国家预印本平台

目的:基于《黄帝内经》营卫理论及现代医学讨论六经辨证本质,方法:在现代中西医学指导下，解读《黄帝内经》营卫的生成过程与功能，分析各脏腑在此过程中的作用，总结卫气病理与治则；在此基础上，分析六经辨证的前身《黄帝内经》六经分证及《伤寒论》六经辨证的本质。结果:营卫为五脏精气合后天水谷精微及自然界清气生成，营卫气包含现代医学多种物质成分，外感病卫气为抗邪主导，卫气病病机为卫气失常，可分为卫气虚证及卫气郁闭证、卫气阻滞证、卫气内伐证、卫气过亢证等实证，以及虚实夹杂的卫气紊乱（失衡）证。卫气受邪，疾病沿经络脏腑传变可以分为六经病。在太阳病期，邪毒多与卫气相争于肺、胃肠黏膜，邪毒侵入肺系、肠胃黏膜下淋巴组织，与散行卫气相搏，为阳明证，卫气进入独行通道淋巴管，在淋巴结与独行卫气相搏，影响胆、胃功能，出现口苦、喜呕、胁肋疼痛等症状，则为少阳证。太阳病为卫分证，阳明证、少阳证为气分证。因感受寒邪伤及营血症状多不明显，故仲景没有研究营血证。太阳病、阳明病、少阳病邪卫相争，伤及脏腑之气，致五脏气虚、气化郁滞，卫气乏源，或有邪毒内侵则发生三阴病，三阴病正邪俱衰者，为虚证，太阴病为肺脾虚并卫气虚，少阴病为心肾虚并卫气虚，厥阴病为肝虚并卫气虚，治疗重在救里。三阴病若邪毒内传于三阴，或病理产物堆积，则发生实证或虚实夹杂证，治在祛邪或扶正祛邪。结论:六经辨证与现代医学可以互通。

元符三年（1100）宋徽宗即位、大赦天下，苏轼由贬所海南昌化军量移廉州，于六月二十日夜渡海，七月四日抵合浦，滞留五十六日后溯南流江北上，经今合浦、浦北、博白、玉林、北流而转藤州，是其晚年人生心境蜕变、诗文风格收官的关键行旅。历来研究多聚焦苏轼居廉轶事与《留别廉守》“小饼如嚼月”的月饼典故，鲜有以整条南流江水路为线索的文学地理考证，亦多忽略今浦北境内江段。本文以水系脉络为纲，结合《苏轼文集》《苏轼诗集》诸家校注、孔凡礼《苏轼年谱》、明清《廉州府志》、王象之《舆地纪胜》与现存碑刻文物，考证苏轼南流江段行旅时序、交游人物、沿途风物与传世诗文，厘清宋代廉州古行政区与现代合浦、浦北之分野，剖析其晚年旷达思想的成型过程，阐释东坡文脉与南流江地域文化千年共生的独特价值。

本文以胃干细胞（gastric stem cells, GSCs）为起点，建立物理模型与数学模型，阐明类器官构建与再生的统一逻辑。将GSCs状态形式化为离散命运与连续状态耦合的希尔伯特空间，用厄米算符、量子跳跃、随机微分方程、相场自由能、Potts接触能、非牛顿流体和反应扩散方程描述自身调控与周围环境的互作。进一步论述自身扩增与环境互作耦合，命运概率和自由能景观决定谱系分化，空间结构由能量最小化涌现，生长遵循扩散限制标度，最终形成类器官并具备再生能力的过程。本征函数框架受时间分辨率和测量模态限制，牛顿流体与无滑移假设需非牛顿及孔隙弹性修正，连续观测的数据量、光毒性和标记限制仍待解决。胃类器官是GSCs在物理约束与数学规律下自发涌现的开放耗散系统，再生是其稳态维持与损伤修复的功能表现。

[目的] 预算受限时，增强代价如何随训练预算变化、是否依赖算子，尚缺乏受控测量。 [方法] 在 Fashion-MNIST 与 CIFAR-10 上完成 186 次受控运行，同一模型与协议下对照 CutMix 与 MixUp，覆盖 1–120 epoch，每配置 3 个种子。 [结果] 惩罚衰减形式与算子、数据集无关，但初值依赖算子：MixUp 初始惩罚仅为 CutMix 的 22.3%（CIFAR-10）与 22.7%（Fashion-MNIST），噪声带时点差 4 倍。 [局限] 模型仅 0.468 M 参数、图像尺寸小、仅测两个算子；120 epoch 仅覆盖 CIFAR-10 且 n=3，置信区间跨零。 [结论] “增强需要多少轮才不亏”取决于算子；种子数须匹配结论的适用范围。

Accurate determination of the hydrogen isotope ratio is essential for particle inventory control and tritium accounting in fusion devices. Conventional spectral fitting approaches based on iterative least-squares optimization are computationally intensive and prone to convergence failure under poor initial guesses, while purely data-driven supervised networks require extensive labeled datasets and exhibit limited extrapolation to experimental conditions outside the training distribution. We report an unsupervised Physics-Informed Neural Network (PINN) that embeds a differentiable 18-component Gaussian forward model directly into the inference architecture. The model accounts for Zeeman splitting, Doppler broadening, instrumental broadening, and three distinct neutral populations. The framework retrieves eight physical parameters, including X H (hydrogen-to-deuterium ratio, X H =n H /(n H +n D )), from raw Balmer-α spectra without reliance on ground-truth labels. A composite loss function enforces multi-view reconstruction consistency and spectral shape regularization, while applying supervised constraints on the magnetic field and neutral temperatures. Training on 360,000 synthetic spectra converges within 40 epochs, yielding a validation loss of 0.03496 and a maximum spectral residual of 0.006. Inversion on 180,000 independent test samples achieves R 2 =0.9999 for X H prediction. Under 1%-3% synthetic photon noise, integration with a Kalman filter doubles the inversion precision; Allan deviation analysis indicates a detection limit of 3.07-7.5 × 10 -5 at 10 s integration time. The proposed approach will offer a label-free, millisecond-scale inference capability compatible with real-time isotope ratio monitoring on current and next-step tokamak devices, with its reliability and accuracy having been successfully validated on experimental data from the EAST tokamak.