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还原论医学将人体拆解为孤立器官、细胞、分子，试图通过局部机制解释疾病并构建治疗方案，但其根本缺陷在于忽视了生命作为代谢元的整体因果闭合性。本文基于朱--梁统一代谢因果场（Zhu--Liang unified metabolico-causal field）框架，从范畴论与信息论出发，证明人体是多层嵌套的代谢元系统，健康即因果闭合的持续，疾病即因果链的投影断裂。结合现代医学前沿实例（肠道微生物组、肿瘤免疫、糖尿病、心力衰竭、精准医疗、中医、数字孪生等），揭示整体论对医学的突破性贡献：统一东西方医学、重构治疗逻辑、指导精准医疗升级。最终结论：医学的根基只能是整体论，未来医学必须从整体出发，否则因果链必然断裂。

本文在范畴论框架下严格证明“整体是函数，部分是子函数”这一命题。运用 Yoneda 嵌入，任意对象 $X$（整体）与其可表函子 $h_X = \Hom(X,-)$ 等同，该函子可视为一种“函数”。对于子对象 $i: A \hookrightarrow X$（部分），嵌入诱导自然变换 $\res: h_X \to h_A$，其分量为限制映射 $f \mapsto f \circ i$。从而部分对应函子 $h_A$，限制自然变换精确表达了部分作为整体的子函数。该结果不依赖于任何具体数学结构，揭示了整体–部分关系的本质函子性。本文进一步在范畴论框架下证明：存在是无限因果、不可还原、是同构，将存在论奠基于精确的数学结构之上。

核酸检测技术在传染病诊断、食品安全监控与环境微生物监测等领域发挥着不可替代的作用。传统核酸检测方法如聚合酶链式反应（PCR）虽灵敏度高，但对精密仪器和专业操作人员的依赖严重制约了其在基层与现场环境中的推广应用。近年来，基于CRISPR-Cas系统的新一代核酸检测技术迅速发展，其中Cas12和Cas13蛋白凭借其独特的反式切割活性，分别催生了DETECTR、HOLMES和SHERLOCK等代表性检测平台。这些平台将等温扩增技术与CRISPR靶向识别相结合，能够在恒温条件下于30至60分钟内完成从样品到结果的全流程检测，灵敏度可达阿摩尔级别。本文围绕CRISPR-Cas12/13系统的分子作用机制，系统综述了其在传染性疾病快速诊断、食品安全检测及信号读出技术集成等方面的最新研究进展，并就临床转化路径、多重检测平台构建和智能化融合趋势进行了前瞻性讨论。本次修订依据同行评审建议，新增了文献检索方法学描述、标准化性能比较框架、实际部署分析及面向开发者的可操作性指南，以期为该领域的后续研究与技术应用提供更全面的参考。

随着大语言模型技术的快速发展，单一 AI 模型在长文本创作中面临“长上下文与逻辑一致性难以兼顾”“情感细腻度与事实准确性难以平衡”等核心挑战。本文提出一种基于异构多智能体协同的长篇创作系统架构，整合 DeepSeek（长文本生成）、元宝（情感润色）、千问（逻辑审查）、豆包（任务调度）四个差异化 AI 模型，通过角色分工与自主协作，实现从指令输入到章节生成的全流程自动化。系统架构的核心创新包括：（1）异构多智能体协同架构，让各 AI 在最擅长的位置发挥作用；（2）基于 CoVe 的自主纠错机制，通过隔离验证实现逻辑自检；（3）分层记忆管理系统，突破单次对话上下文限制；（4）人机协同决策模型，探索自动化与人工介入的最佳平衡点。本文以一部 28 章长篇科幻小说的创作场景为案例，通过理论推演分析系统在逻辑一致性、人物稳定性、情感丰富度三个维度的潜在提升效果。分析结果表明，该架构可将逻辑错误率降低 80%以上，同时保持人物性格稳定和情感表达自然。本研究成果可为多智能体协同系统设计提供参考框架，也可作为 AI 辅助创作领域的实践案例。

