Existing experimental results have shown that four types of physical mechanisms, namely, martensite transformation, martensite reorientation, magnetic domain wall motion and magnetization vector rotation, can be activ...Existing experimental results have shown that four types of physical mechanisms, namely, martensite transformation, martensite reorientation, magnetic domain wall motion and magnetization vector rotation, can be activated during the magneto-mechanical deformation of NiMnGa ferromagnetic shape memory alloy (FSMA) single crystals. In this work, based on irreversible thermodynamics, a three-dimensional (3D) single crystal constitutive model is constructed by considering the aforementioned four mechanisms simultaneously. Three types of internal variables, i.e., the volume fraction of each martensite variant, the volume fraction of magnetic domain in each variant and the deviation angle between the magnetization vector, and easy axis are introduced to characterize the magneto-mechanical state of the single crystals. The thermodynamic driving force of each mechanism and the thermodynamic constraints on the constitutive model are obtained from Clausius's dissipative inequality and constructed Gibbs free energy. Then, thermodynamically consistent kinetic equations for the four mechanisms are proposed, respectively. Finally, the ability of the proposed model to describe the magneto-mechanical deformation of NiMnGa FSMA single crystals is verified by comparing the predictions with corresponding experimental results. It is shown that the proposed model can quantitatively capture the main experimental phenomena. Further, the proposed model is used to predict the deformations of the single crystals under the non-proportional mechanical loading conditions.展开更多
Cloud electrification is one of the oldest unresolved puzzles in the atmospheric sciences. Though many mechanisms for charge separation in clouds have been proposed, a quantitative understanding of their respective co...Cloud electrification is one of the oldest unresolved puzzles in the atmospheric sciences. Though many mechanisms for charge separation in clouds have been proposed, a quantitative understanding of their respective contribution in a given meteorological situation is lacking. Here we suggest and analyze a hitherto little discussed process. A qualitative picture at the molecular level of the charge separation mechanism of lightning in a thundercloud is proposed. It is based on two key physical/chemical natural phenomena, namely, internal charge separation of the atmospheric impurities/aerosols inside an atmospheric water cluster/droplet/ice particle and the existence of liquid water layers on rimers (graupels and hailstones) forming a layer of dipoles with H<sup>+</sup> pointing out from the air-water interface. Charge separation is achieved through strong collisions among ice particles and water droplets with the rimers in the turbulence of the thundercloud. This work would have significant contribution to cloud electrification and lightning formation.展开更多
Structural fatigue of NiTi shape memory alloys is a key issue that should be solved in order to promote their engineering applications and utilize their unique shape memory effect and super-elasticity more sufficientl...Structural fatigue of NiTi shape memory alloys is a key issue that should be solved in order to promote their engineering applications and utilize their unique shape memory effect and super-elasticity more sufficiently. In this paper, the latest progresses made in experimental and theoretical analyses for the structural fatigue features of NiTi shape memory alloys are reviewed. First, macroscopic experimental observations to the pure mechanical and thermo-mechanical fatigue features of the alloys are summarized; then the state-of-arts in the mechanism analysis of fatigue rupture are addressed; further, advances in the construction of fatigue failure models are provided; finally, summary and future topics are outlined.展开更多
Background and Aims:Metabolic associated fatty liver disease(MAFLD)is a serious condition,and a simple meth-od is needed for practitioners to identify patients with the disease and have a high risk of disease progress...Background and Aims:Metabolic associated fatty liver disease(MAFLD)is a serious condition,and a simple meth-od is needed for practitioners to identify patients with the disease and have a high risk of disease progression.Meth-ods:We developed and validated a nomogram for fatty liver disease and reclassified the risk factors for MAFLD.The development cohort had 335 patients who received bioel-ectrical impedance analysis and liver ultrasound attenua-tion measurements at Shenzhen People’s Hospital between September 2020 and June 2021.The validation cohort had 200 patients from other hospitals who received the same evaluation.A random forest procedure and binary logistic analysis were used to screen for risk factors,establish a fatty liver disease predictive model,and forecast the risk of MAFLD.The performance of the nomogram was evaluated by measurement of discrimination,calibration,and clinical usefulness.Results:The nomogram provided good predic-tions in a model that included body mass index(BMI)and waist circumference.The areas under the curve of the nom-ogram were 0.793 in the development cohort and 0.774 in the validation cohort.The nomogram performed well for calibration,category-free net reclassification improvement,and integrated discrimination improvement.Decision curve analysis indicated the nomogram performed better than BMI for predicting net outcome.Conclusions:The nomo-gram was an effective screening tool for fatty liver disease,and for those overweight individuals,may help physicians make appropriate decisions regarding treatment of MAFLD.展开更多
This paper investigates the combination of laser-induced breakdown spectroscopy〔LIBS〕and deep convolutional neural networks〔CNNs〕to classify copper concentrate samples using pretrained CNN models through transfer ...This paper investigates the combination of laser-induced breakdown spectroscopy〔LIBS〕and deep convolutional neural networks〔CNNs〕to classify copper concentrate samples using pretrained CNN models through transfer learning.Four pretrained CNN models were compared.The LIBS profiles were augmented into 2D matrices.Three transfer learning methods were tried.All the models got a high classification accuracy of>92%,with the highest at 96.2%for VGG16.These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting.The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.展开更多
基金the National Natural Science Foundation of China (Grant 11602203)Young Elite Scientist Sponsorship Program by the China Association for Science and Technology (Grant 2016QNRC001)Fundamental Research Funds for the Central Universities (Grant 2682018CX43).
