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A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory

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Frontiers of Chemical Science and Engineering 2023年第3期17卷 358-371页

作者： Yi Tong Mou Shu Mingxin Li Yingwei Liu Ran Tao Congcong Zhou You Zhao Guoxing Zhao Yi Li Yachao Dong Lei Zhang Linlin Liu Jian Du COFCO Biotechnology Co. Ltd.Beijing 100005China COFCO Nutrition and Health Research Institute Co. Ltd.Beijing 102209China Institute of Process Systems Engineering School of Chemical EngineeringDalian University of TechnologyDalian 116024China

Corn to sugar process has long faced the risks of high energy consumption and thin ***,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related *** data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational *** this paper,a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes,which contains data preprocessing,dimensionality reduction,multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection *** the established model,dextrose equivalent value is selected as the output,and 654 sites from the DCS system are selected as the *** analysis is first applied to reduce the data dimension to 155,then the inputs are dimensionalized to 50 by means of genetic algorithm ***,variable importance analysis is carried out by the extended weight connection method,and 20 of the most important sites are selected for each neural *** results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%,which have a better prediction result than other models,and the 20 most important sites selected have better explicable *** major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.

关键词： big data corn to sugar factory neural network variable importance analysis

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Beyond the tumor region:Peritumoral radiomics enhances prognostic accuracy in locally advanced rectal cancer

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World Journal of Gastroenterology 2025年第8期31卷 49-65页

作者： Zhi-Ying Liang Mao-Li Yu Hui Yang Hao-Jiang Li Hui Xie Chun-Yan Cui Wei-Jing Zhang Chao Luo Pei-Qiang Cai Xiao-Feng Lin Kun-Feng Liu Lang Xiong Li-Zhi Liu Bi-Yun Chen Department of Radiology State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat-sen University Cancer CenterGuangzhou 510060Guangdong ProvinceChina Department of Radiology West China HospitalSichuan UniversityChengdu 610041Sichuan ProvinceChina West China School of Medicine Sichuan UniversityChengdu 610041Sichuan ProvinceChina Department of Medical Imaging First Affiliated Hospital of Gannan Medical UniversityGanzhou 341000Jiangxi ProvinceChina

BACKGROUND The peritumoral region possesses attributes that promote cancer growth and ***,the potential prognostic biomarkers in this region remain relatively underexplored in *** To investigate the prognostic value and importance of peritumoral radiomics in locally advanced rectal cancer(LARC).METHODS This retrospective study included 409 patients with biopsy-confirmed LARC treated with neoadjuvant chemoradiotherapy and *** were divided into training(n=273)and validation(n=136)*** on intratumoral and peritumoral radiomic features extracted from pretreatment axial high-resolution small-field-of-view T2-weighted images,multivariate Cox models for progression-free survival(PFS)prediction were developed with or without clinicoradiological features and evaluated with Harrell’s concordance index(C-index),calibration curve,and decision curve *** stratification,Kaplan-Meier analysis,and permutation feature importance analysis were *** The comprehensive integrated clinical-radiological-omics model(ModelICRO)integrating seven peritumoral,three intratumoral,and four clinicoradiological features achieved the highest C-indices(0.836 and 0.801 in the training and validation sets,respectively).This model showed robust calibration and better clinical net benefits,effectively distinguished high-risk from low-risk patients(PFS:97.2%vs 67.6%and 95.4%vs 64.8%in the training and validation sets,respectively;both P<0.001).Three most influential predictors in the comprehensive ModelICRO were,in order,a peritumoral,an intratumoral,and a clinicoradiological ***,the peritumoral model outperformed the intratumoral model(C-index:0.754 vs 0.670;P=0.015);peritumoral features significantly enhanced the performance of models based on clinicoradiological or intratumoral features or their *** Peritumoral radiomics holds greater prognostic value than intratumoral radiomics for predicting PFS in L

关键词： Rectal cancer Peritumoral radiomics Intratumoral radiomics Prognosis analysis variable importance analysis Tumor microenvironment

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Digital mapping of soil phosphorous sorption parameters (PSPs) using environmental variables and machine learning algorithms

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International Journal of Digital Earth 2023年第1期16卷 1752-1769页

作者： Sanaz Saidi Shamsollah Ayoubi Mehran Shirvani Kamran Azizi Shuai Zhao Department of Soil Science College of AgricultureIsfahan University of TechnologyIsfahanIran Department Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences818 South Beijing RoadUrumqiPeople’s Republic of China

In this study some soil phosphorous sorption parameters(PSPs)by using different machine learning models(Cubist(Cu),random forest(RF),support vector machines(SVM)and Gaussian process regression(GPR))were *** results showed that using the topographic attributes as the sole auxiliary variables was not adequate for predicting the ***,remote sensing data and its combination with soil properties were reliably used to predict PSPs(R^(2)=0.41 for MBC by RF model,R^(2)=0.49 for PBC by Cu model,R^(2)=0.37 for SPR by Cu model,and R^(2)=0.38 for SBC by RF model).The lowest RMSE values were obtained for MBC by RF model,PBC by SVM model,SPR by Cubist model and SBC by RF *** results also showed that remote sensing data as the easily available datasets could reliably predict PSPs in the given study *** outcomes of variable importance analysis revealed that among the soil properties cation exchange capacity(CEC)and clay content,and among the remote sensing indices B5/B7,Midindex,Coloration index,Saturation index,and OSAVI were the most imperative factors for predicting *** studies are recommended to use other proximally sensed data to improve PSPs prediction to precise decision-making throughout the landscape.

关键词： Soil fertility random forest adsorption isotherms remote sensing variable importance analysis

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