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简介：等级直方图是合适的工具在一个整体预言系统或框架以内估计整体的质量。由在整体数一个给定的变量的等级，如果它的可变性的起源是外部噪音或来自混乱来源，我们基本上正在做样品分析，它不允许我们区分。最近介绍的平均数到变化对数(MVL)图说明空间可变性，对空间本地化很敏感由空间与时间的混乱系统的无穷小的不安生产了。由把一个简单模型题目用作一个基准到噪音，我们显示出等级直方图和MVL图给的不同信息。因此，外部噪音的主要效果能在一张图被设想。从MVL图，我们清楚地观察振幅生长率并且空间本地化(混乱抑制)的减小，当从等级直方图我们在整体的可靠性观察变化时。我们断定在包括空间与时间的混乱和噪音的一个复杂框架，两个提供一幅更完全的预报图画。

简介：液体岩石相互作用改变geochemical，同位素，petrographic和主人岩石的物理特性，生产热水的活动的一个永久记录。改变的岩石性质的地图证明揭示的常规变化掌握地质的控制并且多半描出地点为地热并且矿物质资源。在许多情况中，geochemical和稳定的同位素数据揭示热液体的起源，并且他们能也提供水库温度的估计并且鉴别液体的地区再装。

简介：AbstractBackground:Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.Method:Five hundred thirteen CT images relating to 57 patients (49 with COVID-19; 8 free of COVID-19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes.Results:The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy; 1 error from 513 images).Conclusion:Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.