文摘Existing experimental results have shown that four types of physical mechanisms, namely, martensite transformation, martensite reorientation, magnetic domain wall motion and magnetization vector rotation, can be activated during the magneto-mechanical deformation of NiMnGa ferromagnetic shape memory alloy (FSMA) single crystals. In this work, based on irreversible thermodynamics, a three-dimensional (3D) single crystal constitutive model is constructed by considering the aforementioned four mechanisms simultaneously. Three types of internal variables, i.e., the volume fraction of each martensite variant, the volume fraction of magnetic domain in each variant and the deviation angle between the magnetization vector, and easy axis are introduced to characterize the magneto-mechanical state of the single crystals. The thermodynamic driving force of each mechanism and the thermodynamic constraints on the constitutive model are obtained from Clausius's dissipative inequality and constructed Gibbs free energy. Then, thermodynamically consistent kinetic equations for the four mechanisms are proposed, respectively. Finally, the ability of the proposed model to describe the magneto-mechanical deformation of NiMnGa FSMA single crystals is verified by comparing the predictions with corresponding experimental results. It is shown that the proposed model can quantitatively capture the main experimental phenomena. Further, the proposed model is used to predict the deformations of the single crystals under the non-proportional mechanical loading conditions.
文摘Cloud electrification is one of the oldest unresolved puzzles in the atmospheric sciences. Though many mechanisms for charge separation in clouds have been proposed, a quantitative understanding of their respective contribution in a given meteorological situation is lacking. Here we suggest and analyze a hitherto little discussed process. A qualitative picture at the molecular level of the charge separation mechanism of lightning in a thundercloud is proposed. It is based on two key physical/chemical natural phenomena, namely, internal charge separation of the atmospheric impurities/aerosols inside an atmospheric water cluster/droplet/ice particle and the existence of liquid water layers on rimers (graupels and hailstones) forming a layer of dipoles with H<sup>+</sup> pointing out from the air-water interface. Charge separation is achieved through strong collisions among ice particles and water droplets with the rimers in the turbulence of the thundercloud. This work would have significant contribution to cloud electrification and lightning formation.
基金supported by the National Natural Science Foundation of China(No.22133001,No.11774233,No.21773252,No.21773257,No.21827803)the Project for high-grade,precision and advance in Beijing(BUPT)。
基金supported by the National Natural Science Foundation of China (11532010)
文摘Structural fatigue of NiTi shape memory alloys is a key issue that should be solved in order to promote their engineering applications and utilize their unique shape memory effect and super-elasticity more sufficiently. In this paper, the latest progresses made in experimental and theoretical analyses for the structural fatigue features of NiTi shape memory alloys are reviewed. First, macroscopic experimental observations to the pure mechanical and thermo-mechanical fatigue features of the alloys are summarized; then the state-of-arts in the mechanism analysis of fatigue rupture are addressed; further, advances in the construction of fatigue failure models are provided; finally, summary and future topics are outlined.
基金Commission of Science and Technology of Shenzhen(GJHZ20200731095401004).
文摘Background and Aims:Metabolic associated fatty liver disease(MAFLD)is a serious condition,and a simple meth-od is needed for practitioners to identify patients with the disease and have a high risk of disease progression.Meth-ods:We developed and validated a nomogram for fatty liver disease and reclassified the risk factors for MAFLD.The development cohort had 335 patients who received bioel-ectrical impedance analysis and liver ultrasound attenua-tion measurements at Shenzhen People’s Hospital between September 2020 and June 2021.The validation cohort had 200 patients from other hospitals who received the same evaluation.A random forest procedure and binary logistic analysis were used to screen for risk factors,establish a fatty liver disease predictive model,and forecast the risk of MAFLD.The performance of the nomogram was evaluated by measurement of discrimination,calibration,and clinical usefulness.Results:The nomogram provided good predic-tions in a model that included body mass index(BMI)and waist circumference.The areas under the curve of the nom-ogram were 0.793 in the development cohort and 0.774 in the validation cohort.The nomogram performed well for calibration,category-free net reclassification improvement,and integrated discrimination improvement.Decision curve analysis indicated the nomogram performed better than BMI for predicting net outcome.Conclusions:The nomo-gram was an effective screening tool for fatty liver disease,and for those overweight individuals,may help physicians make appropriate decisions regarding treatment of MAFLD.
基金supported by the Open Foundation of Key Laboratory of Laser Device Technology,China North Industries Group Corporation Limited(No.KLLDT202109)the National Natural Science Foundation of China(No.62175150)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(No.SL2021ZD103)。
文摘This paper investigates the combination of laser-induced breakdown spectroscopy〔LIBS〕and deep convolutional neural networks〔CNNs〕to classify copper concentrate samples using pretrained CNN models through transfer learning.Four pretrained CNN models were compared.The LIBS profiles were augmented into 2D matrices.Three transfer learning methods were tried.All the models got a high classification accuracy of>92%,with the highest at 96.2%for VGG16.These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting.The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.
