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从社会线索中知觉与解读他人的情绪状态在社会互动与生存进化中扮演着重要的角色。面部表情是最常见的情绪线索。除此之外，生命体的运动也传递着重要的情绪信息，且具有独特的优势。本文系统综述了近年来生物运动情绪研究的进展，并从加工机制、神经基础、亚临床及临床异常等方面进行总结。现有证据表明，生物运动情绪加工依赖整体形状与局部运动的协同作用，并在特定情绪上表现出一定的加工优势，这一过程主要依赖以后侧颞上沟为核心的社会性脑网络。同时，尽管生物运动与面部表情在低水平视觉特征上差异显著，二者却在行为和神经机制上呈现紧密关联，可能共享一套社会情绪知觉系统。此外，生物运动情绪加工能力在亚临床特质个体（高自闭特质、分裂型特质、高焦虑特质）与临床群体（自闭症、精神分裂症、情绪障碍和认知障碍）中表现出不同程度的异常。总的来说，本综述首次整合了生物运动情绪加工在机制及临床异常层面的研究证据，为构建跨刺激类型的社会情绪知觉系统提供了重要基础，也为相关精神与神经疾病的评估与干预指出了潜在的方向。

Generative models for structure-based molecular optimization (SBMO) need supervision that reflects real-world hit-to-lead optimization. Here we introduce LeadFlow, which combines maximum common substructure (MCS) supervision from assay-consistent chemical series, Bayesian Flow Networks for joint generation of continuous 3D coordinates and discrete molecular graphs, and experimental activity-based Direct Preference Optimization (DPO). On MolGenBench, LeadFlow recovered reference active scaffolds in approximately 400 of 600 series and active molecules in approximately 120 series, with active-recovery metrics improving by 1.33- to 2-fold over the best baseline. Scaffold-definition ablations supported improved active chemotype coverage and fused-ring elaboration. Experimental activity-based DPO enriched the generation of higher-activity molecules relative to the pretrained model and a docking-score-based counterpart. In a prospective ClpP agonist optimization campaign, generation followed by prioritization yielded five synthesized compounds with lower mean EC values than the starting compound A1. DC-ClpP-03 reached 33.4 nM, an approximately 180-fold improvement. These results support aligning training supervision and preference objectives with real-world hit-to-lead optimization as a practical strategy for SBMO.

在AI广泛接管组织常规性任务的背景下，员工所承担工作的非常规化程度显著上升，形成AI驱动的工作非常规化(AI-enabled Job Non-Routinization, AIJNR)。现有研究主要将AIJNR理解为员工任务类型的变化，并据此考察其对创造力的影响。然而，AIJNR也在改变员工整体工作的价值来源以及角色与任务边界，使员工需要重新理解自身的工作意义。基于工作特征模型，本研究提出，AIJNR一方面通过提升任务重要性增加员工工作意义感，另一方面通过降低任务完整性减少员工工作意义感；同时，员工基于AI工作重塑会调节AIJNR对其工作特征的影响。研究一基于GitHub协作日志事件(N = 286,773)、案件数据(N = 31,037)和用户层面数据(N = 1,891; N = 764)，初步发现AI出现后工作非常规化程度上升，且AIJNR与任务重要性正相关、与任务完整性负相关。研究二通过视频情景实验(N = 180)，研究三通过三阶段问卷调查(N = 224)进一步验证了上述双路径机制：AIJNR通过任务重要性正向影响工作意义感，通过任务完整性负向影响工作意义感。此外，基于AI工作重塑显著强化AIJNR与任务重要性之间的正向关系，但对AIJNR与任务完整性之间关系的调节作用不显著。最后，本研究讨论了研究发现的理论贡献、实践启示以及未来研究方向。

针对强流重离子加速器装置（HIAF）注入直线加速器（iLinac）在 BRing 注入、T31 供束及直线段调束中的不同事件来源与输出许可需求，研制了基于 White Rabbit 的多模式事件时序触发系统。系统采用三级分层结构，将三种模式的时间信息统一为目标触发时刻，由设备侧 FPGA 完成许可判定及通道调度，实现模式一键切换与延时、脉宽独立调节。现场测试中，1 Hz 和 5 Hz 工况下 Chopper 输出周期标准差分别为 400.06 ns 和 62.645 ns；仪器累计 44136 个时间间隔误差（TIE）样本的标准差为 90.649 ps。LLRF 相对 Chopper 的延时及脉宽响应与设定一致，T31 许可有效时许可监测信号与 Chopper 输出保持周期对应。结果表明，系统在所测条件下实现了百皮秒量级的节点输出稳定性，已用于 HIAF iLinac 现场运行并通过工程验收。

Compact vision-language models (VLMs) now power a growing share of multimodal applications. The benchmarks used to compare them, however, inherit a frontier-centric design: each model is reduced to a single accuracy number, narrowing the inter-model gap on saturated suites and pressing models into low-score bands on harder ones. We introduce PRISM-VLM, a multi-axis discriminative benchmark that scores every item along seven axes covering the recurring failure modes (task quality, behavioral robustness, and capability bottlenecks) and combines them into a single PScore, with items recycled from fifteen public benchmarks. Across compact VLMs from the past two years, PScore separates model pairs more reliably than prior single-axis benchmarks under an item-level paired bootstrap, and surfaces behavioral differences these benchmarks average away. Even models with statistically indistinguishable PScores diverge sharply along the per-axis profile, particularly on sycophancy, which is nearly orthogonal to single-prompt accuracy. We will release the full pipeline, prompts, and per-item annotations.