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Optimization of University Scientific Research Performance Evaluation Management Based on back-propagation artificial neural network

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SENSORS AND MATERIALS 2024年第4期36卷 1575-1590页

作者： Ma, Wanzhi Chu, Na Ningxia Normal Univ Sch Educ Sci Guyuan 756000 Ningxia Peoples R China Woosuk Univ Dept Educ & Culture Contents Dev Jeonju 55338 South Korea Ningxia Normal Univ Sch Foreign Languages Guyuan Ningxia Peoples R China

Public audits in universities have exposed fraudulent practices in scientific research expenditure, posing a significant obstacle to progress. To address this issue, it is imperative to conduct risk assessment and analysis, thereby improving fund control and advocating for standardized research cost management. Such measures are crucial in alleviating the burden on institutions and researchers, fostering a more effective and efficient scientific research environment. In this study, an analysis of the factors affecting scientific research performance yielded three key elements: external environment, individual researchers, and information platform. After applying the nonlinear mapping ability and adaptability of back -propagation artificial neural network (BP -ANN) reverse neural network and obtaining simulation results for the generalization function of discrete information, we established a model for research performance evaluation. Subsequently, research cases were selected to conduct training and fitting experiments, ultimately scoring research performance through a comprehensive evaluation process. The experiment showed that the prediction results obtained using the BPANN algorithm, following learning from preprocessed samples, exhibit high accuracy. Moreover, these results can be updated and adapted with new sample inputs, highlighting the strong feasibility of this method.

关键词： back-propagation artificial neural network colleges and universities scientific research performance evaluation management

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Application of Northern Goshawk back-propagation artificial neural network in the Prediction of Monohydroxycarbazepine Concentration in Patients with Epilepsy

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ADVANCES IN THERAPY 2024年第4期41卷 1450-1461页

作者： Xu, Yichao Shao, Rong Yang, Mingdong Chen, Meng Xu, Junjun Dai, Haibin Zhejiang Univ Sch Med Affiliated Hosp 2 Ctr Clin Pharmacol Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 2 Dept Pharm 88 Jiefang Rd Hangzhou 310009 Zhejiang Peoples R China

IntroductionA northern goshawk back-propagation artificial neural network (NGO-BPANN) model was established to predict monohydroxycarbazepine (MHD) concentration in patients with *** data were collected from 108 Han Chinese patients with epilepsy on oxcarbazepine monotherapy. The results of 14 genotype variates were selected as the input layer in the first BPANN model, and the variables that had a more significant impact on the plasma concentration of MHD were retained. With demographic characteristics and clinical laboratory test results, the genotypes of SCN1A rs2298771 and SCN2A rs17183814 were used to construct the BPANN model. The BPANN model was comprehensively validated and used to predict the MHD plasma concentration of five patients with epilepsy in our *** model demonstrated favorable fitness metrics, including a mean squared error of 0.00662, a gradient magnitude of 0.00753, an absence of validation tests amounting to zero, and a correlation coefficient of 0.980. Sex, BMI, and the genotype SCN1A rs2298771 were ranked highest by the absolute mean impact value (MIV), which is primarily associated with the concentration of MHD. The test group exhibited a range of - 20.84% to 31.03% bias between the predicted and measured values, with a correlation coefficient of 0.941 between the two. With BPANN, the MHD nadir concentration could be predicted *** NGO-BPANN model exhibits exceptional predictive capability and can be a practical instrument for forecasting MHD concentration in patients with *** Trial ***-OOC-17012141.

