pairs of quantum states. In particular, we show that if the hypothesis testing divergence of the one pair dominates over that of the other pair, then this dominance holds for every $f$-divergence. This approach completely characterizes $(\varepsilon,δ)$-QDP mechanisms by identifying the most informative $(\varepsilon,δ)$-DP quantum state pairs. We apply this to analyze the stability of quantum differentially private learning algorithms, generalizing classical results to the case $δ>0$. Additionally, we study precise limits for privatized hypothesis testing and privatized quantum parameter estimation, including tight upper-bounds on the quantum Fisher information under QDP. Finally, we establish near-optimal contraction bounds for differentially private quantum channels with respect to the hockey-stick divergence.

We introduce a witness-based framework for certifying quantum temporal correlations via the pseudo-density matrix (PDM) formalism, which is a spatiotemporal generalization of the density matrix. We define spatial incompatibility (SI) as the minimum distance between a PDM and valid density matrices. For trace-norm distance, we show that this reduces to the PDM's negativity, enabling the construction of experimentally accessible SI witnesses. We derive a tight bound on SI for quantum channels and analyze the respective roles of state and channel coherence in witnessing SI. Our approach, unlike the LG framework, exploits measurements that generate coherence through state disturbance. We further show that channels satisfying the LG inequality for incoherent states can still exhibit detectable SI, demonstrating that measurement disturbance enhances the certification of temporal correlations.