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Compressive strength prediction of sprayed concrete lining in tunnel engineering using hybrid machine learning techniques

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Underground Space 2022年第5期7卷 928-943页

作者： Xin Yin Feng Gao Jian Wu xing huang Yucong Pan Quansheng Liu The Key Laboratory of Safety for Geotechnical and Structural Engineering of Hubei Province School of Civil EngineeringWuhan UniversityWuhan 430072China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhan 430072China PowerChina Huadong Engineering Corporation Limited Hangzhou 311122China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China

Sprayed concrete lining is a commonly employed support measure in tunnel engineering,which plays an important role in construction safety.Compressive strength is a key performance indicator of sprayed concrete lining,and the traditional measuring method is time-consuming and laborious.This paper proposes various hybrid machine learning algorithms to accomplish the advanced prediction of compressive strength of sprayed concrete lining based on the mixture design.Two hundred and five sets of experimental data were collected from a water conveyance tunnel in northwestern China for model construction,and each set of data was made up of six basic input variables(i.e.,water,cement,mineral powder,superplasticizer,coarse aggregate,and fine aggregate)and one output variable(i.e.,compressive strength).In order to eliminate the correlation between input variables,a new composite indicator(i.e.,the water-binder ratio)was introduced to achieve dimensionality reduction.After that,four hybrid models in total were built,namely BPNN-QPSO,SVR-QPSO,ELM-QPSO,and RF-QPSO,where the hyper-parameters of BPNN,SVR,ELM,and RF were auto-tuned by QPSO.Engineering application results indicated that RF-QPSO achieved the lowest mean absolute percentage error(MAPE)of 3.47% and root mean square error(RMSE)of 1.30 and the highest determination coefficient(R^(2))of 0.93 in the four hybrid models.Moreover,RFQPSO had the shortest running time of 0.15 s,followed by SVR-QPSO(0.18 s),ELM-QPSO(1.19 s),and BPNN-QPSO(1.58 s).Compared with BPNN-QPSO,SVR-QPSO,and ELM-QPSO,RF-QPSO performed the most superior performance in terms of both prediction accuracy and running speed.Finally,the importance of input variables on the model performance was quantitatively evaluated,further enhancing the interpretability of RF-QPSO.

关键词： Intelligent construction Hybrid machine learning Sprayed concrete lining Compressive strength prediction Tunnel engineering

Magnetic-biased chiral molecules enabling highly oriented photovoltaic perovskites

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National Science Review 2024年第2期11卷 221-230页

作者： Jing Chen Caner Deger Zhen-huang Su Kai-Li Wang Guang-Peng Zhu Jun-Jie Wu Bing-Chen He Chun-Hao Chen Tao Wang xing-Yu Gao Ilhan Yavuz Yan-Hui Lou Zhao-Kui Wang Liang-Sheng Liao Institute of Functional Nano&Soft Materials(FUNSOM) Laboratory of Advanced Negative Carbon TechnologiesSoochow University Department of Physics Marmara University Shanghai Synchrotron Radiation Facility(SSRF) Shanghai Advanced Research InstituteChinese Academy of Sciences College of Energy Soochow Institute for Energy and Materials Innovations Soochow University Macao Institute of Materials Science and Engineering Macau University of Science and Technology

The interaction between sites A, B and X with passivation molecules is restricted when the conventional passivation strategy is applied in perovskite（ABX3） photovoltaics. Fortunately, the revolving A-site presents an opportunity to strengthen this interaction by utilizing an external field. Herein, we propose a novel approach to achieving an ordered magnetic dipole moment, which is regulated by a magnetic field via the coupling effect between the chiral passivation molecule and the A-site（formamidine ion） in perovskites.This strategy can increase the molecular interaction energy by approximately four times and ensure a well-ordered molecular arrangement. The quality of the deposited perovskite film is significantly optimized with inhibited nonradiative recombination. It manages to reduce the open-circuit voltage loss of photovoltaic devices to 360 mV and increase the power conversion efficiency to 25.22%. This finding provides a new insight into the exploration of A-sites in perovskites and offers a novel route to improving the device performance of perovskite photovoltaics.