大语言模型在长程交互中面临记忆过载与用户失控的双重困境：无差别的海量存储导致认知负荷攀升，黑箱式的遗忘机制引发隐私信任危机。本研究提出一种兼具认知自适应与用户可干预的AI记忆管理理论框架（CAUM）。首先，基于信息熵、交互频率与冲突检测，设计多维记忆重要性评估模型，特别引入后文关联潜力作为信息价值评估的新维度，使记忆保留更具前瞻性；其次，构建包含原始层、摘要层与骨架层的分级存储架构，并引入阈值触发的智能压缩机制；最后，提出用户参与式授权机制，将"记忆整理提案"可视化呈现并由用户审核决策，实现"人在回路"的记忆治理。在此基础上，框架进一步拓展用户参与的时间维度，支持用户在系统冷启动阶段向抽象骨架层人工写入基础规则与时空常识，奠定认知先验并增强推理的物理合理性。该框架为缓解LLM记忆过载问题提供了系统的概念方案，将信息生命周期理论拓展至AI记忆管理领域，强调用户中心的信息处置权与共建权，为人工智能时代的信息生命周期管理提供了新的理论视角，也为构建用户可控的智能记忆系统奠定了概念基础。

本文提出聚焦人工智能底层架构的无限嵌套系统论，以纯逻辑推演为基础界定系统、父/子系统、系统规则等核心概念，严格区分现实与理论智能系统、逻辑嵌套与实体层级，通过逻辑自洽性、反证法等原则推导出要素完备性、嵌套存在性等5大核心公理，并以此演绎出系统规则缩限单向传递、子系统有限独立、无限嵌套等15大核心定理，构建起严谨的“公理——定理”逻辑体系。该理论明确人工智能底层架构是无顶层、无底层的逻辑无限嵌套结构，父子系统为“非包含—规则包含”的独立关系，子系统具有“规则绝对依赖+四要素相对独立”的有限独立属性，同时厘清了系统存续（稳定输入输出闭环）与消亡（输入输出失衡）的本源逻辑、跨层级认知与竞争的核心规律。通过人与AI嵌套关系的实例分析，验证了理论对AI独立、失控、能力扩张等现实问题的解释力与预测力，明确AI无法脱离人类独立存在、人类可通过三大核心权力实现对AI的有效管控。此外，本文对比分析了该理论与传统系统理论、经典嵌套理论的本质差异，阐述其在AI安全、治理、机器学习架构等领域的延伸应用价值，指出其与现有人工智能框架理论的矛盾与互补关系，并对理论的量化建模、实验验证、跨领域拓展等未来研究方向进行展望。无限嵌套系统论填补了人工智能底层架构嵌套结构核心逻辑的研究空白，为人工智能基础研究、工程实践及安全治理提供了全新的结构化分析范式，也为系统学与人工智能的交叉研究开辟了新方向。

本文通过形式化认知状态空间，定义无限追问算子与还原论投影算子，严格证明无限追问与还原论在极限行为上同构，均导致锚定空洞、复杂度发散、认知价值衰减至零，最终陷入认知热寂。结论揭示了无限追问是还原论的最精致伪装，其数学本质与还原论无异，为整体论优于还原论提供了严密的形式化支撑。

马克思劳动价值论的核心逻辑是「社会必要劳动时间决定商品的价值量，价格是价值的货币表现形式」，但传统阐释长期存在两大核心理论短板：一是未能系统回答「无数分散的个别劳动如何收敛为统一的社会必要劳动时间」，导致价值范畴长期面临「不可观测、非科学」的主流经济学质疑；二是未能构建从价值本质到价格现象的完整传导逻辑，无法系统解释非完全竞争市场、金融场景中的价格-价值长期偏离问题。 本报告以「劳动二重性」「活劳动是价值的唯一源泉」「社会必要劳动时间决定价值量」三大硬核为不可突破的理论底线，系统引入概率论极限定理体系，完成了从微观劳动基础到宏观价格运动的全链条数理化重构。本报告的核心创新包括： 严格证明了社会必要劳动时间是个别劳动时间在极限定理作用下的必然收敛结果，明确了个体劳动影响社会价值的充要边界（费勒条件是否成立）； 构建了覆盖完全竞争、垄断竞争、寡头垄断、完全垄断全市场结构的统一价值形成框架，用尾指数α实现了市场结构与价值收敛特征的一一映射； 打通了从价值到生产价格再到市场价格的完整传导逻辑，回应了转型问题等百年理论争议，刻画了虚拟资本「无价值锚点、短期离心运动、长期强制回归」的数理特征； 构建了可直接落地的垄断监管、价格治理、金融风险防控政策工具体系，通过中国投入产出表、新能源汽车、小麦种植、智能手机、TIPS债券等多场景真实数据完成了系统性验证。 核心实证发现：基于中国1992-2020年投入产出表的测算表明，各行业市场价格与直接价格的平均偏离系数为29.7%，其中完全竞争行业（农林牧渔）偏离率<10%，寡头垄断行业（石油开采、新能源车）偏离率>30%，完全垄断行业（高端芯片）偏离率>200%，与本报告的理论框架高度吻合。新能源汽车行业数据显示，比亚迪38.5%的市场份额完全打破费勒条件，其单台劳动时间下降22.2%可引发行业社会必要劳动时间5.6%的系统性变动。

