Robustness Study and Superior Method Development and Validation for Analytical Assay Method of Atropine Sulfate in Pharmaceutical Ophthalmic Solution

Background: The robustness is a measurement of an analytical chemical method and its ability to contain unaffected by little with deliberate variation of analytical chemical method parameters. The analytical chemical method variation parameters are based on pH variability of buffer solution of mobile phase, organic ratio composition changes, stationary phase (column) manufacture, brand name and lot number variation;flow rate variation and temperature variation of chromatographic system. The analytical chemical method for assay of Atropine Sulfate conducted for robustness evaluation. The typical variation considered for mobile phase organic ratio change, change of pH, change of temperature, change of flow rate, change of column etc. Purpose: The aim of this study is to develop a cost effective, short run time and robust analytical chemical method for the assay quantification of Atropine in Pharmaceutical Ophthalmic Solution. This will help to make analytical decisions quickly for research and development scientists as well as will help with quality control product release for patient consumption. This analytical method will help to meet the market demand through quick quality control test of Atropine Ophthalmic Solution and it is very easy for maintaining (GDP) good documentation practices within the shortest period of time. Method: HPLC method has been selected for developing superior method to Compendial method. Both the compendial HPLC method and developed HPLC method was run into the same HPLC system to prove the superiority of developed method. Sensitivity, precision, reproducibility, accuracy parameters were considered for superiority of method. Mobile phase ratio change, pH of buffer solution, change of stationary phase temperature, change of flow rate and change of column were taken into consideration for robustness study of the developed method. Results: The limit of quantitation (LOQ) of developed method was much low than the compendial method. The % RSD for the six sample assay of developed method was 0.4% where the % RSD of the compendial method was 1.2%. The reproducibility between two analysts was 100.4% for developed method on the contrary the compendial method was 98.4%.

Development and Validation of a UPLC Method for the Determination of Docetaxel and Its Related Substances in Pharmaceutical Dosage Forms, an Antineoplastic Agent

A novel, simple, and sensitive Ultra Performance Liquid Chromatography (UPLC) method was developed and validated for the quantification of process-related impurities and degradants, as well as the assay of Docetaxel. The stability-indicating capability of the method was demonstrated through forced degradation studies and a comprehensive mass balance evaluation. Chromatographic separation was achieved using an ACQUITY UPLC BEH C18 column (100 × 2.1 mm, 1.7 µm), with gradient elution. The mobile phase A comprised a mixture of water, methanol, and acetonitrile (500:300:200, v/v/v), while mobile phase B was acetonitrile and water (800:200, v/v). The flow rate was set at 0.4 mL/min, with detection at 232 nm using a photodiode array detector. The method exhibited excellent performance, with a tailing factor of 1.10 for Docetaxel. The method was rigorously validated for precision, accuracy, linearity, LOD, LOQ, ruggedness, specificity, and robustness. Forced degradation studies confirmed the method’s suitability for stability analysis. Stability testing on the drug substance was conducted following ICH guidelines.

A method for Improving the Efficiency of Pear Tree Crossbreeding

In view of the short blooming period of pear tree crossbreeding and the complexity of pollination process, a method that can improve the efficiency of crossbreeding of pear trees was provided. Meanwhile, this method can also be applied to the study of pollen xenia effect, pollination tree selection and pure pollen collection in pear tree cultivation.

Revisiting Akaike’s Final Prediction Error and the Generalized Cross Validation Criteria in Regression from the Same Perspective: From Least Squares to Ridge Regression and Smoothing Splines

In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on the most commonly accepted definition of the MSPE as the expectation of the squared prediction error loss, we provide theoretical expressions for it, valid for any linear model (LM) fitter, be it under random or non random designs. Specializing these MSPE expressions for each of them, we are able to derive closed formulas of the MSPE for some of the most popular LM fitters: Ordinary Least Squares (OLS), with or without a full column rank design matrix;Ordinary and Generalized Ridge regression, the latter embedding smoothing splines fitting. For each of these LM fitters, we then deduce a computable estimate of the MSPE which turns out to coincide with Akaike’s FPE. Using a slight variation, we similarly get a class of MSPE estimates coinciding with the classical GCV formula for those same LM fitters.

Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction

Background： A random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction, using whole-genome data. Leave-one-out cross validation can be used to quantify the predictive ability of a statistical model.Methods： Naive application of Leave-one-out cross validation is computationally intensive because the training and validation analyses need to be repeated n times, once for each observation. Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.Results： Efficient Leave-one-out cross validation strategies is 786 times faster than the naive application for a simulated dataset with 1,000 observations and 10,000 markers and 99 times faster with 1,000 observations and 100 markers. These efficiencies relative to the naive approach using the same model will increase with increases in the number of observations.Conclusions： Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.

