检索结果-内蒙古大学图书馆

predictive model of lunar soil drilling resistance considering axis inclination

ADVANCES IN SPACE RESEARCH 2025年第1期75卷 1565-1584页

作者： Zhou, Jun Liu, Tianxi Zhao, Yang Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China

The axis inclination of a slender spiral drill tool is inevitable while drilling and sampling lunar soil;this alters the stress situation of the drill tool. In this paper, the interaction process between the drill tool and lunar soil is simplified and equated to the process of cylindrical cavity expansion caused by bit penetration and the process of soil agitation caused by the inclined auger, and the predictive model of drilling resistance is constructed. The sensitivities of the main soil mechanical parameters are analyzed, and it indicates that cohesion, soil pressure coefficient, and gravitational acceleration have an obvious positive effect on drilling resistance. A simulated lunar soil drilling test is conducted, and the effectiveness of the drilling resistance predictive model are verified by comparing the test and simulation results, providing theoretical support for technological breakthroughs in other extraterrestrial drilling and sampling missions. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Lunar soil drilling and sampling Drilling resistance predictive model Ground test

来源：评论

学校读者我要写书评

暂无评论

predictive model for assessing the nonlinear surface displacement and mechanical response of shallowly buried tunnels under dip-slip fault dislocation

引用

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2025年 191卷

作者： Wang, Mingnian Yang, Henghong Yu, Li Zhang, Xiao Southwest Jiaotong Univ Sch Civil Engn Chengdu 610036 Peoples R China Southwest Jiaotong Univ State Key Lab Intelligent Geotech & Tunnelling Chengdu 610036 Peoples R China

The fault dislocation induces permanent ground surface displacement, leading to severe damage to tunnels. However, there is a notable scarcity of predictive models for nonlinear surface displacement and tunnel response under dip-slip fault dislocation. Previous analytical models have oversimplified surface displacement to a constant value. To this end, first, a predictive model for assessing the mechanical response of shallowly buried tunnels under dip-slip fault dislocation is established. Then, through a mathematical statistical analysis of field- measured data, a prediction method of nonlinear dip-slip surface displacement has been developed, and the nonlinear dip-slip surface displacement is introduced into the predictive model. The predictive model incorporates nonlinear surface displacement, fault zone width, and geometric nonlinearity, thereby markedly enhancing the accuracy of the calculation results. Secondly, the prediction model undergoes validation through experimental tests and numerical simulations, revealing a maximum error of 3.7 %. In contrast, neglecting nonlinear surface displacement can result in calculation errors as high as 517.5 %. Finally, the proposed predictive model is applied to conduct a parameter analysis, such as maximum surface displacement (Delta dmax), dip angle (alpha), and fault zone width (WF). The results shown that the maximum axial force (Nmax), maximum shear force (Vzmax), and maximum bending moment (Mzmax) of the tunnel increase with the augmentation of Delta dmax. For each incremental increase of 0.2 m in Delta dmax, the Nmax, Vzmax, and Mzmax exhibit an approximate increase of 22.1 %-100.3 %. The Nmax and decreases with the increasing alpha, whereas both the Vzmax and the Mzmax increase as alpha rises. With each incremental increase of 10 degrees in alpha, the Nmax diminishes by approximately 16.1 %-49.2 %, while both the Vzmax and Mzmax experience an increase ranging from about 4.6 % to 19.1 %. An increase in WF results i

关键词： Dip-slip faults Tunnel engineering predictive model Tunnel response Nonlinear surface displacement

来源：评论

学校读者我要写书评

暂无评论

predictive model-based multi-objective optimization with life-long meta-learning for designing unreliable production systems

引用

COMPUTERS & OPERATIONS RESEARCH 2025年 178卷

作者： Mahmoodi, Ehsan Fathi, Masood Ng, Amos H. C. Dolgui, Alexandre Univ Skovde Sch Engn Sci Div Intelligent Prod Syst Skovde 54128 Sweden Uppsala Univ Dept Civil & Ind Engn Div Ind Engn & Management Uppsala Sweden CNRS IMT Atlant LS2N La Chantrerie4rue Alfred Kastler F-44307 Nantes 3 France

