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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4, Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallizationprediction models by machine learning algorithms.prediction models, which were constructed by random decision forests and artificial neural network withFurthermore, a comparison with traditional prediction methods including the modified solubility equationThe highest accuracy shown by the testing set proves that the ML models have the best solubility prediction

Keywords： solubility prediction machine learning artificial neural network random decision forests

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Predictive calculations of gas solubility and permeability in glassy polymeric membranes: An overview

Matteo Minelli, Maria Grazia De Angelis, Giulio C. Sarti

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3, Pages 405-413 doi: 10.1007/s11705-017-1615-5

Abstract: The solubility of pure and mixed gases, diffusivity and permeability of single gases in polymer glassesFor gas and vapor solubility, the comparison with direct experimental data for mixed gases points outalso the ability to account for the significant variations that solubility-selectivity experiences upon

Keywords： solubility permeability glassy polymers NELF model diffusion

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The solubility of cefquinome sulfate in pure and mixed solvents

Rongbao Qi,Jingkang Wang,Junxiao Ye,Hongxun Hao,Ying Bao

Frontiers of Chemical Science and Engineering 2016, Volume 10, Issue 2, Pages 245-254 doi: 10.1007/s11705-016-1569-z

Abstract: The pure-solvent solubility data was correlated to the modified Apelblat and Van’t Hoff equations whereas

Keywords： cefquinome sulfate solubility thermodynamic properties

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Solubility and diffusivity of CO

Guilan Chen, Xingfu Song, Shuying Sun, Yanxia Xu, Jianguo Yu

Frontiers of Chemical Science and Engineering 2016, Volume 10, Issue 4, Pages 480-489 doi: 10.1007/s11705-016-1603-1

Abstract: In this study, the solubility and diffusivity of CO in -butanol+ N235 system were measured and reportedThe absorption of CO in the system is a physical absorption behavior and the solubility of CO decreases

Keywords： carbon dioxide N235 solubility diffusivity coupled process

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Purification of artemisinin from quercetin by anti-solvent crystallization

Chandrakant R. MALWADE, Haiyan QU, Ben-Guang RONG, Lars P. CHRISTENSEN

Frontiers of Chemical Science and Engineering 2013, Volume 7, Issue 1, Pages 72-78 doi: 10.1007/s11705-013-1305-x

Abstract: The fundamental process data such as solubility of artemisinin in pure organic solvents and their binaryThe solubility of quercetin was measured only in pure organic solvents at room temperature.Two different concentrations of impurity, i.e., 10% and 50% of its solubility, in the respective organicHowever, the purity of artemisinin crystals were reduced when quercetin concentration was 50% of its solubility

Keywords： anti-solvent crystallization artemisinin quercetin solubility Artemisia annua

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination Machine learning Prediction Spatial distribution

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3, Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Keywords： XGBoost deep neural network healthcare risk prediction

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Position-varying surface roughness prediction method considering compensated acceleration in milling

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4, Pages 855-867 doi: 10.1007/s11465-021-0649-z

Abstract: Aiming at surface roughness prediction in the machining process, this paper proposes a position-varyingsurface roughness prediction method based on compensated acceleration by using regression analysis.i>R-square proving the effectiveness of the filtering features, is selected as the input of the predictionMoreover, the prediction curve matches and agrees well with the actual surface state, which verifies

Keywords： surface roughness prediction compensated acceleration milling thin-walled workpiece

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Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-023-0961-2

Abstract: Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

Keywords： pile responses excavation prediction deflection bending moments

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Frontiers in Energy 2016, Volume 10, Issue 4, Pages 479-488 doi: 10.1007/s11708-016-0425-7

Abstract: In this paper a novel method for reliability prediction and validation of nuclear power units in serviceThe accuracy of the reliability prediction can be evaluated according to the comparison between the predictedFurthermore, the reliability prediction method is validated using the nuclear power units in North American

Keywords： nuclear power units in service reliability reliability prediction equivalent availability factors

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Calculation on phase diagrams of polyetherimide/

Ming TAN,Gaohong HE,Yan DAI,Rujie WANG,Wenhua SHI

Frontiers of Chemical Science and Engineering 2014, Volume 8, Issue 3, Pages 312-319 doi: 10.1007/s11705-014-1434-x

Abstract: polyetherimide (PEI)/ , -dimethylacetamide (DMAc) with H O and BuOH as non-solvent were simulated using solubility

Keywords： phase diagram solubility parameter interaction parameter ultrafiltration Flory-Huggins theory

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 2, Pages 171-175 doi: 10.1007/s11465-009-0091-0

Abstract: Trend prediction technology is the key technology to achieve condition-based maintenance of mechanicalTo ensure the normal operation of units and save maintenance costs, trend prediction technology is studiedThe main methods of the technology are given, the trend prediction method based on neural network isThe industrial site verification shows that the proposed trend prediction technology can reflect the

Keywords： water injection units condition-based maintenance trend prediction

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Developing prediction models to support drivers in performing rectifications in advance can effectivelysubsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct predictionIn addition, the effects of the activation function and input time-step length on the prediction performance

Keywords： dynamic prediction moving trajectory pipe jacking GRU deep learning

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Prediction of the shear wave velocity

Amoroso SARA

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 1, Pages 83-92 doi: 10.1007/s11709-013-0234-6

Abstract: The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

Keywords： horizontal stress index shear wave velocity flat dilatometer test cone penetration test

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1, Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionto simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for predictionThe study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based

Keywords： earthquake cone penetration test liquefaction support vector machine (SVM) prediction