基于遗传算法与Hardy-Cross方法的矿井通风机控制

针对矿井通风机系统控制的复杂问题,提出了一种遗传算法与Hardy-Cross方法相结合的矿井通风控制,强化了其在处理离散变量和非凸搜索空间中寻找全局最优解的能力。通过数学形式化定义问题并结合基尔霍夫定律,确立了通风机网络的优化模型。根据实际用风情况智能调节通风机的供风量,以实现能源节约和安全生产的目标。通过对某煤矿的通风案例深入研究和验证,风扇的配置在优化气流方面表现出了卓越性能。该研究不仅为矿山工程领域提供了新的优化思路和方法,也为通风机系统的管理提供了重要参考。

Solriamfetol impurities:Synthesis,characterization,and analytical method(UPLC-UV)validation

Given that impurities may affect the quality and safety of drug products,impurity identification and profiling is an integral part of drug quality control and is particularly important for newly developed medications such as solriamfetol,which is used to treat excessive daytime sleepiness.Although the highperformance liquid chromatography analysis of commercial solriamfetol has revealed the presence of several impurities,their synthesis,structure elucidation,and chromatographic determination have not been reported yet.To bridge this gap,we herein identified,synthesized,and isolated eight processrelated solriamfetol impurities,characterized them using spectroscopic and chromatographic techniques,and proposed plausible mechanisms of their formation.Moreover,we developed and validated a prompt impurity analysis method based on ultrahigh-performance liquid chromatography with UV detection,revealing that its selectivity,linearity,accuracy,precision,and quantitation limit meet the acceptance criteria of method validation stipulated by the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use.Thus,the developed method was concluded to be suitable for the routine analysis of solriamfetol substances.

Analytical Method Development and Validation of Some Biosimilar Drugs by High Performance Thin Layer Chromatography

A simple and rapid HPTLC analytical method has been developed and validated for the determination of Etanercept and Filgrastim in pure form and in marketed formulation. Both the drugs were chromatographed on silica gel 60 F254s HPTLC plates, as stationary phase. The mobile phase optimized for Filgrastim and Etanercept was Water: n-butanol (7.5:2.5 v/v) and Isopropyl alcohol: water (6.5:4.5 v/v), respectively. The chromatogram obtained was scanned at 225 nm and 222 nm for filgrastim and etanercept which resulted in a retention factor of 0.45 ± 0.07 and 0.32 ± 0.03, respectively. The method was validated for parameters like linearity, accuracy, precision, specificity and robustness. Recovery studies were performed at three concentration levels, to demonstrate suitability, accuracy and precision of proposed method. Statistical analysis proved that the proposed method is accurate and reproducible with linearity in the range of 500 to 3000 ng/band for filgrastim and 200 to 1200 ng/band for etanercept. The limit of detection and limit of quantification for filgrastim was found to be 63.418 ng/band and 192.177 ng/band. For etanercept, LOD and LOQ were found to be 33.381 ng/band and 101.153 ng/band, respectively. The proposed method can be employed for the routine analysis of selected biosimilars.

Generalized response displacement methods for seismic analysis of underground structures with complex cross section

The response displacement method(RDM)is recommended for the seismic analysis of underground structures in the transverse direction for many codes,including bases for design of structures-seismic actions for designing geotechnical works(ISO 23469)and code for seismic design of urban rail transit structures(GB 50909-2014).However,there are some obvious limitations in the application of RDM.Springs and the shear stress of the soil could be approximately evaluated for the structures having a simple cross section,such as rectangular and circular structures.It is necessary to propose simplified seismic analysis methods for structures with complex cross sections.This paper refers to the idea of RDM and proposes three generalized response displacement methods(GRDM).In GRDM1,a part of the soil surrounding a structure is selected to generate a generalized underground structure with a rectangular cross section,and the same analysis model as RDM is applied to analyze the responses of the structure.In GRDM2,a hollow soil model without a generalized structure is used to compute the equivalent load caused by the relative displacement of the soil,and the soil-structure interaction model is applied to calculate the responses of the structure.In GRDM3,a continuous soil model is applied to compute the equivalent load caused by the relative displacement and shear stress of the soil,and the soil-structure interaction model is applied to analyze the responses of the structure,which is the same as the model used in GRDM2.The time-history analysis method(THAM)is used to evaluate the accuracy of the proposed simplified methods.Results show that the error of GRDM1 is about 20%,while the error is only 5%for GRDM2 and GRDM3.Among the three proposed methods,GRDM3 has obvious advantages regarding calculation efficiency and accuracy.Therefore,it is recommended to use GRDM3 for the seismic response analysis of underground structures that have conventional simple or complex cross sections.