Owing to the realization of advanced manufacturing systems, manufacturers have more flexibility in improving their processes through design decisions. Design decisions in production lines primarily involve two complex problems: buffer and resource allocation (B&RA). The main aim of B&RA is to determine the best location and size of buffers in the production line and optimally allocate production resources, such as operators and machines, to workstations. Inspired by a real-world case from the marine engine production industry, this study addresses B&RA in high-mix, low-volume hybrid flow shops (HFSs) with feed-forward quality inspection. These HFSs can be characterized by uncertainties in demand, material handling, processing times, and quality control. In this study, the production environment is modeled via discrete-event simulation, which reflects the features of the actual system without requiring unreasonable or restrictive assumptions. To replace the expensive simulation runs, five widely used regressor machine learning algorithms in manufacturing are trained on data sampled from the simulation model, and the best-performing algorithm is selected as the predictive model. To obtain high-quality solutions, the predictive model is coupled with an enhanced non-dominated sorting genetic algorithm (En-NSGA-II) that incorporates lifelong meta-learning and features a customized representation and a variable neighborhood search. Additionally, a post-optimality analysis using a pattern-mining algorithm is performed to generate knowledge for allocating buffers and operators based on the optimization results, thus providing promising managerial insights.

关键词： Multi-objective optimization Simulation predictive model Meta-learning Buffer allocation Resource allocation

来源：评论

学校读者我要写书评

暂无评论

predictive model in silicon and pathogenicity mechanism of metabolic syndrome: Impacts of heavy metal exposure

引用

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 373卷 124001页

作者： Zhou, Jing-Xuan Zheng, Zi-Yi Peng, Zhao-Xing Yang, Yu-Ting Ni, Hong-Gang Peking Univ Shenzhen Grad Sch Sch Urban Planning & Design Shenzhen 518055 Peoples R China

Although the association between heavy metals in human and the development of metabolic syndrome (MetS) have been extensively studied, the pathogenic mechanism of MetS affected by metals is not clear to date. In this study, a predictive model was developed with machine learning base on the large-scale dataset. These proposed models were evaluated via comparatively analysis of their accuracy and robustness. With the optimal model, two metals significantly correlated with MetS were screened and were employed to infer the pathogenicity mechanism of MetS via molecular docking. Significant associations between heavy metals and MetS were found. Molecular docking provided insights into the interactions between metal ions and key protein receptors involved in metabolic regulation, suggesting a mechanism by which heavy metals interfere with metabolic functions. Specifically, Ba and Cd affect the development of MetS thru their interactions with insulin and estrogen receptors. This study attempted to explore heavy metals' potential roles in MetS at the molecular level. These findings emphasize the importance of addressing environmental exposures in the prevention and treatment of MetS.

关键词： predictive model Metabolic syndrome Heavy metal Pathogenesis Molecular docking

来源：评论

学校读者我要写书评

暂无评论

predictive model for the interface bond strength of geosynthetic-reinforced asphalt layers

引用

GEOTEXTILES AND GEOMEMBRANES 2024年第4期52卷 841-854页

作者： Silva, M. P. S. Zornberg, J. G. Correia, N. S. Univ Fed Sao Carlos Civil Engn Dept Washington Luis Rdkm 235 BR-13565905 Sao Carlos SP Brazil Univ Texas Austin Civil Engn Dept Univ Stn C1792 Austin TX 78712 USA

Innovation in the use of geosynthetics in roadway applications has resulted in the development of several different products, such as asphalt reinforcement geogrids and paving mats to minimize reflective cracks and limit moisture infiltration. For a proper performance of the reinforced asphalt systems, an adequate interface bond strength has proven to be crucial. However, the influence of the different paving interlayer characteristics, combined with tack coat types, and tack coat rates on the interface bond strength remains unclear. In this study, a comprehensive program of interface shear tests was conducted using Leutner shear device and laboratoryprepared reinforced asphalt specimens. The program involved eight geosynthetic types, two tack coat types, and three application rates. Results revealed that the geosynthetic type, tack coat type and rate, as well as the interactions among the parameters significantly affect interface bond strength. Multiple linear regression analysis indicated that geogrid aperture area, geosynthetic thickness, geotextile backing thickness, and the presence of bitumen coating are the most affecting parameters on bond strength. A predictive model for the bond strength based on geosynthetic parameters is presented. Based on literature results for specimens extracted from the field, the proposed predictive models were found to adequately predict interface bond strength.

