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Dynamic response surface methodology using lasso regression for organic pharmaceutical synthesis

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Frontiers of Chemical Science and Engineering 2022年第2期16卷 221-236页

作者： Yachao Dong Christos Georgakis Jacob Santos-Marques Jian Du Institute of Chemical Process Systems Engineering School of Chemical EngineeringDalian University of TechnologyDalian 116024China Department of Chemical and Biological Engineering and Systems Research Institute Tufts UniversityMedfordMA 02155USA

To study the dynamic behavior of a process,time-resolved data are collected at different time instants during each of a series of experiments,which are usually designed with the design of experiments or the design of dynamic experiments *** utilizing such time-resolved data to model the dynamic behavior,dynamic response surface methodology(DRSM),a datadriven modeling method,has been *** approaches can be adopted in the estimation of the model parameters:stepwise regression,used in several of previous publications,and lasso regression,which is newly incorporated in this paper for the estimation of DRSM ***,we show that both approaches yield similarly accurate models,while the computational time of lasso is on average two magnitude *** case studies are performed to show the advantages of the proposed *** the first case study,where the concentrations of different species are modeled directly,DRSM method provides more accurate models compared to the models in the *** second case study,where the reaction extents are modeled instead of the species concentrations,illustrates the versatility of the DRSM ***,DRSM with lasso regression can provide faster and more accurate datadriven models for a variety of organic synthesis datasets.

关键词： data-driven modeling pharmaceutical organic synthesis lasso regression dynamic response surface methodology

Dialectical study of disulfidptosis and clinical outcome in patients with colorectal cancer

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Traditional Medicine Research 2024年第10期9卷 52-63页

作者： Hua-Mei Zhu Yu-Tao Chen Yang Ye Yue-Feng Hu Department of Orthopaedics The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical UniversityWenzhou 325000China The Second School of Medicine Wenzhou Medical UniversityWenzhou 325000China

Background:Colorectal cancer(CRC)is a leading cause of cancer mortality *** study aims to develop a prognostic model based on disulfidptosis-related genes to assess survival outcomes in CRC,highlighting the tumor microenvironment’s ***:The thought of traditional Chinese medicine syndrome differentiation and treatment runs through the whole *** analyzed CRC tissue data from The Cancer Genome Atlas and the Gene Expression Omnibus using single-sample gene set enrichment and weighted gene correlation network analyses to identify prognostic markers and evaluate immune *** also investigated predictive drug ***:We identified seven disulfidptosis-related markers–complement C1q A chain(C1QA),solute carrier family 11 member 1(SLC11A1),cluster of differentiation 36(CD36),cluster of differentiation 6(CD6),interleukin 1 receptor associated kinase 3(IRAK3),S100 calcium binding protein A8(S100A8),and CD8 subunit alpha(CD8A)–that significantly influence *** classified in the low-risk group demonstrated improved overall survival compared to those in the high-risk group across training(P=0.0026)and validation cohorts(P=0.032).Differential gene expression was significant in the high-risk group(P<0.001),and prevalent mutations included APC regulator of WNT signaling pathway(APC),tumor protein P53(TP53),Titin(TTN),and Kirsten rat sarcoma viral oncogene(KRAS).The risk score correlated linearly with tumor microenvironment *** results of drug analysis showed that some traditional drugs may have anticancer effects through the vertical action of ***:Our prognostic model,integrating seven disulfidptosis-related genes,categorizes CRC patients by survival probability and underscores these genes as potential biomarkers linked to the tumor *** findings support their use in refining therapeutic strategies for CRC.

关键词： colorectal cancer tumor microenvironment disulfidptosis WGCNA lasso regression

Genomic Selection for Frogeye Leaf Spot Resistance in Soybean

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Journal of Northeast Agricultural University(English Edition) 2024年第1期31卷 11-19页

作者： Yao Lanning Chen Yizhi Li Haochen Zhang Yue Xia Mingyu Ning Shicheng Ning Hailong College of Arts and Sciences Northeast Agricultural UniversityHarbin 150030China College of Life Sciences Northeast Agricultural UniversityHarbin 150030China College of Economics and Management Northeast Agricultural UniversityHarbin 150030China College of Veterinary Medicine Northeast Agricultural UniversityHarbin 150030China Key Laboratory of Soybean Biology Ministry of EducationKey Laboratory of Soybean Biology and Breeding/GeneticsMinistry of AgricultureCollege of AgricultureNortheast Agricultural UniversityHarbin 150030China Zhongnongfa Wudalianchi Agricultural Technology Co. LtdWudalianchi 164100HeilongjiangChina

Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator(lasso) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance.

关键词： lasso regression stepwise regression genomic selection model soybean frogeye leaf spot(FLS)disease

Application of Grey Model and Neural Network in Financial Revenue Forecast

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Computers, Materials & Continua 2021年第12期69卷 4043-4059页

作者： Yifu Sheng Jianjun Zhang Wenwu Tan Jiang Wu Haijun Lin Guang Sun Peng Guo College of Engineering and Design Hunan Normal UniversityChangsha410081China Big Data Institute Hunan University of Finance and EconomicsChangsha410205China University Malaysia Sabah Sabah88400Malaysia

There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future *** grey neural network model fused with lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using *** can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction *** this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of ***,we used lasso regression to reduce the dimensionality of the *** the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–***,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal *** experimental results show that the combined model has a good effect in financial revenue forecasting.

