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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1688-y

Abstract:

● A novel VMD-IGOA-LSTM model has proposed for the prediction ofwater quality.

Keywords: Water quality prediction     Grasshopper optimization algorithm     Variational mode decomposition     Long short-term    

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1667-3

Abstract:

● Hybrid deep-learning model is proposed for water quality prediction

Keywords: Water quality prediction     Soft-sensor     Anaerobic process     Tree-structured Parzen Estimator    

Analysis and prediction of the influence of energy utilization on air quality in Beijing

LI Lin, HAO Jiming, HU Jingnan

Frontiers of Environmental Science & Engineering 2007, Volume 1, Issue 3,   Pages 339-344 doi: 10.1007/s11783-007-0058-5

Abstract: This work evaluates the influence of energy consumption on the future air quality in Beijing, using 2000The air quality model was adopted to simulate the temporal and spatial distribution of each pollutantemission, concentration distribution, and sectoral share responsibility rate were analyzed, and air qualityAccording to the current policy and development trend, air quality in the eight urban areas could become

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1698-9

Abstract:

● Data acquisition and pre-processing for wastewater treatment were summarized.

Keywords: Chemical oxygen demand     Mining-beneficiation wastewater treatment     Particle swarm optimization     Support vector regression     Artificial neural network    

Digital-Twin-Enhanced Quality Prediction for the Composite Materials Article

Yucheng Wang, Fei Tao, Ying Zuo, Meng Zhang, Qinglin Qi

Engineering 2023, Volume 22, Issue 3,   Pages 23-33 doi: 10.1016/j.eng.2022.08.019

Abstract: Quality defects in composite materials can lead to lower quality components, creating potential riskExperimental and simulation methods are commonly used to predict the quality of composite materials.However, it is difficult to predict the quality of composite materials accurately due to the uncertainFeatures are added to the proposed model by generating simulated data to enhance the quality predictionAn extreme learning machine (ELM) for quality prediction is trained with the generated data.

Keywords: Digital twin     Quality prediction     Composites     Coupling models    

Artificial intelligence in radiotherapy: a technological review

Ke Sheng

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 431-449 doi: 10.1007/s11684-020-0761-1

Abstract: This review describes the RT workflow and identifies areas, including imaging, treatment planning, qualityassurance, and outcome prediction, that benefit from AI.

Keywords: artificial intelligence     radiation therapy     medical imaging     treatment planning     quality assurance     outcomeprediction    

From total quality management to Quality 4.0: A systematic literature review and future research agenda

Frontiers of Engineering Management 2023, Volume 10, Issue 2,   Pages 191-205 doi: 10.1007/s42524-022-0243-z

Abstract: Quality 4.0 is an emerging concept that has been increasingly appreciated because of the intensificationIt deals with aligning quality management practices with the emergent capabilities of Industry 4.0 toimprove cost, time, and efficiency and increase product quality.concept, Quality 4.0 implementation, quality management in Quality 4.0, and Quality 4.0 model and applicationthe quality curve theory, and highlight future research opportunities.

Keywords: quality management     Quality 4.0     Industry 4.0     literature review     predictive quality    

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    

Research and establishment of enterprise quality metadata standard

SONG Han, LI Jie, ZHANG Genbao

Frontiers of Mechanical Engineering 2008, Volume 3, Issue 1,   Pages 106-110 doi: 10.1007/s11465-008-0019-0

Abstract: Enabling quality managers to utilize and manage quality data efficiently under modern quality managementcircumstances is a primary issue for improving enterprise quality management.A concept of quality metadata is proposed in this paper, which can help quality managers gain a deeperunderstanding of various features of quality data and establish a more stable foundation for furtherThe procedure of establishing quality metadata standards is emphasized in the paper, and the content

Keywords: enterprise quality     foundation     description     quality management     primary    

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    

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    

Foxtail millet: nutritional and eating quality, and prospects for genetic improvement

Lu HE,Bin ZHANG,Xingchun WANG,Hongying LI,Yuanhuai HAN

Frontiers of Agricultural Science and Engineering 2015, Volume 2, Issue 2,   Pages 124-133 doi: 10.15302/J-FASE-2015054

Abstract: more important than ever to develop breeding strategies that facilitate the increasing demand for high qualityHere we review research on foxtail millet quality evaluation, appearance, cooking and eating qualityimportant crop, outline current status of breeding of foxtail millet, and make suggestions to improve grain quality

Keywords: foxtail millet     grain quality     quality evaluation     breeding for quality    

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    

A review of hydrological/water-quality models

Liangliang GAO,Daoliang LI

Frontiers of Agricultural Science and Engineering 2014, Volume 1, Issue 4,   Pages 267-276 doi: 10.15302/J-FASE-2014041

Abstract: Water quality models are important in predicting the changes in surface water quality for environmentalA range of water quality models are wildly used, but every model has its advantages and limitations forThe aim of this review is to provide a guide to researcher for selecting a suitable water quality modelEight well known water quality models were selected for this review: SWAT, WASP, QUALs, MIKE 11, HSPF

Keywords: water quality models     applications     future trends    

Title Author Date Type Operation

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Journal Article

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Journal Article

Analysis and prediction of the influence of energy utilization on air quality in Beijing

LI Lin, HAO Jiming, HU Jingnan

Journal Article

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

Journal Article

Digital-Twin-Enhanced Quality Prediction for the Composite Materials

Yucheng Wang, Fei Tao, Ying Zuo, Meng Zhang, Qinglin Qi

Journal Article

Artificial intelligence in radiotherapy: a technological review

Ke Sheng

Journal Article

From total quality management to Quality 4.0: A systematic literature review and future research agenda

Journal Article

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

Journal Article

Research and establishment of enterprise quality metadata standard

SONG Han, LI Jie, ZHANG Genbao

Journal Article

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

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

Foxtail millet: nutritional and eating quality, and prospects for genetic improvement

Lu HE,Bin ZHANG,Xingchun WANG,Hongying LI,Yuanhuai HAN

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

A review of hydrological/water-quality models

Liangliang GAO,Daoliang LI

Journal Article