关键词： perovskite photovoltaics magnetic moment A-site regulation chiral molecules

Efficacy and safety of anlotinib combined with the STUPP regimen in patients with newly diagnosed glioblastoma: a multicenter, single-arm, phase Ⅱ trial

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Cancer Biology & Medicine 2024年第5期21卷 433-444页

作者： Shuzhen Lai Peijing Li Xiaohui Liu Guihong Liu Tieming Xie xing Zhang Xiaoxuan Wang Jing huang Yiqiang Tang Zhigang Liu Guoping Shen Chaoming Li Fangxiao Lu Lei Wang Fagui Jiang Caixing Sun Yuanyuan Chen Ming Chen Department of Radiotherapy Dongguan Institute of Clinical Cancer Research Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors Affiliated Dongguan Hospital Southern Medical University The Cancer Center The Fifth Affiliated Hospital of Sun Yat-Sen University Department of Radiation Oncology The First Affiliated Hospital of Sun Yat-Sen University Department of Oncology Maoming People's Hospital Department of Radiation Oncology Kecheng District People's Hospital Chinese Academy of Sciences Department of Radiation Oncology State Key Laboratory of Oncology in South China Sun Yat-sen University Cancer Center United Laboratory of Frontier Radiotherapy Technology of Sun Yat-sen University & Chinese Academy of Sciences Ion Medical Technology Co. Ltd Department of Radiation Oncology The Second Affiliated Hospital of Soochow University Department of Radiation Oncology Yuebei People's Hospital Affiliated to the Medical College of Shantou University Chinese Academy of Sciences Department of Radiation Oncology The Second Clinical Medical College of Zhejiang Chinese Medical University Department of Radiation Oncology The Affiliated Hospital of Xuzhou Medical University Chinese Academy of Sciences State Key Laboratory of Translational Medicine and Innovative Drug Development Jiangsu Simcere Diagnostics Co. Ltd Department of Radiation Oncology The Affiliated Huai'an First People's Hospital of Nanjing Medical University Department of Radiation Oncology Jiangxi Cancer Hospital of Nanchang University

Objective: Glioblastomas are highly vascularized malignant tumors. We determined the efficacy and safety of the anti-angiogenic multi-kinase inhibitor, anlotinib, for a newly diagnosed glioblastoma.Methods: This multicenter, single-arm trial（NCT04119674） enrolled 33 treatment-na?ve patients with histologically proven glioblastomas between March 2019 and November 2020. Patients underwent treatment with the standard STUPP regimen [fractionated focal irradiation in daily fractions of 1.8-2 Gy given 5 d/w × 6 w（total = 54-60 Gy）] or radiotherapy plus continuous daily temozolomide（TMZ）(75 mg/m2of body surface area/d, 7 d/w from the first to the last day of radiotherapy), followed by 6 cycles of adjuvant TMZ（150±200 mg/m2× 5 d during each 28-d cycle） plus anlotinib(8 mg/d on d 1±14 of each 3-w cycle for 2 cycles during concomitant chemoradiotherapy, 8 maximal cycles as adjuvant therapy, followed by maintenance at 8 mg/d. The primary endpoint was progression-free survival（PFS）. Secondary endpoints included overall survival（OS） and adverse events（AEs）.Results: Thirty-three patients received the planned treatment. The median PFS was 10.9 months（95% CI, 9.9±18.7 months） and the 12-month PFS rate was 48.5%. The median OS was 17.4 months（95% CI, 14.5±21.1 months） and the 12-month OS rate was 81.8%. The most common AEs included hypertriglyceridemia [58%（n = 19）], hypoalbuminemia [46%（n = 15）], and hypercholesterolemia [46%（n = 15）] during concurrent chemoradiotherapy and leukopenia [73%（n = 24）], hypertriglyceridemia [67%（n = 22）], and neutropenia [52%（n = 17）] during adjuvant therapy. Five patients discontinued treatment due to AEs. HEG1（HR, 5.6; 95% CI, 1.3±23.7; P = 0.021） and RP1L1 alterations（HR, 11.1; 95% CI, 2.2±57.2; P = 0.004） were associated with a significantly shorter PFS.Conclusions: Anlotinib plus the STUPP regimen has promising anti-tumor activity against glioblastoma and manageable toxicity. HEG1 and RP1L1 alterations might be novel predictive biomarkers of the response to anlotinib.

关键词： Glioblastoma anlotinib anti-angiogenesis multi-kinase inhibitor progression-free survival temozolomide

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World Journal of Clinical Cases 2022年第11期10卷 3334-3351页

作者： Yue Qiu Hong-Tao Wang Xi-Fan Zheng xing huang Jin-Zhi Meng Jun-Pu huang Zhen-Pei Wen Jun Yao Department of Bone and Joint Surgery The First Affiliated Hospital of Guangxi Medical UniversityNanning 530000Guangxi Zhuang Autonomous RegionChina Department of Bone and Joint Surgery Guangxi Medical UniversityNanning 530000Guangxi Zhuang Autonomous RegionChina Department of Orthopedics The First Affiliated Hospital of Guangxi Medical UniversityGuangxi Medical UniversityNanning 530000Guangxi Zhuang Autonomous RegionChina