关键词： Fiscal revenue lasso regression gray prediction model BP neural network

Consumer attitudes towards the mountain product label: Implications for mountain development

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Journal of Mountain Science 2021年第9期18卷 2255-2272页

作者： BASSI Ivana CARZEDDA Matteo GRASSETTI Luca ISEPPI Luca NASSIVERA Federico Department of Agricultural FoodEnvironmental and Animal SciencesUniversity of Udinevia delle Scienze 206UdineItaly Department of Economics BusinessMathematics and StatisticsUniversity of Triestevia Tigor 22TriesteItaly Department of Economics and Statistics University of Udinevia Tomadini 30/AUdineItaly

To protect and promote the originality and authenticity of mountain foodstuffs, the European Union set Regulation No 1151/2012 to create the optional quality term "mountain product". Our research aimed at exploring the attractiveness of the mountain product label for consumers, considering both attitude towards the label itself and purchase intentions. We propose a model to investigate relationships between four latent constructs-mountain attractiveness, mountain food attractiveness, attitude towards the mountain product label, and purchase intention-which have been tested, thus confirming the statistical relevance of the relationships. All 47 items selected for describing the latent constructs are suitable for this purpose. Ridge and lasso results also show that 17 items of the first three constructs are relevant in explaining purchase intentions. Some contextual variables, such as age, income, geographical origin of consumers, and knowledge of mountain products and mountains for tourism purposes, can positively influence consumers’ behavior. These findings could support the design of mountain development strategies, in particular marketing actions for both the product and the territory.

关键词： Mountain product label Consumer attitudes Purchase intentions Structural Equation Model(SEM) lasso regression

Forecasting Chinese Corporate Bond Defaults: Comparative Study of Market- vs. Accounting-Based Models

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Frontiers of Economics in China-Selected Publications from Chinese Universities 2019年第4期14卷 536-582页

作者： Michael Peng Dongkai Jiang Yingjie Wang Boston Consulting Group 10 Hudson YardsNew York CityNY 10001USA Witzcredit Risk Analysis 362 Milford CourtNew TownPA 18940USA KPMG 5001 Shennan E RdDiwang TowerLuohu DistrictShenzhen 518001China

This paper provides the first empirical study on bond defaults in the Chinese *** overcomes the deficiencies of existing methods,which suffer from lack of actual default data for back *** newly available bond default data,we analyze the roles of market variables against accounting variables under various *** we find that Merton's market-based structural model and KMV's Distance to Default exhibit languid discriminating power compared with hazard models that have carefully constructed predictors,other market variables carry significant information about bond defaults and could help improve on models with only the accounting *** implies that the collective intelligence of the market could somehow mitigate the distortion caused by misreported accounting ***,model performance can be significantly improved by adding predicting variables that link an individual financial measure to the broader market performance,such as the relative margin—a business environment proxy introduced in this *** not only shed light on the default behavior of the Chinese bond market,but also provide a promising approach to improve the variable selection process.

关键词： bond default Chinese bond default bankruptcy forecast hazard model Merton model accounting variables Z-score lasso regression

Magnitude and Determinants of Mortalities Related to COVID-19:Evidence from 94 Countries Using regression Techniques

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Fudan Journal of the Humanities and Social Sciences 2022年第4期15卷 475-499页

作者： Ashis Kumar Pradhan Ronny Thomas Sandhyarani Rout Alok Kumar Pradhan Department of Humanities and Social Sciences Maulana Azad National Institute of TechnologyBhopalMadhya PradeshIndia Rajagiri Business School(RBS)and Rajagiri College of Social Sciences(RCSS) Rajagiri Valley CampusKakkanadKochiKeralaIndia Department of Social Science Fakir Mohan UniversityBalasoreOdishaIndia Indian Institute of Management Nagpur DahegaonIndia

This study attempts to investigate the factors determining COVID-19 deaths during the pandemic across countries by employing a rich dataset sourced from 94 countries updated till 6 February,*** empirical analysis,the study makes use of cross-sectional linear regression technique in the first part and after required diagnostic tests use 2SLS regression technique for correcting possible endogeneity bias in the second *** from the study indicate that factors like total reported cases,population size,population over 70 years of age,extreme poverty,and human development index play significant role in determining COVID-19-related ***,to check the robustness of the findings the present study employed lasso *** from the study highlight the possibility of government intervention to devise appropriate policies to control COVID-related incidence and death.

关键词： COVID-19 deaths Mortality risks Infectious diseases lasso regression Pandemic

Research on tune feedback of the Hefei Light SourceⅡbased on machine learning

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Nuclear Science and Techniques 2022年第3期33卷 45-52页

作者： Yong-Bo Yu Gong-Fa Liu Wei Xu Chuan Li Wei-Min Li Ke Xuan National Synchrotron Radiation Laboratory University of Science and Technology of ChinaHefei 230029AnhuiChina

The theory of tune feedback correction and the principle of a feedback algorithm based on machine learning are introduced,with a focus on the application of lasso regression for tune feedback *** verification and online feedback correction results are *** results show that,after applying machine learning,the feedback accuracy of the tune feedback system was higher,and the betatron tune stability was further improved.

关键词： Storage ring Tune feedback Machine learning lasso regression

Pruning Channels and Fully Convolutional Deep Learning Based Target Detection

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Pruning Channels and Fully Convolutional Deep Learning Based...