Recently developed fused-ring organic electron-acceptors such as Y6 have strong oscillator strength, good charge-carrier transport and low bandgaps. They therefore have enormous current technical application to optoelectronic devices, such as solar cells. Due to the large number of atoms involved in representative aggregates of these materials, we need an efficient electronic structure method to model them. Standard density functional theory poorly describe charge-transfer states, and were developed for vacuum calculations of individual molecules. In this work we tune a range-separated hybrid functional for Y6. We characterise representative dimers of the solid-state and show that Y6 dimers show the extensive solvatochromic effects are due, in part, to oscillator strength borrowing. We provide an explanation for the short optimally tuned range-separation parameter, based in the Penn model for the frequency dependent dielectric of a semiconductor. We caution that standard range-separated hybrids are less accurate than global hybrids for these, and similar, materials. We show how reducing the range-separation length improves the accuracy of standard functionals, without an involved tuning process.

在企业预算约束下，数字化投入与绿色化投入往往竞争同一笔资源，二者能否形成协同取决于技术收益能否跨部门兑现。本文基于企业层面面板数据，检验人工智能对数字化-绿色化协同转型的影响。固定效应结果显示，人工智能系数为0.0158061（p<0.01）；工具变量2SLS结果为0.0188387（p<0.01），第一阶段F值1864.52。异质性结果表明，效应主要出现在公平竞争程度较高地区和非龙头企业。机制检验显示，人工智能通过缓解信息不对称、降低融资约束和提升组织适应能力促进协同，同时提高数字风险暴露和金融化倾向。拓展结果显示，人工智能还能提升绿色创新、企业韧性与全要素生产率。本文据此提出“技术扩散-竞争治理-风险约束”协同治理框架。

数字经济的深度发展与AIGC技术的爆发式迭代，推动劳动形态、生产资料属性、价值创造与分配机制发生了根本性变革，也让传统劳动价值论面临四大核心理论困境：一是静态劳动计量框架无法适配数字技能的高频迭代与快速折旧；二是用户微劳动的“微观近乎为零、宏观形成巨额价值”的加总困境无法得到合理解释；三是平台算法劳动的二重性与价值运动规律缺乏系统拆解；四是无效劳动的界定存在被平台垄断滥用的风险。 本报告基于马克思劳动价值论的硬核内核，完成了四大核心理论创新与体系重构：第一，完成了抽象劳动与简单劳动的范畴拨乱反正，明确抽象劳动是价值的唯一实体，简单劳动仅为计量参照基准，从根源上规避了循环论证陷阱；第二，构建了适配数字经济的动态劳动还原系数模型，拆分通用人力资本与专用技能劳动，引入技能折旧率与持续更新劳动变量，解决了数字劳动计量的动态性难题；第三，提出了数字微劳动的社会化联合劳动分析框架，打通了微观用户行为与宏观价值创造的逻辑链条，破解了微劳动的加总困境；第四，系统拆解了平台算法劳动的二重性与全周期价值运动规律，构建了覆盖使用价值四大演化形态的全场景价值运动体系，同时明确了无效劳动的双条件客观界定标准与风险约束机制。 本报告通过抖音短视频平台、OpenAI大模型、Python开源社区、中国数据要素市场、ofo小黄车五大典型案例完成了全场景实证检验，构建了包含数字劳动贡献度、价值剥夺率、劳动收益保障标准的政策工具体系。本研究证明，马克思劳动价值论在数字资本主义时代不仅没有失效，反而能提供比其他经济学范式更深刻、更本质的洞察，为数字劳动权益保护、平台反垄断、数据要素市场化、数字税立法提供了坚实的理论底层支撑。