关键词： Geosynthetics Asphalt layer Reinforced asphalt system Interface bond strength Geosynthetics parameters predictive model

来源：评论

学校读者我要写书评

暂无评论

predictive model for initial response to first-line treatment in children with infantile epileptic spasms syndrome

引用

ITALIAN JOURNAL OF PEDIATRICS 2025年第1期51卷 1-12页

作者： Ge, Wenrong Wan, Lin Wang, Zong Fu, Lijun Yang, Guang Capital Med Univ Beijing Friendship Hosp Dept Paediat Beijing Peoples R China Peoples Liberat Army Gen Hosp Senior Dept Pediat Med Ctr 7 Beijing 100000 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 1 Dept Paediat Beijing Peoples R China Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shandong Univ Lab Big Data & Artificial Intelligence Technol Jinan Peoples R China Chinese Peoples Liberat Army Med Sch Beijing Peoples R China Southern Med Univ Sch Clin Med 2 Guangzhou Peoples R China

BackgroundPrevious studies have suggested that factors such as the treatment interval and aetiology may influence the initial response rate to first-line treatment for infantile epileptic spasms syndrome (IESS). However, few children with IECSS have undergone clinically accessible tests to determine the *** a dataset from our previously published research, we constructed and tested a predictive model for the initial response to first-line treatment in children with IESS. Random sampling and 5-fold cross-validation were performed, with synthetic minority oversampling technique to correct data imbalance. Machine learning algorithms and evaluation metrics optimised model accuracy and *** study included 532 children with IESS who had completed monotherapy first-line treatment, of whom 160 achieved an initial response. The model's accuracy, F1 score, and area under the curve (AUC) in the validation set were 0.7836 +/- 0.0229 (ranging from 0.75167 to 0.80536), 0.7833 +/- 0.0229 (ranging from 0.75145 to 0.80531), and 0.8516 +/- 0.0165 (ranging from 0.82468 to 0.86936), respectively. Factors such as the age of seizure onset, age of spasm onset, lead time, MRI subtype, treatment choice, and age at treatment consistently ranked in the top six for importance in contributing to the *** study findings suggest that this model may help effectively predict the initial response to first-line treatment, supporting clinical decision-making for children with IESS. Key predictors such as the age of seizure onset and MRI subtype enable early, data-driven intervention strategies in clinical practice.

关键词： Infantile epileptic spasms syndrome (IESS) First-line treatment Initial response predictive model

来源：评论

学校读者我要写书评

暂无评论

predictive model development combining CT-FFR and SYNTAX score for major adverse cardiovascular events in complex coronary artery disease

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Luo, Weiqing Li, Chen Yan, Guangdong Huang, Zhichuan Yue, Yilin Yang, Deguang Zhang, Shaoheng Jinan Univ Guangzhou 510632 Peoples R China Jinan Univ Dept Cardiol Guangzhou Red Cross Hosp 396 Tongfu Middle Rd Guangzhou 510220 Peoples R China Jinan Univ Dept Cardiol Affiliated Hosp 1 Guangzhou 510632 Peoples R China

