To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)pre...To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.展开更多
[Objectives]The extraction conditions of formula oolong tea were investigated by an orthogonal experiment.[Methods]The technical conditions were optimized by the 4C method,and the application of formula oolong tea ext...[Objectives]The extraction conditions of formula oolong tea were investigated by an orthogonal experiment.[Methods]The technical conditions were optimized by the 4C method,and the application of formula oolong tea extract in cigarettes was studied.[Results]①In the experimental range,the best sensory evaluation effect of formula oolong tea extract was obtained with extraction conditions of 70%ethanol as extraction solvent,extraction time h,extraction temperature 25℃,and ultrasonic frequency 80 kHz,and follow-up low-temperature concentration,low-temperature sedimentation and low-temperature centrifugation.②The effects of different centrifugal speeds on the quality of formula oolong tea extract were explored.The formula oolong tea extract obtained under the conditions of 3000 r/min and centrifugal time of 10 min showed the best evaluation effect with soft and delicate smoke,rich smoke fragrance,good comfort and refreshing mouthfeel.③The effective aroma components in the formula oolong tea extract were qualitatively analyzed by GC-MS.[Conclusions]This study provides high-quality raw materials and a theoretical basis for the research of independent flavor blending in cigarette industry enterprises.展开更多
The 'Industry 4.0' era is characterised by a series of new digital technologies, which affect every link of the vertical value chain and are the important driving force of world change. Digitalisation creates ...The 'Industry 4.0' era is characterised by a series of new digital technologies, which affect every link of the vertical value chain and are the important driving force of world change. Digitalisation creates new jobs for people and creates more value for businesses.展开更多
This study was carried to assess the quality of liquid waste produced by the Nouakchott Friendship Hospital in Mauritania,the aim is to quantify different heavy metals obtained from discharge of the hospital waste six...This study was carried to assess the quality of liquid waste produced by the Nouakchott Friendship Hospital in Mauritania,the aim is to quantify different heavy metals obtained from discharge of the hospital waste six heavy metals(arsenic,lead,cobalt,chromium,cadmium and copper)were object of evaluation.Analysis was carried using inductively coupled plasma-optical emission spectrometry(ICP-OES)method and the standards used are those of the WHO.The average content of heavy metals in different samples is different:Arsenic(4.625μg/L),Lead(3.800μg/L),Cyanide(0.05μg/L),Chromium(0.013μg/L),Cadmium(<LD(0.000000012μg/L)and Copper(60μg/L).Results showed that the samples of liquid waste from the Nouakchott Friendship Hospital were very loaded with pollutants,this may constitute a threat to the environment and a potential health risk for human,since the continuous introduction of these heavy metals into aquatic medium could be harmful through bioaccumulation and biomagnifications.展开更多
文摘To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.
文摘[Objectives]The extraction conditions of formula oolong tea were investigated by an orthogonal experiment.[Methods]The technical conditions were optimized by the 4C method,and the application of formula oolong tea extract in cigarettes was studied.[Results]①In the experimental range,the best sensory evaluation effect of formula oolong tea extract was obtained with extraction conditions of 70%ethanol as extraction solvent,extraction time h,extraction temperature 25℃,and ultrasonic frequency 80 kHz,and follow-up low-temperature concentration,low-temperature sedimentation and low-temperature centrifugation.②The effects of different centrifugal speeds on the quality of formula oolong tea extract were explored.The formula oolong tea extract obtained under the conditions of 3000 r/min and centrifugal time of 10 min showed the best evaluation effect with soft and delicate smoke,rich smoke fragrance,good comfort and refreshing mouthfeel.③The effective aroma components in the formula oolong tea extract were qualitatively analyzed by GC-MS.[Conclusions]This study provides high-quality raw materials and a theoretical basis for the research of independent flavor blending in cigarette industry enterprises.
文摘The 'Industry 4.0' era is characterised by a series of new digital technologies, which affect every link of the vertical value chain and are the important driving force of world change. Digitalisation creates new jobs for people and creates more value for businesses.
文摘This study was carried to assess the quality of liquid waste produced by the Nouakchott Friendship Hospital in Mauritania,the aim is to quantify different heavy metals obtained from discharge of the hospital waste six heavy metals(arsenic,lead,cobalt,chromium,cadmium and copper)were object of evaluation.Analysis was carried using inductively coupled plasma-optical emission spectrometry(ICP-OES)method and the standards used are those of the WHO.The average content of heavy metals in different samples is different:Arsenic(4.625μg/L),Lead(3.800μg/L),Cyanide(0.05μg/L),Chromium(0.013μg/L),Cadmium(<LD(0.000000012μg/L)and Copper(60μg/L).Results showed that the samples of liquid waste from the Nouakchott Friendship Hospital were very loaded with pollutants,this may constitute a threat to the environment and a potential health risk for human,since the continuous introduction of these heavy metals into aquatic medium could be harmful through bioaccumulation and biomagnifications.
