Samples of preparations contaminated by diethylene glycol(DEG),diethylene glycol raw materials and laboratory prepared solutions were measured to get NIR spectra.Then the iden-tification models were developed using th...Samples of preparations contaminated by diethylene glycol(DEG),diethylene glycol raw materials and laboratory prepared solutions were measured to get NIR spectra.Then the iden-tification models were developed using the collected spectra and the spectra of distilled water,propylene glyool and the preparations without diethylene glyool.Besides,the quantification model was also established for determining the concentration of diethylene glyool in the pre-parations.V alidation results show the identification and quantification models have ideal pre-diction performance.The emergency NIR models are rapid,easy to use and accurate,and can be implemented for identifying diethylene glycol raw material,screening the preparations contam-inated by diethylene glycol in the markets and analyzing the concentrations of DEG.展开更多
Magnetic topological materials have attracted much attention due to the correlation between topology and magnetism.Recent studies suggest that EuCd_(2)As_(2) is an antiferromagnetic topological material.Here by carryi...Magnetic topological materials have attracted much attention due to the correlation between topology and magnetism.Recent studies suggest that EuCd_(2)As_(2) is an antiferromagnetic topological material.Here by carrying out thorough magnetic,electrical and thermodynamic property measurements,we discover a long-time relaxation of the magnetic susceptibility in EuCd_(2)As_(2).The(001)in-plane magnetic susceptibility at 5 K is found to continuously increase up to∼10%over the time of∼14 hours.The magnetic relaxation is anisotropic and strongly depends on the temperature and the applied magnetic field.These results will stimulate further theoretical and experimental studies to understand the origin of the relaxation process and its effect on the electronic structure and physical properties of the magnetic topological materials.展开更多
Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmiu...Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmium content have caused several significant public health contamination incidents.It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards.This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method.Firstly,a long and short-term memory network is used to build the depth prediction model by using the regularization method,and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise.Finally,the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance.Then,early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture,including data resource,business logic,and application service layers.The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.展开更多
文摘Samples of preparations contaminated by diethylene glycol(DEG),diethylene glycol raw materials and laboratory prepared solutions were measured to get NIR spectra.Then the iden-tification models were developed using the collected spectra and the spectra of distilled water,propylene glyool and the preparations without diethylene glyool.Besides,the quantification model was also established for determining the concentration of diethylene glyool in the pre-parations.V alidation results show the identification and quantification models have ideal pre-diction performance.The emergency NIR models are rapid,easy to use and accurate,and can be implemented for identifying diethylene glycol raw material,screening the preparations contam-inated by diethylene glycol in the markets and analyzing the concentrations of DEG.
基金Supported by the National Key Research and Development Program of China(Grant Nos.2016YFA0300600 and 2018YFA0305600)the National Natural Science Foundation of China(Grant No.11974404)+1 种基金the Strategic Priority Research Program(B)of the Chinese Academy of Sciences(Grant No.XDB33000000)the Youth Innovation Promotion Association of CAS(Grant No.2017013).
文摘Magnetic topological materials have attracted much attention due to the correlation between topology and magnetism.Recent studies suggest that EuCd_(2)As_(2) is an antiferromagnetic topological material.Here by carrying out thorough magnetic,electrical and thermodynamic property measurements,we discover a long-time relaxation of the magnetic susceptibility in EuCd_(2)As_(2).The(001)in-plane magnetic susceptibility at 5 K is found to continuously increase up to∼10%over the time of∼14 hours.The magnetic relaxation is anisotropic and strongly depends on the temperature and the applied magnetic field.These results will stimulate further theoretical and experimental studies to understand the origin of the relaxation process and its effect on the electronic structure and physical properties of the magnetic topological materials.
基金supported in part by the National Key Research and Development Program of China(2017YFC1600605,2020YFC1606801).
文摘Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmium content have caused several significant public health contamination incidents.It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards.This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method.Firstly,a long and short-term memory network is used to build the depth prediction model by using the regularization method,and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise.Finally,the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance.Then,early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture,including data resource,business logic,and application service layers.The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.
