As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decompos...As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.展开更多
A short review is given of standard regression analysis. It is shown that the results presented by program packages are not always reliable. Here is presented a general framework for linear regression that includes mo...A short review is given of standard regression analysis. It is shown that the results presented by program packages are not always reliable. Here is presented a general framework for linear regression that includes most linear regression methods based on linear algebra. The H-principle of mathematical modelling is presented. It uses the analogy between the modelling task and measurement situation in quantum mechanics. The principle states that the modelling task should be carried out in steps where at each step an optimal balance should be determined between the value of the objective function, the fit, and the associated precision. H-methods are different methods to carry out the modelling task based on recommendations of the H-principle. They have been applied to different types of data. In general, they provide better predictions than linear regression methods in the literature.展开更多
Objective:To analyze syndrome differentiation and medication rules for leucorrhea in ancient books of traditional Chinese medicine(TCM)based on the“Chinese Medical Code”using the latent structure model.Methods:The f...Objective:To analyze syndrome differentiation and medication rules for leucorrhea in ancient books of traditional Chinese medicine(TCM)based on the“Chinese Medical Code”using the latent structure model.Methods:The formulations for leucorrhea were searched in the Chinese Medical Code(software),based on which an Excel sheet was created to conduct frequency analysis and construct a matrix of high-frequency medications.The high-frequency medications for leucorrhea were then analyzed using the Latent 5.0 software and a latent structure model.With SPSS Modeler and Latent 5.0 as the main analysis methods,Chinese medications with a frequency>1.00%were investigated using the latent structure model and association rules,and the medication rules in treating leucorrhea with TCM were dissected based on the data of frequency.Results:For 373 kinds of Chinese medicines retrieved from 236 TCM prescriptions,the total medication frequency was 2522.Chinese Angelica(Angelicae Sinensis Radix),White Peony Root(Paeoniae Radix Alba),Indian Buead Tuckahoe(Poria),Common Oyster Shell(Ostreae Concha),and Largehead Atractylodes Rhizome(Atractylodis Macrocephalae Rhizoma)were high-frequency medicines for leucorrhea.Tonic,heat-clearing,and qi-regulating medicines had a high frequency of usage.According to a formula-based analysis of the evidence,the main syndromes of leucorrhea included liver qi affecting the spleen pattern,damp heat accumulation pattern,liver qi stagnation pattern,liver-stomach disharmony pattern,yin deficiency of the liver and kidney pattern,and spleen deficiency with dampness pattern,which are generally in line with the etiology and pathogenesis of leucorrhea,namely pathogenic dampness as the detrimental factor together with spleen-kidney dysfunction.However,in ancient books,emphasis was laid out on the regulation of the liver.This study determined 31 association rules for treating leucorrhea,where the primary compatible medicines contained Chinese Angelica(Angelicae Sinensis Radix)-Nutgrass Galingale Rhizome(Cyperus rotundus),Chinese Angelica(Angelicae Sinensis Radix)-Szechwan Lovage Rhizome(Ligusticum chuanxiong Hort.),White Peony Root(Paeoniae Radix Alba)-Szechwan Lovage Rhizome(Ligusticum chuanxiong Hort.),White Peony Root(Paeoniae Radix Alba)-Adhesive Rehmannia Root Tuber(Rehmanniae Radix)and Tangerine Peel(Citri Reticulatae Pericarpium)-Ternate Pinellia(Rhizoma Pinelliae).Conclusion:The medication rules targeting leucorrhea in the Chinese Medical Code were mainly represented by the regulation of qi and blood,as well as the 3 internal organs-the kidney,spleen,and liver.展开更多
Objective:To analyze the identification and typing of bone flaccidity and the law of medication in ancient books based on the Encyclopaedia of TCM(Traditional Chinese Medicine)using the hidden structure model.Methods:...Objective:To analyze the identification and typing of bone flaccidity and the law of medication in ancient books based on the Encyclopaedia of TCM(Traditional Chinese Medicine)using the hidden structure model.Methods:The formulations for bone flaccidity were searched in the Chinese Medical Code(software),based on which an Excel sheet was created to conduct frequency analysis and construct a matrix of high-frequency medications.The high-frequency medications for bone flaccidity were then analyzed using the Latent 5.0 software and a latent structure model.With SPSS Modeler and Latent 5.0 as the main analysis methods,Chinese medications with a frequency>1.00%were investigated using the latent structure model and association rules,and the medication rules in treating bone flaccidity with TCM were dissected based on the data of frequency.Results:A total of 223 TCM prescriptions were included,with 270 extracted TCM and a total medication