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针对大型飞机全机静力试验中承载结构大变形特征，提出了一套完整的飞机大变形结构强度试验方案，开展了大变形试验误差分析和试验系统设计要求研究，突破了试验件载荷处理、飞机支持、载荷施加、配平设计、姿态修正等关键技术，最后以某型飞机大变形试验工况为研究对象完成了试验技术的应用与验证。试验结果表明，提出的试验方案科学合理，可以精确完成机翼大变形状态下考核结构的强度验证，验证了技术方案的有效性，为大型飞机大变形试验技术标准化提供了重要参考。

提振消费、扩大内需已被列为我国经济工作首要任务，如何通过有效的助推方式提振国民消费成为学界和政界共同关注的焦点。然而，以物质激励为主的传统行为干预策略在长期影响方面存在局限，如何利用有效的助推策略实现长期的提振消费效果是当下的研究缺口。本研究从时间心理学的研究视角出发，综合运用二手数据分析、实验室实验、大规模问卷调查、现场实验等混合方法，系统揭示时间观如何通过刺激个体体验消费需求进而助推消费倾向的影响机制。研究发现：(1)相对于循环时间观，线性时间观能更有效地促进个体消费倾向，而体验消费需求在二者间起中介作用；(2)时间范围对上述影响存在调节效应，有限时间范围会增强时间观对消费倾向的影响，而开放时间范围则削弱上述影响。本文将时间观助推纳入扩大内需研究情境，拓展了框架效应研究边界，研究结论为我国提振消费策略提供了实证支持和实践指导，对制定长期提振消费政策有重要的借鉴意义。

We investigate Coulomb spin liquids in classical spin-3/2 ice and show that the enlarged on-site Hilbert space gives rise to a qualitatively new class of such phases. Beyond the conventional magnetic monopoles of spin-1/2 ice, the system hosts additional low-energy crystal-field excitations, whose interplay with monopoles significantly modifies both equilibrium and non-equilibrium properties. Following a thermal quench, we find a pronounced dynamical arrest manifested in an exponentially long-lived {athermal} plateau in spin autocorrelations. This constitutes a rare example of dynamical arrest in a short-range interacting, disorder-free system. We demonstrate that the arrested dynamics originate from novel composite excitation structures unique to spin-3/2 ice and from kinetically constrained relaxation pathways that require activated processes. Our results establish higher-spin ice as a fertile platform for realising unconventional Coulomb spin liquids and dynamical arrest without quenched disorder.

A dataset server must often distribute the same large payload to many clients, incurring massive communication costs. Since clients frequently operate on diverse hardware and software frameworks, transmitting a pre-trained model is often infeasible; instead, agents require raw data to train their own task-specific models locally. While dataset distillation attempts to compress training signals, current methods struggle to scale to high-resolution data and rarely achieve sufficiently small files. In this paper, we propose Pseudo-Labels as Data (PLADA), a method that completely eliminates pixel transmission. We assume agents are preloaded with a large, generic, unlabeled reference dataset (e.g., ImageNet-1K, ImageNet-21K) and communicate a new task by transmitting only the class labels for specific images. To address the distribution mismatch between the reference and target datasets, we introduce a pruning mechanism that filters the reference dataset to retain only the labels of the most semantically relevant images for the target task. This selection process simultaneously maximizes training efficiency and minimizes transmission payload. Experiments on 10 diverse datasets demonstrate that our approach can transfer task knowledge with a payload of less than 1 MB while retaining high classification accuracy, offering a promising solution for efficient dataset serving.

Bio-inspired event cameras have recently attracted significant research due to their asynchronous and low-latency capabilities. These features provide a high dynamic range and significantly reduce motion blur. However, because of the novelty in the nature of their output signals, there is a gap in the variability of available data and a lack of extensive analysis of the parameters characterizing their signals. This paper addresses these issues by providing readers with an in-depth understanding of how intrinsic parameters affect the performance of a model trained on event data, specifically for object detection. We also use our findings to expand the capabilities of the downstream model towards sensor-agnostic robustness.