Patients with complex coronary artery disease (CAD) often have poor clinical outcomes. This study aimed to develop a predictive model for assessing the 1-year risk of major adverse cardiovascular events (MACE) in patients with stable complex CAD, using retrospective data collected from January 2020 to September 2023 at Guangzhou Red Cross Hospital. The goal was to enable early risk stratification and intervention to improve clinical outcomes. A total of 369 patients were included and randomly divided into a training set (70%) for model development and a validation set (30%) for performance evaluation. predictive factors were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by logistic regression to construct the model and create a nomogram. Seven independent predictors were identified: functional SYNTAX score (OR 1.257, 95% CI 1.159-1.375), low-density lipoprotein cholesterol (LDL-C, OR 1.487, 95% CI 1.147-1.963, /1mmol/L), left ventricular ejection fraction (LVEF, OR 0.934, 95% CI 0.882-0.985, /1%), albumin (OR 0.889, 95% CI 0.809-0.974, /1g/L), pulse pressure >= 72 mmHg (OR 3.358, 95% CI 1.621-7.118), angiotensin-converting enzyme 2 (ACE2) >= 27.5 U/L (OR 2.503, 95% CI 1.290-5.014), and diabetes (OR 2.261, 95% CI 1.186-4.397). Among these, the functional SYNTAX score was the strongest predictor. The area under the receiver operating characteristic curve (AUC) was 0.843 for the training set and 0.844 for the validation set, with Youden indices of 0.561 and 0.601, respectively. Calibration curves and decision curve analysis demonstrated good predictive accuracy and clinical utility of the model. These findings suggest that the developed model has strong predictive performance for 1-year MACE risk in patients with complex CAD, and early risk stratification and intervention based on this model may improve clinical outcomes.

关键词： Coronary artery disease Coronary CT-derived fractional flow reserve Functional SYNTAX score Major adverse cardiovascular events predictive model

来源：评论

学校读者我要写书评

暂无评论

predictive model for the progression of inactive thyroid eye disease: a retrospective study

引用

ENDOCRINE 2024年第2期84卷 533-540页

作者： Qi, Lingzi Song, Xuefei Li, Yinwei Sun, Jing Zhou, Huifang Fan, Xianqun Shanghai Jiao Tong Univ Peoples Hosp 9 Dept Ophthalmol Sch Med Shanghai 200025 Peoples R China Shanghai Key Lab Orbital Dis & Ocular Oncol Shanghai 200025 Peoples R China

Purpose This study aimed to develop a model for predicting the progression of inactive thyroid eye disease (TED).Methods We retrospectively analyzed 107 patients with inactive TED, who did not receive any TED treatment during the follow-up period of 3-6 months. A multivariable regression analysis was employed to examine the clinical risk factors influencing the progress of TED and a predictive model based on risk factors was *** Seventeen (15.9%) patients had progressed at the last observation. Male (p = 0.024), having smoking history (p = 0.009), compound indicator combined the duration of TED and immunosuppressive treatment history before 3 months (p = 0.026) and more severe diplopia (p = 0.015) were found to be the potential risk factors for progression. Among the subset of patients who exhibited progression, there was a notable escalation in the severity of proptosis (p = 0.021), margin reflex distance-1 (p = 0.031), and clinical activity score (p = 0.011). The multivariable stepwise regression analysis identified positive smoking history as well as untreated and having a duration of TED > 12 months as independent risk factors of TED progression. A predictive model including these risk factors was built, with the AUC (Area Under Curve) score of *** The predictors of TED progression include smoking history and composite indicators of duration time and treatment history. Our model may provide recommendations for immunosuppressive therapy and prognostic predictions for inactive TED patients preparing for surgery.

关键词： Thyroid eye disease Risk factors predictive model Progression

来源：评论

学校读者我要写书评

暂无评论

predictive model for HBsAg clearance rate in chronic hepatitis B patients treated with pegylated interferon α-2b for 48 weeks

引用

HEPATOLOGY INTERNATIONAL 2025年第2期19卷 358-367页

作者： Tan, Zhili Kong, Nan Zhang, Qiran Gao, Xiaohong Shang, Jia Geng, Jiawei You, Ruirui Wang, Tao Guo, Ying Wu, Xiaoping Zhang, Wenhong Qu, Lihong Zhang, Fengdi Tongji Univ Dept Infect Dis Shanghai East Hosp Sch Med Shanghai Peoples R China Fudan Univ Dept Infect Dis Shanghai Key Lab Infect Dis & Biosafety Emergency Natl Med Ctr Infect DisHuashan HospShanghai Med Shanghai Peoples R China Yanan Univ Affiliated Hosp Dept Infect Dis Yanan Shaanxi Peoples R China Henan Prov Peoples Hosp Dept Infect Dis & Hepat Dis Zhengzhou Henan Peoples R China Kunming Univ Sci & Technol Peoples Hosp Yunnan Prov 1 Dept Infect Dis & Hepat Dis Affiliated Hosp Kunming Yunnan Peoples R China Third Peoples Hosp Taiyuan Deparment Hepatol Taiyuan Peoples R China Nanchang Univ Dept Infect Dis Affiliated Hosp 1 Nanchang Peoples R China