frequency of 2340 times.Among them are Achyranthis Bidentatae Radix,Poria,Angelicae Sinensis Radix,Eucommiae Cortex,Rehmanniae Radix Praeparata,and Ginseng Radix Et Rhizoma.They were the high-frequency medicines for treating bone flaccidity,and the efficacy of tonifying medicinal,heat-clearing medicinal,and blood activating and stasis-resolving medicinal were high-frequency medicinal categories.Through the analysis of the evidence by formula,it was concluded that spleen-kidney yang deficiency,kidney deficiency and blood stasis,kidney yang deficiency,qi deficiency/qi stagnation and blood stasis,and liver and kidney yin deficiency were the 6 main types of bone flaccidity,which was generally in line with the etiology of bone flaccidity,which was caused by kidney yang deficiency and accompanied by malfunction of the liver and kidneys.In ancient books,the regulation of the kidney is put into an important position.In this study,19 correlative principles for the treatment of bone flaccidity were derived,whose main combinations are Paeoniae Radix Alba-Angelicae Sinensis Radix,Eucommiae Cortex+Poria-Ginseng Radix Et Rhizoma,Dioscoreae Spongiosae Rhizoma+Cuscutae Semen-Eucommiae Cortex.Conclusions:The medication law of bone flaccidity in the Encyclopaedia of TCM mainly shows that it attaches importance to the regulation of tonifying the liver and kidney to strengthen the tendons and bones and emphasizes that the regulation of the 3 Zang–fu organs,namely the kidney,the liver and the spleen,can play an important role in the treatment of bone flaccidity.展开更多
Syndrome differentiation is the character of Chinese medicine （CM）. Disease differentiation is the principle of Western medicine （WM）. Identifying basic syndromes feature and structure of disease of WM is an impor...Syndrome differentiation is the character of Chinese medicine （CM）. Disease differentiation is the principle of Western medicine （WM）. Identifying basic syndromes feature and structure of disease of WM is an important avenue for prevention and treatment of integrated Chinese and Western medicine. The idea here is first to divide all patients suffering from a disease of WM into several groups in the light of the stage of the disease, and secondly to identify basic syndromes feature in a distinct stage, and finally to achieve the purpose of syndrome differentiation. Syndrome differentiation is simply taken as a classifier that classifies patients into distinct classes primarily based on overall observation of their symptoms. Previous clustering methods are unable to cope with the complexity of CM. We therefore show a new multi-dimensional clustering method in the form of general latent structure （GLS） model, which is a suitable statistical learning technique of latent class analysis. In this paper, we learn an optimal GLS model which reflects much better model quality compared with other latent class models from the osteoporosis patient of community women （OPCW） real data including 40 65 year old women whose bone mineral density （BMD） is less than mean2.0 standard deviation （M 2.0SD）. Further, we illustrate a case analysis of statistical identification of CM syndromes feature and structure of OPCW from qualitative and quantitative contents through the GLS model. Our analysis has discovered natural clusters and structures that correspond well to CM basic syndrome and factors of osteoporosis patients （OP）. The GLS model suggests the possibility of establishing objective and quantitative diagnosis standards for syndrome differentiation on OPCW. Hence, for the future it can provide a reference for the similar study from the perspective of a combination of disease differentiation and syndrome differentiation.展开更多
The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （200...The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.展开更多
Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group stru...Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.展开更多
The weather research and forecasting（WRF） model is a new generation mesoscale numerical model with a fine grid resolution（2 km）, making it ideal to simulate the macro-and micro-physical processes and latent heatin...The weather research and forecasting（WRF） model is a new generation mesoscale numerical model with a fine grid resolution（2 km）, making it ideal to simulate the macro-and micro-physical processes and latent heating within Typhoon Molave（2009）. Simulations based on a single-moment, six-class microphysical scheme are shown to be reasonable, following verification of results for the typhoon track, wind intensity, precipitation pattern, as well as inner-core thermodynamic and dynamic structures. After calculating latent heating rate, it is concluded that the total latent heat is mainly derived from condensation below the zero degree isotherm, and from deposition above this isotherm. It is revealed that cloud microphysical processes related to graupel are the most important contributors to the total latent heat. Other important latent heat contributors in the simulated Typhoon Molave are condensation of cloud water, deposition of cloud ice, deposition of snow, initiation of cloud ice crystals, deposition of graupel, accretion of cloud water by graupel, evaporation of cloud water and rainwater,sublimation of snow, sublimation of graupel, melting of graupel, and sublimation of cloud ice. In essence, the simulated latent heat profile is similar to ones recorded by the Tropical Rainfall Measuring Mission, although specific values differ slightly.展开更多
