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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

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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

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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

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

Frontiers of Engineering Management 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

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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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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

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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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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

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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

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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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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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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

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Title Author Date Type Operation

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

LI Lin, HAO Jiming, HU Jingnan

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

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

Journal Article

Research and establishment of enterprise quality metadata standard

SONG Han, LI Jie, ZHANG Genbao

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

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

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

Journal Article

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

Pijush SAMUI

Journal Article

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

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

A review of hydrological/water-quality models

Liangliang GAO,Daoliang LI

Journal Article

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