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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    

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    

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

Chen Zijin,Wang Fuliang,Lu Shouxiang

Strategic Study of CAE 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    

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    

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    

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    

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    

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    

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    

Risk Matrix Method and Its Application in the Field of Technical Project Risk Management

Zhu Qichao,Kuang Xinghua,Shen Yongping

Strategic Study of CAE 2003, Volume 5, Issue 1,   Pages 89-94

Abstract:

Technical project risk management has always been given great concern by the Department of DefenseThis paper systematically introduces risk matrix and its application, which is one of the most popularrisk management technologies in the field of DoD acquisition projects risk management.As a conclusion, this paper evaluates the usability of risk matrix when to be used to assess and mitigaterisk management.

Keywords: risk matrix     risk management     project management    

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    

Bioaerosol emissions variations in large-scale landfill region and their health risk impacts

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 12, doi: 10.1007/s11783-022-1593-9

Abstract:

● The airborne bacteria in landfills were 4–50 times higher than fungi.

Keywords: Microbial aerosols     Landfill sites     Health risk assessment     CALPUFF    

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    

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    

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    

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

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

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

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

Journal Article

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

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

Risk Matrix Method and Its Application in the Field of Technical Project Risk Management

Zhu Qichao,Kuang Xinghua,Shen Yongping

Journal Article

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Journal Article

Bioaerosol emissions variations in large-scale landfill region and their health risk impacts

Journal Article

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

Journal Article

Prediction of the shear wave velocity

Amoroso SARA

Journal Article

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

Pijush SAMUI

Journal Article