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在食品安全问题与数字化转型升级的双重背景下，供应链透明化已从餐饮企业的风险管理工具，演进为品牌形象建设的核心战略资产。本文立足于国家推动餐饮产业数字化的政策环境，首先分析了连锁餐饮企业的数字化实践现状。进而，论文深入探讨了供应链透明化的多层本质与公众的隐性需求，系统阐述了其通过战略性信任构建与品牌价值叙事对品牌形象的积极作用。通过“西贝事件”的反例，本研究揭示了数字化能力缺失导致的透明化困境。最终，论文论证了数字化技术对实现实质透明的关键推动作用，并提出企业应通过构建一体化数字平台与主动对接监管，将供应链打造为品牌信任的基石，从而在数字化竞争中赢得可持续优势。

基于钾离子通道折纸风车模型的结构启发，结合DNA分子的多链折叠特性、离子调控机制及遗传信息传递规律，提出四链DNA倒锥形风车模型，突破传统双螺旋模型的线性结构局限。该模型以四链DNA为核心骨架，通过“双梯倒扣”形成类风车的辐射状空间构象，链间通过氢键、碱基堆积作用及离子调控稳定，同时依托“单梯遗传”机制实现遗传信息的精准传递。本文详述模型的构建溯源、结构特征、遗传机制及潜在生物学意义，为DNA多链结构与遗传调控研究提供新视角。

针对传统应急决策系统存在的数据来源单一、智能化程度低、决策路径固化等问题,我们提出一种基于多Agent协作的智能应急决策支持系统。系统采用了我们提出的Plan-Execute-Monitor循环架构,集成Web搜索、知识图谱查询和地理信息服务等多源信息融合模块,构建了思维树推理驱动的多智能体协作机制。通过引入计划导向路径生成、多维度进展评估和自适应执行监控等关键技术,解决了传统多智能体系统指令遵循失败、步骤重复和上下文丢失等问题。我们参考GAIA评估框架,基于政府开源文件和网络爬虫自主构建了包含135个任务的GAIA-应急管理领域数据集。在该数据集上的实验结果表明,PEM架构准确率达到48.7\%,比传统迭代搜索架构的28.6\%高出20.1个百分点,平均执行时间降低63.7\%,验证了系统的有效性。

Diffusion Large Language Models (dLLMs) are rapidly emerging alongside autoregressive models as a powerful paradigm for complex reasoning, with reinforcement learning increasingly used for downstream alignment. Existing trajectory-based RL methods uniformly allocate policy gradients across denoising steps, implicitly treating all steps as equally important. We challenge this assumption by analyzing trajectories with several step-level metrics: entropy-based uncertainty, Confidence-Margin (CM) uncertainty, and Rate of Entropy Change (RoEC). These reveal structured "zones of confusion": transient spikes in uncertainty and instability that strongly predict final success or failure, while most steps remain stable. We propose Adaptive Trajectory Policy Optimization (ATPO), a lightweight step-selection strategy that dynamically reallocates gradient updates to these high-leverage steps without changing the RL objective, rewards, or compute budget. Using a hybrid RoEC+CM rule, ATPO delivers substantial gains in reasoning accuracy and training stability across benchmarks, showing that exploiting trajectory dynamics is key to advancing dLLM RL.

Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accuracy, especially for domain-specific or context-dependent scenarios. For this, this paper proposes a Physics-Constrained Multimodal Data Evaluation (PCMDE) metric combining large language models with reasoning, knowledge based mapping and vision-language models to overcome these limitations. The architecture is comprised of three main stages: (1) feature extraction of spatial and semantic information with multimodal features through object detection and VLMs; (2) Confidence-Weighted Component Fusion for adaptive component-level validation; and (3) physics-guided reasoning using large language models for structural and relational constraints (e.g., alignment, position, consistency) enforcement.