关键词： Plasma concentration Pharmacokinetic parameters Epilepsy Monohydroxycarbazepine back-propagation artificial neural network

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Creep Fracture Life Prediction of 9%Cr Heat-Resistant Steel Based on the back-propagation artificial neural network Considering Microstructure Characteristics

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STEEL RESEARCH INTERNATIONAL 2024年第0期

作者： Zhao, Liwei Shen, Junjie Li, Bo Xue, Ziyu Shao, Yuqing Ren, He Tianjin Univ Technol Sch Mech Engn Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin 300384 Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin 300384 Peoples R China Northeastern Univ Sch Mat Sci & Engn MOE Key Lab Anisotropy & Texture Mat Shenyang 110819 Peoples R China

This study proposes a creep-life prediction model based on a back-propagation artificial neural network (BP-ANN). The model predicted the minimum creep rate by optimizing microstructure characteristics, including martensite lath width, sub-grain, precipitates (M23C6 and Laves phases), and dislocation density. The BP-ANN-based predicted creep life of 9%Cr heat-resistant steel is compared with the traditional time-temperature life prediction method and the machine learning support vector regression model. The results show that the BP-ANN model has better accuracy and performance. The % error between the predicted and the experimental creep life is 5% to 10%, primarily distributed in the two-factor band line (2% to 5%). The BP-ANN model gives the lowest mean absolute error (0.9926), root mean square error (29.411), average absolute relative error (4.7%), and multiple coefficient of determination (R2 0.96693). The results show that the BP-ANN model improved the creep fracture life prediction of 9%Cr heat-resistant steel.

关键词： 9%Cr heat-resistant steel back-propagation artificial neural network life prediction microstructure characteristic

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A creep life prediction model of P91 steel coupled with back-propagation artificial neural network (BP-ANN) and 0 projection method

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INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING 2023年第1期206卷

作者： Wang, Lin Liu, Xinbao Fan, Ping Zhu, Lin Zhang, Kai Wang, Kai Song, Chaolu Ren, Siyu Northwest Univ Sch Chem Engn Xian 710069 Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

In order to predict the long-term creep life of P91 steel, this work proposed a coupled model, combining the error-trained back-propagation artificial neural network (BP-ANN) and an improved 0 model. Both the shortterm rupture life obtained by creep experiments and the related creep data of National Institute for Materials Science (NIMS) database were used to validate the above model. In this model, creep parameters (temperature, stress) were used as the input and relative error of 5 0 model extrapolations as the output. Then, the prediction errors of BP-ANN method were introduced to correct the 5 0 model extrapolation results to achieve a more accurate prediction. Consequently, the long-term creep life of P91 steel was predicted. It demonstrated that with the proposed model, the prediction capability for long-term creep life of P91 steel with 200,000 h is much better than those of other 0 methods, which has a prediction uncertainty less than 5%.

关键词： P91 steel Creep life prediction back-propagation artificial neural network 0 projection model

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Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach

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BMC ORAL HEALTH 2024年第1期24卷 1页

作者： Cai, Jingyi Min, Ziyang Deng, Yudi Jing, Dian Zhao, Zhihe Sichuan Univ West China Hosp Stomatol State Key Lab Oral Dis 14 3rd SectSouth Renmin Rd Chengdu 610041 Sichuan Peoples R China Sichuan Univ West China Hosp Stomatol Natl Clin Res Ctr Oral Dis 14 3rd SectSouth Renmin Rd Chengdu 610041 Sichuan Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples Hosp 9 Sch Med Dept Orthodont 639 Zhizaoju Rd Shanghai 200011 Peoples R China Shanghai Jiao Tong Univ Coll Stomatol 639 Zhizaoju Rd Shanghai 200011 Peoples R China

backgroundAdequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine *** and methodsCephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences *** regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and *** of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.

关键词： Orthodontics Occlusal plane Machine learning back-propagation artificial neural network Aesthetic improvement

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Prediction reliability improvement on long-term creep life for P91 steel using a hybrid method of artificial neural network and CDM model

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ENGINEERING FRACTURE MECHANICS 2025年 323卷

作者： Zhang, Kai Liu, Xinbao Zhu, Lin Northwest Univ Sch Chem Engn 229 North Taibai Rd Xian 710069 Peoples R China