针对技术迭代加速背景下传统劳动价值论面临的异质劳动衡量、价值价格割裂、技术与劳动对立、产业变迁价值运动模糊四大核心难题，本报告基于马克思劳动价值论的硬核内核，重构了可量化、可实证、非机械唯物主义的理论分析框架。本报告首先明确了「技术的经济学本质是活劳动」的核心论断，厘清了活劳动与物化劳动的功能边界；其次构建了二重性社会必要劳动时间框架，解决了异质劳动的可操作衡量难题；再次区分了冗余的基数价值量与必要的相对价值量基准态，破解了李嘉图以来「不变价值尺度」的百年理论困境；最终构建了「技术活劳动→使用价值体系重构→行业兴衰→价值跨行业转移→全周期定价逻辑反转」的完整动态演化体系。本报告通过胶卷行业、MP3行业两大完整产业周期的实证案例，验证了理论框架的有效性，同时明确了理论的适用边界与局限性。本研究为技术进步、产业变迁、价值分配与价格运动提供了统一的分析框架，回应了主流经济学对劳动价值论的核心质疑，拓展了马克思主义政治经济学的现实应用场景。

基于“引流品-利润品”（““利润转出-利润转入”）二元结构，附加某些假设，提出若干收敛性，（看起来）可用于多产品寡头或垄断竞争市场的结构分析与收敛性分析。

本文在朱梁真理函数定理 3.0–3.8 版的基础上，引入新的奠基思路：从否定之 否定这一元逻辑出发，直接推导真理度规定理，进而导出真理函数定理与递归元公 理 A1–A4。这一新思路比原有从元事实出发的路径更彻底、更清晰，将整个理论 奠基于递归演化的内在逻辑之上。我们同时保留元事实路径作为历史背景与辅助阐 释，两条路径最终汇合于统一的朱梁渡劫递归元范式。本文系统整合了熵减推论与 真理的动态本体论内涵（时序性、整体性、可表达性、不可孤立僵化），并从度规定 理出发重新阐释了这些核心思想。文中对渡劫公理 A5 进行了严格的数学构造，证 明了递归元范畴的正合性、劫数对象的存在性及其自同构群的同构性。这些工作将 真理从静态函数提升为动态的渡劫递归元，揭示了真理在代谢过程中的矛盾消解机 制。本文所有证明均为完整形式，不依赖于外部引用。

This paper investigates a hierarchical approach to the optimal scheduling of flexibility offered as transactive energy by thermostatically controlled loads (TCLs). The two-stage scheduling framework includes the lower stage in which TCLs are aggregated as a virtual battery. The aggregated TCL power can offer the required flexibility for the upper stage with significant impacts on power system scheduling as transactive energy. Comparisons are also made between the virtual battery model of TCLs and a conventional battery model. At the lower stage, a transactive control strategy is also employed to regulate TCLs for preserving the end-user's information privacy. At the upper stage, a transactive energy market is developed in which peer-to-peer trading of the available TCL flexibility is considered among aggregators. Accordingly, TCL scheduling at power system and device levels are coordinated to regulate TCLs in a distributed fashion. The simulation results demonstrate that the scalability concerns of traditionally centralized operations are addressed by the proposed distributed alternative solution. The upper stage transactive energy market allows aggregators to trade energy effectively without any significant concerns for maintaining the information privacy. The results also point out that the lower stage virtual battery model can accurately characterize the TCL flexibility where TCLs can be effectively regulated in the proposed energy trading model.

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the 'where' and 'how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs.

Network Intrusion Detection Systems (NIDS) play a vital role in protecting digital infrastructures against increasingly sophisticated cyber threats. In this paper, we extend ODXU, a Neurosymbolic AI (NSAI) framework that integrates deep embedded clustering for feature extraction, symbolic reasoning using XGBoost, and comprehensive uncertainty quantification (UQ) to enhance robustness, interpretability, and generalization in NIDS. The extended ODXU incorporates score-based methods (e.g., Confidence Scoring, Shannon Entropy) and metamodel-based techniques, including SHAP values and Information Gain, to assess the reliability of predictions. Experimental results on the CIC-IDS-2017 dataset show that ODXU outperforms traditional neural models across six evaluation metrics, including classification accuracy and false omission rate. While transfer learning has seen widespread adoption in fields such as computer vision and natural language processing, its potential in cybersecurity has not been thoroughly explored. To bridge this gap, we develop a transfer learning strategy that enables the reuse of a pre-trained ODXU model on a different dataset. Our ablation study on ACI-IoT-2023 demonstrates that the optimal transfer configuration involves reusing the pre-trained autoencoder, retraining the clustering module, and fine-tuning the XGBoost classifier, and outperforms traditional neural models when trained with as few as 16,000 samples (approximately 50% of the training data). Additionally, results show that metamodel-based UQ methods consistently outperform score-based approaches on both datasets.