An effective method to calculate the electron impact excitation cross sections of helium from ground state to a final channel in the whole energy region

The electron impact excitation(EIE) cross sections of an atom/ion in the whole energy region are needed in many research fields, such as astrophysics studies, inertial confinement fusion researches and so on. In the present work, an effective method to calculate the EIE cross sections of an atom/ion in the whole energy region is presented. We use the EIE cross sections of helium as an illustration example. The optical forbidden 1^(1)S–n^(1)S(n = 2–4) and optical allowed 1^(1)S–n^(1)P(n = 2–4) excitation cross sections are calculated in the whole energy region using the scheme that combines the partial wave R-matrix method and the first Born approximation. The calculated cross sections are in good agreement with the available experimental measurements. Based on these accurate cross sections of our calculation, we find that the ratios between the accurate cross sections and Born cross sections are nearly the same for different excitation final states in the same channel. According to this interesting property, a universal correction function is proposed and given to calculate the accurate EIE cross sections with the same computational efforts of the widely used Born cross sections,which should be very useful in the related application fields. The datasets presented in this paper are openly available at https://www.doi.org/10.57760/sciencedb.j00113.00142.

Seasonal Prediction of Tropical Cyclones and Storms over the Southwestern Indian Ocean Region Using the Generalized Linear Models

Tropical cyclones (TCs) and storms (TSs) are among the devastating events in the world and southwestern Indian Ocean (SWIO) in particular. The seasonal forecasting TCs and TSs for December to March (DJFM) and November to May (NM) over SWIO were conducted. Dynamic parameters including vertical wind shear, mean zonal steering wind and vorticity at 850 mb were derived from NOAA (NCEP-NCAR) reanalysis 1 wind fields. Thermodynamic parameters including monthly and daily mean Sea Surface Temperature (SST), Outgoing Longwave Radiation (OLR) and equatorial Standard Oscillation Index (SOI) were used. Three types of Poison regression models (i.e. dynamic, thermodynamic and combined models) were developed and validated using the Leave One Out Cross Validation (LOOCV). Moreover, 2 × 2 square matrix contingency tables for model verification were used. The results revealed that, the observed and cross validated DJFM and NM TCs and TSs strongly correlated with each other (p ≤ 0.02) for all model types, with correlations (r) ranging from 0.62 - 0.86 for TCs and 0.52 - 0.87 for TSs, indicating great association between these variables. Assessment of the model skill for all model types of DJFM and NM TCs and TSs frequency revealed high skill scores ranging from 38% - 70% for TCs and 26% - 72% for TSs frequency, respectively. Moreover, results indicated that the dynamic and combined models had higher skill scores than the thermodynamic models. The DJFM and NM selected predictors explained the TCs and TSs variability by the range of 0.45 - 0.65 and 0.37 - 0.66, respectively. However, verification analysis revealed that all models were adequate for predicting the seasonal TCs and TSs, with high bias values ranging from 0.85 - 0.94. Conclusively, the study calls for more studies in TCs and TSs frequency and strengths for enhancing the performance of the March to May (MAM) and December to October (OND) seasonal rainfalls in the East African (EA) and Tanzania in particular.

Validation method for simulation models with cross iteration

Cross iteration often exists in the computational process of the simulation models, especially for control models. There is a credibility defect tracing problem in the validation of models with cross iteration. In order to resolve this problem, after the problem formulation, a validation theorem on the cross iteration is proposed, and the proof of the theorem is given under the cross iteration circumstance. Meanwhile, applying the proposed theorem, the credibility calculation algorithm is provided, and the solvent of the defect tracing is explained. Further, based on the validation theorem on the cross iteration, a validation method for simulation models with the cross iteration is proposed, which is illustrated by a flowchart step by step. Finally, a validation example of a sixdegree of freedom (DOF) flight vehicle model is provided, and the validation process is performed by using the validation method. The result analysis shows that the method is effective to obtain the credibility of the model and accomplish the defect tracing of the validation.

Sensitivity Analysis of Electromagnetic Scattering from Dielectric Targets with Polynomial Chaos Expansion and Method of Moments

In this paper,an adaptive polynomial chaos expansion method(PCE)based on the method of moments(MoM)is proposed to construct surrogate models for electromagnetic scattering and further sensitivity analysis.The MoM is applied to accurately solve the electric field integral equation(EFIE)of electromagnetic scattering from homogeneous dielectric targets.Within the bistatic radar cross section(RCS)as the research object,the adaptive PCE algorithm is devoted to selecting the appropriate order to construct the multivariate surrogate model.The corresponding sensitivity results are given by the further derivative operation,which is compared with those of the finite difference method(FDM).Several examples are provided to demonstrate the effectiveness of the proposed algorithm for sensitivity analysis of electromagnetic scattering from homogeneous dielectric targets.