In present study, a hybrid method had been proposed to improve the low prediction reliability of long-term creep life for P91 steel using a single conventional model. It combined the continuum damage mechanics (CDM) model with the error-trained back-propagation artificial neural network (BP-ANN) model. Firstly, both the conventional CDM model and the Larson-Miller (L-M) parameter method were employed to predict the long-term creep life. Subsequently, the shortterm rupture life data of P91 steel obtained with creep experiments and the related data from the National Institute for Materials Science (NIMS) database and other researchers were utilized to train the hybrid method, respectively. Meanwhile, the extrapolation optimization of the CDM model was carried out with relative errors from the error-trained BP-ANN model. Consequently, the long-term creep life of P91 steel was obtained using three different models. The results demonstrated that, in contrast to the L-M parameter method and single CDM model, the present hybrid method with training data lower than 10,000 h exhibits enhanced prediction reliability of the creep life of P91 steel up to 200,000 h, with a relative error of less than 8 %.

关键词： P91 steel Larson-Miller parameter method CDM model back-propagation artificial neural network Creep life prediction

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artificial neural network Model for Predicting Mechanical Strengths of Economical Ultra-High-Performance Concrete Containing Coarse Aggregates: Development and Parametric Analysis

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MATERIALS 2024年第16期17卷 3908页

作者： Li, Ling Gao, Yufei Dong, Xuan Han, Yongping Northeast Forestry Univ Sch Civil Engn & Transportat 26 Hexing Rd Harbin 150040 Peoples R China Beijing Union Univ Biochem Engn Coll Fatou Xili Dist 3 Beijing 100023 Peoples R China

Ultra-high-performance concrete with coarse aggregates (UHPC-CA) has the advantages of high strength, strong shrinkage resistance and a lower production cost, presenting a broad application prospect in civil engineering construction. In view of the difficulty in establishing a mathematical model to accurately predict the mechanical properties of UHPC-CA, the back-propagation artificial neural network (BP-ANN) method is used to fully consider the various influential factors of the compressive strength (CS) and flexural strength (FS) of UHPC-CA in this paper. By taking the content of cement (C), silica fume (SF), slag, fly ash (FA), coarse aggregate (CA), steel fiber, the water-binder ratio (w/b), the sand rate (SR), the cement type (CT), and the curing method (CM) as input variables, and the CS and FS of UHPC-CA as output objectives, the BP-ANN model with three layers has been well-trained, validated and tested with 220 experimental data in the studies published in the literature. Four evaluating indicators including the determination coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the integral absolute error (IAE) were used to evaluate the prediction accuracy of the BP-ANN model. A parametric study for the various influential factors on the CS and FS of UHPC-CA was conducted using the BP-ANN model and the corresponding influential mechanisms were analyzed. Finally, the inclusion levels for the CA, steel fiber, and the dimensionless parameters of the W/B and sand rate were recommended to obtain the optimal strength of UHPC-CA.

关键词： back-propagation artificial neural network UHPC-CA compressive and flexural strength prediction parametric study

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A simulation study on NOx reduction efficiency in SCR catalysts utilizing a modern C3-CNN algorithm

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FUEL 2024年 363卷

作者： Han, Peilun Shen, Xiaoqian Shen, Boxiong Hebei Univ Technol Sch Energy & Environm Engn Tianjin 300130 Peoples R China King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia

The simulation of De-NOx system by selective catalytic reduction (SCR) catalyst is very important in industrial application, however, the simulation is always highly time-consuming. In this work feed-forward back-propagation artificial neural network (BPNN) and Cross-Channel Communication Convolutional neural network (C3CNN) algorithm are first proposed as a tool for numerical simulation on De-NOx system by selective catalytic reduction (SCR) catalyst. Initially, one-dimensional Computational Fluid Dynamics (CFD) model allowed the analysis of the contribution of several parameters in SCR reaction (gas velocity, ammonia-to-nitrogen ratio, temperature, and channel length) to DeNOx efficiency. Then, 3600 derived data samples are trained by BPNN neural network which shows a high predictivity (R2 = 0.95542). Additionally, the influence on simulation results of algorithm parameters is analyzed. Furthermore, the introduced Cross-Channel Communication Convolutional neural network (C3-CNN) algorithm enhanced the accuracy, efficiency and reduced training time for the DeNOx system simulation.

