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适用于食品词
分析仪器的不断演变决定了数据生产的指数增加，这反过来促进了新的尖端分析挑战，需要将人工智能（AI）算法逐步整合到仪器数据处理软件中。机器学习，深度学习和计算机视觉是利用高级分析化学指标的信息潜力所采用的最常见技术。在本文中，我们的主要重点是阐明利用AI工具用于全面的二维气体色谱数据（PRE）处理的显着优势。我们说明了AI技术如何有效地探索从多维平台中得出的复杂数据集，这些数据集结合了综合的二维分离与质谱的质量光谱法，在充满挑战的食品摩托学应用领域中。基于图像处理，计算机视觉和AI气味的模式识别是通过介绍操作原理，审查可用工具和软件解决方案的原理，并通过选定的应用程序来说明其潜力和局限性。
美国谴责的美国法庭摘要，Scotus＃23-939
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EDA的应用
摘要：为了提高3D过渡金属复合物中M @ L键的理解，可以通过能量分解分析分析化学价模型（EDA-NOCV）的天然轨道，因为它提供了对M @ L Incractions的完整，定量和化学直觉的缩写描述。在这项研究中，由于过渡温度（T 1/2）对M @ l键的微妙变化而言，通过使用八面体自旋（SCO）配合物来建立并验证了通常适用的碎片和计算方案。Specifically, EDA-NOCV analysis of Fe @ N bonds in five [Fe II ( L azine ) 2 (NCBH 3 ) 2 ], in both low-spin (LS) and paramagnetic high-spin (HS) states led to: 1) development of a general, widely applicable, corrected M + L 6 fragmenta- tion, tested against a family of five LS [Fe II ( L azine ) 3 ](BF 4 ) 2复合物；这证实了三个L苯二氮更强