As far as Chinese art,YIN YANG viewpoint and TAI JI thought derived from ZHOU YI have been embedded deeply into Chinese artistic creating for thousand years,the typical examples are traditional Chinese painting and mo...As far as Chinese art,YIN YANG viewpoint and TAI JI thought derived from ZHOU YI have been embedded deeply into Chinese artistic creating for thousand years,the typical examples are traditional Chinese painting and mountains-and -waters painting.If people do not find the basis to explain the methodology of "Qi"and"Yun"from the origin,the know of traditional culture is all in vain."Qi"expresses an invisible space concept,it has raised to a cultural philosophical concept in Chinese eyes,the explanation of this problem will be of benefit to the search for the latent structure in Chinese national artistic form,and the modern signification of latent structure at present would be found.展开更多
Multi-label classification problems arise frequently in text categorization, and many other related applications. Like conventional categorization problems, multi-label categorization tasks suffer from the curse of hi...Multi-label classification problems arise frequently in text categorization, and many other related applications. Like conventional categorization problems, multi-label categorization tasks suffer from the curse of high dimensionality. Existing multi-label dimensionality reduction methods mainly suffer from two limitations. First, latent nonlinear structures are not utilized in the input space. Second, the label information is not fully exploited. This paper proposes a new method, multi-label local discriminative embedding (MLDE), which exploits latent structures to minimize intraclass distances and maximize interclass distances on the basis of label correlations. The latent structures are extracted by constructing two sets of adjacency graphs to make use of nonlinear information. Non-symmetric label correlations, which are the case in real applications, are adopted. The problem is formulated into a global objective function and a linear mapping is achieved to solve out-of-sample problems. Empirical studies across 11 Yahoo sub-tasks, Enron and Bibtex are conducted to validate the superiority of MLDE to state-of-art multi-label dimensionality reduction methods.展开更多
Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is pr...Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is proposed.Orthogonal projections to latent structures(O-PLS)is a general linear multi-variable data modeling method.It can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel technique”to calculate predictive components and response-orthogonal components in the model.Therefore,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain extent.To verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.展开更多
This work studied the effect of differential temperatures on the latent heat in the nucleation of CdSe quantum dots（QDs）.The result showed that,by the formula of phase change,with increasing the reaction temperature...This work studied the effect of differential temperatures on the latent heat in the nucleation of CdSe quantum dots（QDs）.The result showed that,by the formula of phase change,with increasing the reaction temperature,the latent heat in the nucleation of QDs reduced.CdSe QDs with the size-dispersion from 2.7 to 3.6 nm were synthesized via oleic acid-paraffin liquid system by controlling the reaction temperature from 180 to 220℃.Synthesized QDs were characterized by UV-vis absorption spectra and X-ray diffraction（XRD）.The result of UV-vis absorption spectra showed that with increasing of reaction temperature,the first absorption peak was red-shifted and the size of QD increased.The result of XRD showed that the synthesized QDs were zinc-blende structure.展开更多
Background Extreme rainfall and flooding events are projected to increase in frequency and disturb biogeochemical cycles such as the nitrogen(N)cycle.By combining trees and grasses,silvopastoral agroforestry is expect...Background Extreme rainfall and flooding events are projected to increase in frequency and disturb biogeochemical cycles such as the nitrogen(N)cycle.By combining trees and grasses,silvopastoral agroforestry is expected to increase the stability of this cycle in response to flooding.However,little is known about the response of nitrification to flooding in silvopastoral systems.Aim of this study was to assess nitrification stability in response to flooding and identify the main causal relations that drive it in temperate silvopastures.Methods The nitrification stability(i.e.,resistance and resilience)was assessed in two silvopastoral systems(i.e.,hedgerows and alley cropping)at three positions relative to the trees.The resistance and resilience of nitrification potential were measured in the laboratory after four weeks of flooding stress and four weeks after the