Background and Aims Chronic hepatitis B (CHB) is a major global health concern. This study aims to investigate the factors influencing hepatitis B surface antigen (HBsAg) clearance in CHB patients treated with pegylated interferon alpha-2b (Peg-IFN alpha-2b) for 48 weeks and to establish a predictive model. Methods This analysis is based on the "OASIS" project, a prospective real-world multicenter study in China. We included CHB patients who completed 48 weeks of Peg-IFN alpha-2b treatment. Patients were randomly assigned to a training set and a validation set in a ratio of approximately 4:1 by spss 26.0, and were divided into clearance and non-clearance groups based on HBsAg status at 48 weeks. Clinical data were analyzed using SPSS 26.0, employing chi-square tests for categorical data and Mann-Whitney U tests for continuous variables. Significant factors (p < 0.05) were incorporated into a binary logistic regression model to identify independent predictors of HBsAg clearance. The predictive model's performance was evaluated using ROC curve analysis. Results We included 868 subjects, divided into the clearance group (187 cases) and the non-clearance group (681 cases). They were randomly assigned to a training set (702 cases) and a validation set (166 cases). Key predictors included female gender (OR = 1.879), lower baseline HBsAg levels (OR = 0.371), and cirrhosis (OR = 0.438). The final predictive model was:Logit(P) = 0.92 + Gender (Female) * 0.66 - HBsAg (log) * 0.96 - Cirrhosis * 0.88. ROC analysis showed an AUC of 0.80 for the training set and 0.82 for the validation set, indicating good predictive performance. Conclusion Gender, baseline HBsAg levels, and cirrhosis are significant predictors of HBsAg clearance in CHB patients after 48 weeks of Peg-IFN alpha-2b therapy. The developed predictive model demonstrates high accuracy and potential clinical utility.

关键词： Chronic Hepatitis B (CHB) HBsAg clearance predictive model Logistic regression Clinical prognosis

来源：评论

学校读者我要写书评

暂无评论

predictive model of Internal Bleeding in Elderly Aspirin Users Using XGBoost Machine Learning

引用

RISK MANAGEMENT AND HEALTHCARE POLICY 2024年 17卷 2255-2269页

作者： Chen, Tenggao Lei, Wanlin Wang, Maofeng Wenzhou Med Univ Affiliated Dongyang Hosp Dept Colorectal Surg Dongyang 322100 Zhejiang Peoples R China Wenzhou Med Univ Affiliated Dongyang Hosp Dept Biomed Sci Lab 60 Wuning West Rd Dongyang 322100 Zhejiang Peoples R China

Objective: This study aimed to develop a predictive model for assessing internal bleeding risk in elderly aspirin users using machine learning. Methods: A total of 26,030 elderly aspirin users (aged over 65) were retrospective included in the study. Data on patient demographics, clinical features, underlying diseases, medical history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. Patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation, respectively. Least absolute shrinkage and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and multivariate logistic regression were employed to develop prediction models. model performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC). Results: The XGBoost model exhibited the highest AUC among all models. It consisted of six clinical variables: HGB, PLT, previous bleeding, gastric ulcer, cerebral infarction, and tumor. A visual nomogram was developed based on these six variables. In the training dataset, the model achieved an AUC of 0.842 (95% CI: 0.829-0.855), while in the test dataset, it achieved an AUC of 0.820 (95% CI: 0.800-0.840), demonstrating good discriminatory performance. The calibration curve analysis revealed that the nomogram model closely approximated the ideal curve. Additionally, the DCA curve, CIC, and NRC demonstrated favorable clinical net benefit for the nomogram model. Conclusion: This study successfully developed a predictive model to estimate the risk of bleeding in elderly aspirin users. This model can serve as a potential useful tool for clinicians to estimate the risk of bleeding in elderly aspirin users and make informed decisions regarding their treatment and management.

关键词： aspirin bleeding haemorrhage predictive model extreme gradient boosting nomogram

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：