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心理预算是约束支出决策的关键因素，区分其作用范围是评估约束效果的前提，但现有文献尚未对其进行系统分析。为明确心理预算对支出决策的约束空间，研究构建了包含认知标签构建、支出识别和预算参考阶段的约束过程模型。研究指出，心理预算系统的约束范围有限、约束条件可塑：那些无法归类到预算账户、未在决策前完成识别，以及效用高于超支边界的支出难以被心理预算约束；决策者可能通过调整支出的归类改变预算约束条件。而且，该系统还具备提升决策效率和收益的双重适应价值。研究完善了心理预算的理论体系，为预算约束下的支出决策动机分析提供了新框架，对个体预算管理、企业营销及公共政策制定具有参考价值。

周晓林、孙兆辉、胡捷（2026）提出中国心理学急需突破“自然科学—社会科学”的双重属性框架，系统强化“工学属性”，推动学科整体从“解释世界”转向“改造世界”，并建设自主工程体系与人才培养链条，以回应国家战略与社会服务需求。本文对该倡议作初步评述，认为其转型关键在于补齐心理学从机制证据到系统交付的转化链条，以回应可重复性与外推性挑战、研究—实践转化鸿沟、数智时代对学科能力的需求变化、范式化研究传统的局限以及技术导向与价值转化之间的张力。基于工程逻辑，本文提出“机制科学—转化科学—系统工程”的三层路径与“证据—实施—治理”闭环：机制层产出可证伪模型、可迁移指标与边界条件；转化层实现构念操作化、测量/算法封装与可复用模块；系统层面向真实场景完成部署、监测运维、审计合规与风险处置。本文进一步指出，工程化转型应是能力扩容而非对基础研究的替代，其落地取决于人才培养、评价体系与项目组织的协同重构。上述框架旨在为中国心理学的工程化转型提供可讨论的分析视角与制度化路径。

RF cavities loaded with magnetic alloy (MA) or ferrite ring cores are used in synchrotrons and storage rings if the maximum RF frequency is in the order of a few MHz. A simple model for the description of cavities of this type is derived. The most important parameters are defined, and some properties of the material and of the cavity are summarized. Different cavity configurations, development aspects, and several practical topics are covered. A few specific ferrite- and MA-loaded cavity systems are discussed as examples.

In this work we consider basic principles and problems of the standard quantum mechanical formalism. Especially we consider final measurement or detection procedure (collapse) as a quantum-classical continuous phase transition with spontaneous (non-dynamical) unitary symmetry (superposition) breaking (effective hiding). Practically, collapse as an effective (non-absolute) phenomena can be considered as an especial case of the general formalism of spontaneous symmetry breaking (with applications in many different domains of the physics, e.g. in elasticity of rigid bodies, quantum theory of ferromagnetism, quantum theory of electro-weak interactions as well as in chaotic inflation cosmology). General theory of the detection procedure (collapse) we consider in real experiment of single photon interference at beam splitter with final detection of the photon coordinate by usual photographic plate.

Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressive (AR) LLMs, typically preserve attention sink tokens because AR sinks serve as stable global anchors. We show that this assumption does not hold for DLMs: the attention-sink position exhibits substantially higher variance over the full generation trajectory (measured by how the dominant sink locations shift across timesteps), indicating that sinks are often transient and less structurally essential than in AR models. Based on this observation, we propose ${\bf \texttt{Sink-Aware Pruning}}$, which automatically identifies and prunes unstable sinks in DLMs (prior studies usually keep sinks for AR LLMs). Without retraining, our method achieves a better quality-efficiency trade-off and outperforms strong prior pruning baselines under matched compute. Our code is available at https://github.com/VILA-Lab/Sink-Aware-Pruning.