BACKGROUND Melanomas are malignant tumors that can occur in different body parts or tissues such as the skin,mucous membrane,uvea,and pia mater.Long non-coding RNAs(lncRNAs)are key factors in the occurrence and development of many malignant tumors,and are involved in the prognosis of some patients.AIM To identify autophagy-related lncRNAs in melanoma that are crucial for the diagnosis,treatment,and prognosis of melanoma patients.METHODS We retrieved transcriptome expression profiles and clinical information of 470 melanoma patients from The Cancer Genome Atlas(TCGA)database.Then,we identified autophagy-related genes in the Human Autophagy Database.Using R,coexpression analysis of lncRNAs and autophagy-related genes was conducted to obtain autophagy-related lncRNAs and their expression levels.We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets,to systematically evaluate the prognostic value of autophagyrelated lncRNAs in melanoma.Fifteen autophagy-related lncRNAs were identified and an autophagy-related prognostic signature for melanoma was established.The Kaplan-Meier and univariate and multivariate Cox regression analyses were used to calculate risk scores.Based on the risk scores,melanoma patients were randomly divided into high-and low-risk groups.Receiver operating characteristic curve analysis,dependent on time,was performed to assess the accuracy of the prognostic model.At the same time,we also downloaded the melanoma data sets GSE65904,GSE19234,and GSE78220 from the GENE EXPRESSION OMNIBUS database for model verification.Finally,we performed Gene Set Enrichment Analysis functional annotation,which showed that the low and the high-risk groups had different enriched pathways.RESULTS The co-expression network for autophagy-related genes was constructed using R,and 936 lncRNAs related to autophagy were identified.Then,52 autophagy-related lncRNAs were significantly associated with TCGA melanoma patients’survival by univariate Cox proportional risk analysis(P<0.

关键词： Melanoma Long non-coding RNAs Autophagy Prognosis The Cancer Genome Atlas Bioinformatics

Nickel-catalyzed arylcyanation of alkenes via cyano group translocation: access to 1,n-dinitriles or 4-amino nitriles

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Science China Chemistry 2024年

作者： Xiaohong Li Shenfan Wang Xiangxiang Fu Donghui xing Zeyuan Fu Yuanfu Deng Huanfeng Jiang Liangbin huang State Key Laboratory of Pulp and Paper Engineering School of Chemistry and Chemical Engineering South China University of Technology

Herein, a nickel-catalyzed arylcyanation of unactivated alkenes via cyano group translocation with aryl boronic acids has been developed. These transformations provided a robust approach to constructing structurally diverse 1,n-dinitriles or 4-amino nitriles from easily prepared and commercially available starting materials. The cyano group translocation was achieved,involving the addition into the intramolecular C–N triple bond followed by the retro-Thorpe reaction. Mechanistic studies revealed that high temperature and CsHCO3as the base were crucial for the cyano group translocation.

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A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第3期14卷 798-812页

作者： xing huang Quantai Zhang Quansheng Liu Xuewei Liu Bin Liu Junjie Wang Xin Yin State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan430071China Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province School of Civil EngineeringWuhan UniversityWuhan430072China The 2nd Engineering Company of China Railway 12th Bureau Group Taiyuan030032China

Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the increment

关键词： Tunnel boring machine(TBM) Real-time cutter-head torque prediction Bidirectional long short-term memory (BLSTM) Bayesian optimization Multi-algorithm fusion optimization Incremental learning

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Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

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China Geology 2024年第1期7卷 104-115页

作者： Ao Zhang Xin-wen Zhao xing-yuezi Zhao Xiao-zhan Zheng Min Zeng Xuan huang Pan Wu Tuo Jiang Shi-chang Wang Jun He Yi-yong Li Wuhan Center China Geological SurveyMinistry of Natural Resources(Geosciences Innovation Center of Central South China)Wuhan 430205China Guangzhou Institute of Geological Survey Guangzhou 510080China Hubei Transportation Planning Design Institute Co. LtdWuhan 430050China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

关键词： Landslides susceptibility assessment Machine learning Logistic Regression Random Forest Support Vector Machines XGBoost Assessment model Geological disaster investigation and prevention engineering

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Robust model for tunnel squeezing using Bayesian optimized classifiers with partially missing database

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Underground Space 2023年第3期10卷 91-117页

作者： Yin Bo xing huang Yucong Pan Yanfang Feng Penghai Deng Feng Gao Ping Liu Quansheng Liu Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province School of Civil EngineeringWuhan UniversityWuhanHubei 430072China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhanHubei 430072China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanHubei 430071China