基于Cross-Validation的小波自适应去噪方法

小波去噪算法中,阈值的选择非常关键.提出一种自适应阈值选择算法.该算法先通过Cross-Validation方法将噪声干扰信号分成两个子信号,一个用于阈值处理,一个用作参考信号;再采用最深梯度法来寻求一个最优去噪阈值.仿真和实验结果表明:在均方误差意义上,所提算法去噪效果优于Donoho等提出的VisuShrink和SureShrink两种去噪算法,且不需要带噪信号的任何'先验信息',适应于实际信号去噪处理.

基于gBLUP方法及Cross-validation大豆表型精准预测研究

为了实现提高产量和抵抗病害等能力的目的,需要提高育种水平,通过设计交差验证(Cross-Validation)实验进行大豆基因型和表型数据的分组处理,根据数据的个体和mark的数量进行合理分配,采用gBLUP(genomic Best Linear Unbiased Prediction)方法进行表型预测。根据对大豆数据多个性状通过不同分组的对比来得到精确值的范围,为后续的育种分析提供依据。对于只有大豆基因型数据而没有表型数据的情况,需要模拟表型,根据设定遗传力和模拟位点的个数(NQTN)进行模拟,然后再进行不同分组获取精准值,这样扩大了大豆数据的预测灵活性。

Methodology Validation of Microbial Limit Test of Bupi Qiangli Ointment

[Objective] This study was conducted to establish a microbial limit test method for Bupi Qiangli Ointment. [Method] The conventional method and medium dilution method were used for bacterial, mold and yeast counting in sample recovery test. [Result] The medium dilution method （1：10 test solution, 0.5 ml/plate） could effectively eliminate the inhibition effect of the Bupi Qiangli Ointment, and the recovery of Staphylococcus aureus was greater than 70% in the 3 batches of samples; and the conventional method exhibited the recoveries of E. coil, Bacillus subtilis, Candida albicans and Aspergillus greater than 70% in the 3 batches of samples. [Conclusion] Due to Bupi Qiangli Ointment has strongly antibacterial effect on Staphylococcus au- reus, the medium dilution method was used for bacterial counting, and the conventional method was used for mold and yeast counting; and the conventional method was used for controlled bacterium examination of E. coll.

基于Cross-Validation的电机故障诊断振动数据处理方法

针对牵引电机故障诊断研究中所采用的神经网络方法,提出在模型训练阶段引入K折交叉验证。该方法在划分训练集与测试集期间,使验证集能够遍历所有数据集,从多方向开始学习,从而在一定程度上避免了局部极小的问题。训练完成后,以神经网络作为分类器进行故障识别。神经网络学习算法采用随机梯度下降的方法,每次投入一组数据集进行训练,大大提高了训练速度。Eclipse+Anaconda仿真结果证明:与传统神经网络电机故障诊断方法相比,该方法可以在一定程度上避免过拟合现象,同时避免局部极小。此外,在Matlab环境下,单独比较支持向量机采用交叉验证前后的故障分类效果。对比结果表明:交叉验证方法从多方向开始学习,对于提升故障诊断的准确率有较好作用。

非参数回归的L_1-Cross-Validation最近邻中位数估计的强相合性

考虑非参数回归模型Yi=g（xi）+ei,i≥1,其中g（x）是待估计的连续函数,{xi,i≥1}是非随机的,{ei,i≥1}是iid随机误差,在本文中,我们讨论最近邻中位数估计（x）=m(Y（i（1））,…,Yi（h*）=Yi（1）,…,Yi（h*）之中位数,其中h*利用L1—Cross—Validation方法选择,在一定条件下,建立了L1—Cross—Validation最近邻中位数估计的强相合性。

非参数回归的L_1-cross-validation最近邻估计的强相合性

考虑非参数回归模型:Y_i=g(x_i)+e_i,i≥1,其中g是待估计的连续函数,{x_i,i≥1}是非随机的,{e_i,i≥1)是iid随机误差。在本文中,我们讨论最近邻估计g_(n,h)(x)=1/h∑Y_(R_(i,x)^(n)),其中h利用L_1-cross-validation方法选择,在一定条件下,证明了L_1-cross-validation最近邻估计的强相合性。

Fast cross validation for regularized extreme learning machine

A method for fast 1-fold cross validation is proposed for the regularized extreme learning machine （RELM）. The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive 1-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l 〉 20. To corroborate the efficacy and feasibility of fast l-fold cross validation, experiments on five benchmark regression data sets are evaluated.