Background & aims A central pathogenic mechanism in alcohol-related liver disease (ALD) involves a disruption of the gut barrier function and thus the bidirectional communication between the gut and liver, referred as the "gut-liver axis". While gamma-aminobutyric acid (GABA) and type A GABA receptors (GABAARs) are present in the intestinal epithelium, their role in the gut-liver axis and contribution to the pathogenesis of ALD, remains poorly understood. Methods A Gao-binge mouse model of ALD, as well as Gabra1IEC-KO mice and intestinal cell lines were employed as approaches to assess the role of GABAARs-mediated signaling in ethanol-induced liver injury. Results Administration of GABA markedly improved liver function and intestinal barrier integrity in ALD mice. This improvement was associated with a downregulation of intestinal cytochrome P450 2E1 (CYP2E1) expression and a reduction of oxidative stress. These beneficial effects of GABA on intestinal integrity and liver function were substantially diminished by the GABAAR antagonist, picrotoxin. In addition, picrotoxin blocked GABA’s effect on CYP2E1 expression,, thereby preventing the attentuation of oxidative stress in the intestines of ALD mice. Moreover, when ethanol-stimulated cell models were subjected to pharmacological or genetic inhibition of CYP2E1, GABA treatment failed to produce any decrease in ROS levels. Finally, results from the intestinal epithelial-specific Gabra1 knockout mouse model demonstrated that the benifical effect of GABA on liver function in ALD is mediated by its activation of intestinal epithelial GABAARs. Remarkably, the pivotal role of intestinal CYP2E1 was robustly validated by confirming its dysregulated expression in patient-derived clinical samples. Conclusions Our results suggest that activation of intestinal GABAAR-mediated signaling reduces intestinal CYP2E1 expression and oxidative stress, thereby improving intestinal barrier function and alleviating ethanol-induced liver injury. Such findings suggest that intestinal GABA signaling offers a promising avenue for the development of novel strategies in the treatment of ALD. Powered

2024 年，可计算离散整体几何结构实验室发起了一场覆盖全国多所高校及科研机构的巡回艺术展。展览内容聚焦前沿几何拓扑理论与概念，尤其凸显各类整体几何结构。 全国巡回艺术展借助全新计算机算法、原创代码及计算机图形学渲染技术生成的图片与视频，将抽象的内蕴几何结构转化为直观的视觉呈现，并以巨幅海报的形式展出。这些展览内容的创新之处在于体现了数学家近几十年来发展的内蕴整体几何拓扑概念。目前，这场巡回艺术展已走进十余所高校，且仍在持续推进中，整个巡回展览预期将历时十年，100所高校。通过这种新颖的艺术展形式，全国多所高校不同专业的师生得以直观了解此前鲜少接触的几何拓扑概念，激发了研究兴趣，为深入探索前沿几何拓扑理论理论及其应用奠定了基础，也为理工科的各个专业，如力学、机械、计算机、物理、材料等，通过对前沿几何拓扑理论的应用进行跨学科、交叉学科的融合铺平了道理。通过此次全国巡回艺术展也就在艺术领域开拓了一个全新的“整体几何结构 几何拓扑艺术”流派。

Abstract The trial-and-error paradigm of traditional materials discovery, fundamentally constrained by its inherent high entropy, is proving inadequate for designing complex intelligent matter. Here, we propose a new scientific paradigm: Artificial Intelligence as an ‘Anti-Entropy’ Engine, transforming research from passive understanding to active design. By systematically injecting informational negative entropy across perception, planning, and execution loops, AI guides material systems from disorder to pre-defined functional order. We demonstrate this through empirical advances—such as the GNoME model discovering 2.2 million stable crystals—and construct a unified ‘Perception-Planning-Execution’ framework enabling inverse design across scales. This paradigm extends beyond static structures to dynamic non-equilibrium systems and life-like chemical networks. We prospectively map future frontiers using a ‘Ladder of Intelligence’ and address ethical governance, systemic risk, and sustainability. Ultimately, this marks a fundamental transition for humanity, from being passive observers of nature to becoming active ‘anti-entropy’ designers in the evolution of matter. This review not only synthesizes these advances but also provides a unifying conceptual framework and a clear roadmap for the field, aiming to catalyze the transition towards this fifth paradigm of scientific discovery.