end of the stress,respectively.For the first time,we used multigroup latent structural equation modeling(ML-SEM)to explore the spatial structure of causal relations between nitrification stability and soil properties across all positions of the two silvopastoral systems.Results Tree rows of both systems favored nitrification resistance,while the mean nitrification potential under flooded conditions was on average 27%and 35%higher as compared to non-stressed soils at the two positions assessed in the grass alleys.ML-SEM revealed that the causal relations that explained these results differed between the two systems.The ML-SEM models tested were unable to explain the causal relations in the hedgerow system.However,the model that considered a covariance between soil physical properties and soil resources availability(model A)was able to explain them in the alley-cropping system.It revealed that causal relations explaining nitrification stability varied according to the position relative to the trees:in the tree rows nitrification stability was associated with higher soil organic carbon concentration and earthworm abundance;in the grass alleys it was associated with higher soil organic carbon concentration and soil bulk density.Conclusions This study indicates that silvopastoral systems help regulate the N cycle near the trees.The results further imply that improvements in soil organic carbon concentration and soil bulk density favor the regulation of N-related processes in grasslands.展开更多
Climate change affects various facets of life but there is little data on its effects on wild mushroom fruiting.Yunnan Province in China is a rich source of wild mushrooms and has experienced a temperature rise over r...Climate change affects various facets of life but there is little data on its effects on wild mushroom fruiting.Yunnan Province in China is a rich source of wild mushrooms and has experienced a temperature rise over recent decades.This has resulted in warmer temperatures but the impacts of these changes on mushroom production lack documentation.We collected data on the fruiting of the highly prized matsutake mushroom(Tricholoma matsutake)in West Yunnan,China over an 11 year period from 2000 to 2010.Fruiting phenology and productivity were compared against the driving meteorological variables using Projection to Latent Structure regression.The mushrooms appeared later in the season during the observation period,which is most likely explained by rising temperatures and reduced rain during May and June.High temperature and abundant rain in August resulted in good productivity.The climate response of matsutake production results from a sequence of processes that are possibly linked with regulatory signals and resource availability.To advance the knowledge of this complex system,a holistic research approach integrating biology,ecology,genetics,physiology,and phytochemistry is needed.Our results contribute to a general model of fungal ecology,which can be used to predict the responses of fungi to global climate change.展开更多
A new filtering method is presented which extends the SureShrink algorithm by eliminating the peak noise in the wavelet transformed signal to improve the overall filtering properties. Data from industrial plants alway...A new filtering method is presented which extends the SureShrink algorithm by eliminating the peak noise in the wavelet transformed signal to improve the overall filtering properties. Data from industrial plants always contain some peak noise, but ‘denoise’ algorithms such as ‘SureShrink’ can have difficulty in handling sudden large excursions in the corrupting noise. In the new algorithm the peak noise is reduced prior to filtering using the SureShrink algorithm. The pre screened data can be used to build a number of projections to latent structures regression models. Data from an industrial fluidized bed reactor is used to evaluate the new algorithm, which demonstrates improved performance in terms of improved modeling capability through use of the new data pre filtering algorithm.展开更多
基金supported by the National Natural Science Foundation of China(62273354,61673387,61833016).
文摘As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
文摘A short review is given of standard regression analysis. It is shown that the results presented by program packages are not always reliable. Here is presented a general framework for linear regression that includes most linear regression methods based on linear algebra. The H-principle of mathematical modelling is presented. It uses the analogy between the modelling task and measurement situation in quantum mechanics. The principle states that the modelling task should be carried out in steps where at each step an optimal balance should be determined between the value of the objective function, the fit, and the associated precision. H-methods are different methods to carry out the modelling task based on recommendations of the H-principle. They have been applied to different types of data. In general, they provide better predictions than linear regression methods in the literature.
基金This study was supported by a grant from the China Academy of Chinese Medical Sciences(No.C12021A00404).