关键词： SCR catalyst CFD simulation Machine learning Cross-Channel Communication Convolutional neural network back-propagation artificial neural network (BPNN)

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Elevated liver enzymes in the first trimester are associated with gestational diabetes mellitus: A prospective cohort study

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DIABETES-METABOLISM RESEARCH AND REVIEWS 2024年第4期40卷 e3799-e3799页

作者： Cui, Lingling Yang, Xiaoli Li, Zhiqian Gao, Yuting Zhang, Zhengya Xu, Dongmei Liu, Xinxin Zhengzhou Univ Coll Publ Hlth 100 Sci Ave Zhengzhou 450001 Ha Peoples R China Zhengzhou Univ Affiliated Hosp 3 Dept Obstet & Gynecol Zhengzhou Peoples R China

Aims: Previous studies have found that a single liver enzyme may predict gestational diabetes mellitus (GDM), but the results have been inconsistent. This study aimed to explore the associations of liver enzymes in early pregnancy with risk of GDM, as well as to independently rank risk factors. Methods: This prospective cohort study included 1295 women who underwent liver enzyme measurements during early pregnancy and completed GDM assessment in mid-pregnancy. Logistic regression and restricted cubic spline analyses were conducted to assess the relationship between liver enzymes and risk of GDM. back-propagation artificial neural network was performed to rank independently risk factors of GDM. Results: Women diagnosed with GDM exhibited significantly higher levels of liver enzymes than those without GDM (all p < 0.05). The highest quartile of liver enzymes was associated with higher risk of GDM compared with the lowest quartile, with adjusted odds ratio (ORs) ranging from 2.76 to 8.11 (all p < 0.05). Moreover, the ORs of GDM increased linearly with liver enzymes level (all P for overall association <0.001). Furthermore, back-propagation artificial neural network identified gamma-gamma-glutamyl transferase (GGT) as accounting for the highest proportion in the ranking of GDM risk prediction weights (up to 20.8%). Conclusions: Single or total elevations of liver enzymes in early pregnancy could predict the GDM occurrence, in which GGT, alkaline Phosphatase, and aspartate aminotransferase were the three most important independent risk factors.

关键词： back-propagation artificial neural network gestational diabetes mellitus liver enzymes risk factor

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Arrhenius constitutive equation and artificial neural network model of flow stress in hot deformation of offshore steel with high strength and toughness

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MATERIALS TECHNOLOGY 2023年第1期38卷

作者： Li Fangpo Li Ning Ren Xiaojian Qiao Song Lu Caihong Wang Jianjun Xu Yang Wang Bin CNPC Tubular Goods Res Inst Xian Peoples R China Inst Hyg Ordance Ind Xian Peoples R China Northeastern Univ State Key Lab Rolling & Automat Shenyang Peoples R China Shandong Iron & Steel Grp Rizhao Co Ltd Rizhao Peoples R China CNPC Tubular Goods Res Inst Xian 710076 Peoples R China

In this study, the thermal deformation behaviour of a high strength offshore steel at different temperatures and rates was investigated through thermal compression *** Arrhenius constitutive model and a back propagation artificial neural network (BP-ANN) were established to address more complex deformation characteristics. The performances of both models were was evaluated using standard statistical parameters such as the correlation coefficient (R) and average absolute relative error (AARE). The results showed that both models can accurately predict the rheological stresses generated during *** BP-ANN outperforms the Arrhenius equation model with correlation coefficients of fit greater than 99.9% and less than 0.8% relative error. At a strain rate of 0.01 s(-1) and 10 s(-1), the accuracy of the ANN decreases slightly due to the fact that it exceeds the strain rate range of the training set, as compared to the Arrhenius constitutive equations as these are more accurately predicted.

关键词： High strength offshore steel compression experiment Arrhenius constitutive equation back-propagation artificial neural network flow stress

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