Accurately predicting and estimating the squeezing and ground response to tunneling remains challenging.Moreover,tunnel-squeezing hazards are much more likely to occur in deeply buried long tunnels with complex engineering-geological environments.There-fore,a high-performance predictive model for tunnel squeezing is necessary.A superior ensemble classifier is put forward in this study,which is composed of four individual classifiers(gradient boosting classifier,extra-trees classifier,AdaBoost classifier,and Logistic regression classifier)and two optimization algorithms(Bayesian optimization(BO)and sparrow search algorithm(SSA)).The training database covers five parameters:tunnel depth(H),rock tunneling quality index(Q),tunnel diameter(D),support stiffness(K),and strength stress ratio(SSR),about which the basic information is accessible at the early design phases.However,the dataset compiled from the literature is insufficient.Thus,the ten proposed methods are used to replace the missing values.During the model training pro-cess,BO shows its strong ability to optimize seventeen hyperparameters.When applied to tune the classifiers’weights,SSA achieves a fast and efficient performance.The novel Shapley Additive Explanations–LightGBM method indicates that the K is the most important input feature,followed by SSR,Q,H,and D,respectively.The ensemble classifier is then validated using the test set and additional his-torical case projects.The validation shows that the model can achieve an accuracy of 98%(i.e.,the error rate is 2%)on the test set,higher than those achieved by previous prediction models.Moreover,the predicted probability could provide warning information for timely support measures.Finally,the application results are illustrated through tests on the tunnel sections that have not yet been excavated in the line of the Sichuan–Tibet railway project.The applied predictive tendencies and laws are in line with the practical experience.In sum-mary,the proposed model’s prediction results are reasonable,and its pre

关键词： Tunnel squeezing hazard Bayesian optimization Machine learning techniques Sparrow search algorithm Ensemble classifier Incomplete database

Effect of endometrial thickness on the risk of ectopic pregnancy for frozen embryo transfer

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生殖与发育医学(英文) 2024年第2期8卷 75-81页

作者： huang Li-Juan Zheng Qu Wang Xue-Jiao Zhong Zhao-Hui Fu Li-Juan Lv xing-Yu Geng Li-Hong Tang Xiao-Jun Ding Yu-Bin Wan Qi Department of Obstetrics and Gynecology Women and Children’s Hospital of Chongqing Medical University Chongqing China School of Public Health Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China Chongqing China The Reproductive Center Chengdu Xinan Gynecology Hospital Chengdu China School of Public Health Research Center for Medical and Social Development Chongqing Medical University Chongqing China Department of Pharmacology Academician Workstation Changsha Medical University Changsha China Department of Obstetrics and Gynecology West China Second Hospital Sichuan University Chengdu China

Objective: Few studies have explored the suitability of endometrial thickness (EMT) as a predictor of ectopic pregnancy (EP) in frozen embryo transfer (FET) cycles. Our study aimed to investigate the association between EMT and EP and explore the cutoff value of EMT to predict EP.Methods: This retrospective cohort study included women aged 20-45 years undergoing FET between March 2015 and September 2021. The primary outcomes and measured variables were EP and EMT, respectively. The threshold effect of EMT on EP was calculated based on a constructed smooth curve fitting. The patients were divided into two groups based on the threshold for comparison. A subgroup analysis was conducted to examine whether the relationship between EMT and EP remained consistent across the different subgroups. Additionally, a generalized linear model was used to analyze the interactions between the treatment factors and EMT in EP.Results: In total, 27,549 FET cycles were included, with 27,086 intrauterine and 463 ectopic pregnancies. The risk of EP in the EMT ≤8.7 mm group was 3.36 times higher than that in the EMT >8.7 mm group (odds ratio [OR]: 3.36, 95% confidence interval [CI]: 2.57-4.05). Subgroup analysis further indicated that a thin EMT was a risk factor for EP, independent of other risks. We also noted that diminished ovarian reserve, tubal factor infertility, transfer of two embryos, or transfer of cleavage-stage embryos may further enhance the risk of EP in patients with thin endometrium.Conclusions: Thin endometrium was associated with an increased risk of EP. Patients with EMT less than 8.7 mm need close medical observation and management after embryo transfer. In addition, diminished ovarian reserve, tubal factor infertility, transfer of two embryos, or transfer of cleavage-stage embryos may significantly enhance the effect of a thin endometrium on the risk of EP.

关键词： Endometrial thickness Thin endometrium Ectopic pregnancy FET cycle Risk factors