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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 tohyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk beginsIn addition, the fracture risk of patients with diabetes and osteoporosis has not been further studiedthis paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture riskdiabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk

Keywords： XGBoost deep neural network healthcare risk prediction

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SinoSCORE: a logistically derived additive prediction model for post-coronary artery bypass grafting

Zhe Zheng, Lu Zhang, Xi Li, Shengshou Hu, on behalf of the Chinese CABG Registry Study

Frontiers of Medicine 2013, Volume 7, Issue 4, Pages 477-485 doi: 10.1007/s11684-013-0284-0

Abstract: study aims to construct a logistically derived additive score for predicting in-hospital mortality riskThresholds were defined for each model to distinguish different risk groups.

Keywords： coronary artery bypass grafting risk stratification in-hospital mortality

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Analysis of GM（1，1）Model and Its Application in Fire Risk Prediction

Chen Zijin,Wang Fuliang,Lu Shouxiang

Strategic Study of Chinese Academy of Engineering 2007, Volume 9, Issue 5, Pages 91-94

Abstract:

Theoretical analysis of grey prediction model GM(1, 1) is present in Example applications of the criterion in fire risk grey prediction are discussed.

Keywords： fire forecast GM(1 1) rate of the fire injured

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Understanding and addressing the environmental risk of microplastics

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 1, doi: 10.1007/s11783-023-1612-5

Abstract: Hence, great uncertainties might exist in microplastics exposure and health risk assessment based on

Keywords： Emerging contaminants Microplastics Environment risk Health effect

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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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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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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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Ecological Risk Management of Drinking Water Project: The Case Study of Kunming City

Ji-liang Zheng,Jun Hu,Xuan Zhou,Ching Yuen Luk

Frontiers of Engineering Management 2015, Volume 2, Issue 3, Pages 311-319 doi: 10.15302/J-FEM-2015045

Abstract: The ecological risk management of drinking water project is an important means of ensuring the safetyBased on ecological risk assessment and management theories, this paper establishes an ecological riskIts ecological risk management of drinking water has attracted the attention of both the local government

Keywords： drinking water project ecological risk ecological risk assessment risk management

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An energy consumption prediction approach of die casting machines driven by product parameters

Frontiers of Mechanical Engineering Pages 868-886 doi: 10.1007/s11465-021-0656-0

Abstract: The energy consumption prediction of die casting machines can support energy consumption quota, processTo fill this gap, this paper proposes an energy consumption prediction approach for die casting machinesFirstly, the system boundary of energy consumption prediction is defined, and subsequently, based onConsequently, a systematic energy consumption prediction approach for die casting machines, involvingThe results show that the prediction accuracy of production time and energy consumption reached 91.64%

Keywords： die casting machine energy consumption prediction product parameters

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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 coneThe SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRMUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionThe 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

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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 crystallizationHerein we used seven descriptors based on understanding dissolution behavior to establish two solubility predictionThe solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the predictionFurthermore, 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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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Frontiers of Structural and Civil Engineering Pages 976-989 doi: 10.1007/s11709-022-0840-2

Abstract: Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

Keywords： damage prediction ANN BOA FEM experimental modal analysis

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Epidemic obesity in children and adolescents: risk factors and prevention

Eun Young Lee, Kun-Ho Yoon

Frontiers of Medicine 2018, Volume 12, Issue 6, Pages 658-666 doi: 10.1007/s11684-018-0640-1

Abstract: The complexity of risk factors for developing obesity among children and adolescents leads to difficultythat of obesity, an effective prevention strategy is to focus on overweight youth, who are at high riskMultifaceted, comprehensive strategies involving behavioral, psychological, and environmental risk factors

Keywords： obesity children adolescents epidemiology risk factor prevention

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Optimal risk allocation in alliance infrastructure projects: A social preference perspective

Xiang DING, Qian LI

Frontiers of Engineering Management 2022, Volume 9, Issue 2, Pages 326-336 doi: 10.1007/s42524-020-0145-x

Abstract: The mechanism of risk allocation is designed to protect all stakeholders, and it is vital to projectFew research has focused on partners’ social preferences affecting the output of risk allocation.risk-management effort simultaneously.Results show that an AM’s IA significantly affects risk allocation between AL and AM.preference negatively affects AL’s optimal risk-sharing ratio.

Keywords： public project contract design risk sharing inequity aversion governance

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The prediction of adsorption isotherms of ester vapors on hypercrosslinked polymeric adsorbent

Liuyan WU,Lijuan JIA,Xiaohan LIU,Chao LONG

Frontiers of Environmental Science & Engineering 2016, Volume 10, Issue 3, Pages 482-490 doi: 10.1007/s11783-015-0826-6

Abstract: Adsorption isotherms of methyl acetate, ethyl acetate, propyl acetate, isopropyl acetate and ethyl propionate on hypercrosslinked polymeric resin (ND-100) were measured at 303K, 318K and 333K,respectively, and well fitted by Dubinin–Astakhov (DA) equation. The plots of the adsorbed volume ( ) versus the adsorption potential ( ) at three different temperatures all fell basically onto one single curve for every ester. A predicted model based on DA equation was obtained on the basis of adsorption equilibrium data of methyl acetate, ethyl acetate and ethyl propionate at 318K. The model equation successfully predicted the adsorption isotherms of methyl acetate, ethyl acetate and ethyl propionate on ND-100 at 303K, and 333K, and also gave accurate predictive results for adsorption isotherms of the other two ester compounds (propyl acetate and isopropyl acetate) on ND-100 at 303K, 318K and 333K. The results proved the effectiveness of DA model for predicting the adsorption isotherms of ester compounds onto ND-100. In addition, the relationship between physico-chemical properties of adsorbates and their adsorption properties was also investigated. The results showed that molecular weight, molar volume and molar polarizability had good linear correlations with the parameter (which represents adsorption characteristic energy) of DA equation.

Keywords： hypercrosslinked polymeric adsorbent adsorption isotherm ester prediction

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Title Author Date Type Operation

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

Journal Article

SinoSCORE: a logistically derived additive prediction model for post-coronary artery bypass grafting

Zhe Zheng, Lu Zhang, Xi Li, Shengshou Hu, on behalf of the Chinese CABG Registry Study

Journal Article

Analysis of GM（1，1）Model and Its Application in Fire Risk Prediction

Chen Zijin,Wang Fuliang,Lu Shouxiang

Journal Article

Understanding and addressing the environmental risk of microplastics

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

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

Journal Article

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Journal Article

Ecological Risk Management of Drinking Water Project: The Case Study of Kunming City

Ji-liang Zheng,Jun Hu,Xuan Zhou,Ching Yuen Luk

Journal Article

An energy consumption prediction approach of die casting machines driven by product parameters

Journal Article

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

Pijush SAMUI

Journal Article

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

Journal Article

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Journal Article

Epidemic obesity in children and adolescents: risk factors and prevention

Eun Young Lee, Kun-Ho Yoon

Journal Article

Optimal risk allocation in alliance infrastructure projects: A social preference perspective

Xiang DING, Qian LI

Journal Article

The prediction of adsorption isotherms of ester vapors on hypercrosslinked polymeric adsorbent

Liuyan WU,Lijuan JIA,Xiaohan LIU,Chao LONG

Journal Article

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