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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

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

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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

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

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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

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

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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

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

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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

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

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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

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

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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

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predictive model for resistant starch (RS) in rice (Oryza sativa L.) based on digestibility factors

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Journal of Plant Biochemistry and Biotechnology 2025年 1-18页

作者： Rahaman, Sohel Awana, Monika Goswami, Deepanyeta Kumari, Sweta Bollinedi, Haritha Anand, Anjali Dash, Sukanta Rao, Mahesh Singh, Archana Singh, A.K. Krishnan, Veda Food Matrix Lab Division of Biochemistry ICAR - Indian Agricultural Research Institute New Delhi India Division of Genetics ICAR - Indian Agricultural Research Institute New Delhi India Division of Plant Physiology ICAR- Indian Agricultural Research Institute New Delhi India Division of Design of Experiments ICAR- Indian Agricultural Statistics Research Institute New Delhi India National Institute of Plant Biotechnology (NIPB) New Delhi India

Altering the digestibility of starch to enhance the resistance to digestion, specifically targeting the creation of resistant starch (RS), holds significant importance in the fields of agriculture, food, and nutrition. This modification not only restricts the amplitude of glycemic response but also promotes gut health. Traditionally, quantifying RS has relied on complex, time-consuming, and costly human digestion simulation assays. Acknowledging the association between starch digestibility and various factors, such as microstructure, gelatinization temperature, total starch (TS), total amylose, and amylopectin, this study aimed to establish a fundamental relationship among these explanatory variables and RS through the development of a forecasting model known as the starch quality matrix (SQM). Constructed using Pearson’s correlation, the SQM proved to be significant based on model statistics, and the regression model’s adequacy was confirmed through residual diagnosis. Notably, both TS and total amylose content exhibited a significantly positive impact on RS, with coefficients of 0.030 and 0.024, respectively. model validation utilized root mean square error and mean absolute error. The correlation between RS and inherent glycemic potential was further verified through in-house developed in-vitro starch hydrolyzation kinetics. This study unveils, for the first time, a perspective on the relationship influencing starch digestibility and introduces the SQM tool. This tool is poised to facilitate future efforts in breeding high-RS rice varieties with a low glycemic index. © The Author(s), under exclusive licence to Society for Plant Biochemistry and Biotechnology 2025.

关键词： Glycemic potential predictive model Resistant starch Starch quality Starch quality matrix

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A predictive model of photosynthetic rates for eggplants: Integrating physiological and environmental parameters

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 234卷

作者： Gao, Pan Lu, Miao Yang, Yongxia Li, Huiming Tian, Shijie Hu, Jin Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China

Photosynthesis plays a pivotal role in vegetable growth. However, its intricate interplay with plant physiology and environmental factors complicates precise prediction of photosynthetic rates (Pn). Current predictive models primarily focus on environmental influences on photosynthesis, limiting their applicability to leaves exhibiting different physiological traits. To address the challenge, we introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies. Eggplant leaves were used as experimental samples. We collected 5280 Pn data of leaves with different ChlF parameters under controlled changes in temperature, [CO2], and light intensity. The Fo (initial fluorescence) and Fv/Fm (Maximum light energy conversion efficiency of PSII system) were selected as key ChlF indicators using the entropy method. Fo and Fv/Fm, along with temperature, [CO2], and light intensity, are key features, while Pn serves as a label, forming a robust modeling dataset. Then, we proposed a Convolutional Neural Network Regression model with Input Encoding and Genetic Algorithm optimization (CNNR-IEGA) to train these environment and fluorescence data and develop the predictive model for eggplant *** results indicate that the model exhibits excellent performance in predicting Pn. On unknown datasets, the root mean square error of the model is only 0.97 mu mol center dot m- 2 center dot s- 1, with a high coefficient of determination reaching 0.99. Compared to models established by other algorithms (including multiple nonlinear regression, support vector regression, and back propagation neural network), the proposed model demonstrates superior performance across training, testing, and validation sets. Furthermore, compared to models without ChlF parameters and those with single ChlF parameters, the proposed model has the highest accuracy. This demonstrates the validity of using fluorescence to characterize

关键词： Photosynthetic rate Chlorophyll fluorescence predictive model Neural networks Optimization algorithms

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Surveillance of microbial pathogens and development of a predictive model for Staphylococcus aureus in dried fish fillet

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FOOD SCIENCE AND BIOTECHNOLOGY 2025年第9期34卷 2061-2070页

作者： Han, Jeong A. Hwang, Sunyoung Son, Nari Kwak, Hyo-Sun Joo, In Sun Ryu, Sangryeol Suh, Soo Hwan Minist Food & Drug Safety Natl Inst Food & Drug Safety Evaluat Div Microbiol Cheongju 28159 Chungbuk South Korea Seoul Natl Univ Res Inst Agr & Life Sci Dept Agr Biotechnol Dept Food & Anim Biotechnol Seoul 08826 South Korea Konyang Univ Dept Pharmaceu & Biotechnol 158 Gwanjeodong Ro Daejeon 36365 South Korea

This study aimed to achieve two primary objectives: (1) to evaluate microbial contamination in 580 dried fish fillets for the presence of key foodborne pathogens, including S. aureus, Salmonella spp., L. monocytogenes, C. perfringens, and Vibrio spp.;and (2) to develop predictive survival models for S. aureus in dried squid, Hwangtae-chae (dried pollack) and seasoned dried filefish. Microbial surveillance detected no, Salmonella spp., L. monocytogenes, or Vibrio spp.;however, S. aureus and C. perfringens was identified in one and four samples, respectively. predictive modeling involved inoculating dried squid, Hwangtae-chae and seasoned dried filefish with S. aureus and storing them at 4 degrees C, 10 degrees C, 20 degrees C and 37 degrees C. The survival rates, analyzed using the Weibull distribution, exhibited clear temperature-dependent behavior. Secondary models, validated with Root Mean Square Error (RMSE) values of 0.39, 0.36 and 0.35 demonstrate their utility as effective tools for microbial risk assessment and enhancing food safety management practices.

关键词： Staphylococcus aureus predictive model Dried fish fillet Dried squid Dried pollack

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