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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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The human body employs intricate mechanisms to counteract changes in blood pH, preventing conditions like acidosis (pH < 7.35) and alkalosis (pH > 7.45). These compensatory responses aim to restore normal arterial blood pH by engaging respiratory or renal systems, depending on the source of the imbalance.
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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基于机器学习的LIBS量化优化的剩余补偿算法

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    科学领域:

    • 分析化学
    • 光谱学
    • 材料科学

    背景情况:

    • 使用激光诱导分解光谱 (LIBS) 的定量分析经常面临预测准确性的挑战.
    • 环境和样本特定的参数可以显著影响LIBS光谱数据和随后的定量预测.
    • 现有的模型可能无法完全解释这些变量,导致预测错误.

    研究的目的:

    • 开发和验证基于剩余补偿的LIBS新型定量预测算法.
    • 通过将环境和样本参数整合到LIBS定量模型中,提高预测准确度.
    • 在不同的回归模型和样本类型中评估拟议的算法的有效性.

    主要方法:

    • 为LIBS定量预测开发了一种剩余补偿算法.
    • 该算法与支持向量机回归 (SVR),部分最小平方回归 (PLSR),随机森林回归 (RFR) 和K-最近邻居回归 (KNNR) 模型集成.
    • 使用10个元素的合金样本采用了10倍交叉验证方法.

    主要成果:

    • 剩余补偿算法显著降低了预测的平均绝对误差 (MAEP) 和预测的平均相对误差 (MREP).
    • 与最初的PLSR模型相比,MAEP和MREP的平均降低分别为51. 8%和64. 8%.
    • 对于基于SVR的模型,MAEP和MREP分别减少了43.0%和51.1%,表明了广泛的适用性.

    结论:

    • 拟议的剩余补偿算法有效地提高了LIBS定量预测的准确性.
    • 纳入环境和样本参数对于改善LIBS分析性能至关重要.
    • 这种方法为使用LIBS的合金提供了更可靠的元素分析方法.