文摘Objective:To analyze syndrome differentiation and medication rules for leucorrhea in ancient books of traditional Chinese medicine(TCM)based on the“Chinese Medical Code”using the latent structure model.Methods:The formulations for leucorrhea were searched in the Chinese Medical Code(software),based on which an Excel sheet was created to conduct frequency analysis and construct a matrix of high-frequency medications.The high-frequency medications for leucorrhea were then analyzed using the Latent 5.0 software and a latent structure model.With SPSS Modeler and Latent 5.0 as the main analysis methods,Chinese medications with a frequency>1.00%were investigated using the latent structure model and association rules,and the medication rules in treating leucorrhea with TCM were dissected based on the data of frequency.Results:For 373 kinds of Chinese medicines retrieved from 236 TCM prescriptions,the total medication frequency was 2522.Chinese Angelica(Angelicae Sinensis Radix),White Peony Root(Paeoniae Radix Alba),Indian Buead Tuckahoe(Poria),Common Oyster Shell(Ostreae Concha),and Largehead Atractylodes Rhizome(Atractylodis Macrocephalae Rhizoma)were high-frequency medicines for leucorrhea.Tonic,heat-clearing,and qi-regulating medicines had a high frequency of usage.According to a formula-based analysis of the evidence,the main syndromes of leucorrhea included liver qi affecting the spleen pattern,damp heat accumulation pattern,liver qi stagnation pattern,liver-stomach disharmony pattern,yin deficiency of the liver and kidney pattern,and spleen deficiency with dampness pattern,which are generally in line with the etiology and pathogenesis of leucorrhea,namely pathogenic dampness as the detrimental factor together with spleen-kidney dysfunction.However,in ancient books,emphasis was laid out on the regulation of the liver.This study determined 31 association rules for treating leucorrhea,where the primary compatible medicines contained Chinese Angelica(Angelicae Sinensis Radix)-Nutgrass Galingale Rhizome(Cyperus rotundus),Chinese Angelica(Angelicae Sinensis Radix)-Szechwan Lovage Rhizome(Ligusticum chuanxiong Hort.),White Peony Root(Paeoniae Radix Alba)-Szechwan Lovage Rhizome(Ligusticum chuanxiong Hort.),White Peony Root(Paeoniae Radix Alba)-Adhesive Rehmannia Root Tuber(Rehmanniae Radix)and Tangerine Peel(Citri Reticulatae Pericarpium)-Ternate Pinellia(Rhizoma Pinelliae).Conclusion:The medication rules targeting leucorrhea in the Chinese Medical Code were mainly represented by the regulation of qi and blood,as well as the 3 internal organs-the kidney,spleen,and liver.
基金supported by China Academy of Chinese Medical Science and Technology Innovation Project Heidihuang Pill Major Research Project(No.CI2021A00606)China Academy of Chinese Medical Sciences Self-selected projects(YZX202237,YZX202241,YZX202246,YZ2020042,YZ202225,YZ2020019)Beijing Traditional TCM Science and Technology Development Fund Project(No.JJ2018-99).
文摘Objective:To analyze the identification and typing of bone flaccidity and the law of medication in ancient books based on the Encyclopaedia of TCM(Traditional Chinese Medicine)using the hidden structure model.Methods:The formulations for bone flaccidity were searched in the Chinese Medical Code(software),based on which an Excel sheet was created to conduct frequency analysis and construct a matrix of high-frequency medications.The high-frequency medications for bone flaccidity were then analyzed using the Latent 5.0 software and a latent structure model.With SPSS Modeler and Latent 5.0 as the main analysis methods,Chinese medications with a frequency>1.00%were investigated using the latent structure model and association rules,and the medication rules in treating bone flaccidity with TCM were dissected based on the data of frequency.Results:A total of 223 TCM prescriptions were included,with 270 extracted TCM and a total medication frequency of 2340 times.Among them are Achyranthis Bidentatae Radix,Poria,Angelicae Sinensis Radix,Eucommiae Cortex,Rehmanniae Radix Praeparata,and Ginseng Radix Et Rhizoma.They were the high-frequency medicines for treating bone flaccidity,and the efficacy of tonifying medicinal,heat-clearing medicinal,and blood activating and stasis-resolving medicinal were high-frequency medicinal categories.Through the analysis of the evidence by formula,it was concluded that spleen-kidney yang deficiency,kidney deficiency and blood stasis,kidney yang deficiency,qi deficiency/qi stagnation and blood stasis,and liver and kidney yin deficiency were the 6 main types of bone flaccidity,which was generally in line with the etiology of bone flaccidity,which was caused by kidney yang deficiency and accompanied by malfunction of the liver and kidneys.In ancient books,the regulation of the kidney is put into an important position.In this study,19 correlative principles for the treatment of bone flaccidity were derived,whose main combinations are Paeoniae Radix Alba-Angelicae Sinensis Radix,Eucommiae Cortex+Poria-Ginseng Radix Et Rhizoma,Dioscoreae Spongiosae Rhizoma+Cuscutae Semen-Eucommiae Cortex.Conclusions:The medication law of bone flaccidity in the Encyclopaedia of TCM mainly shows that it attaches importance to the regulation of tonifying the liver and kidney to strengthen the tendons and bones and emphasizes that the regulation of the 3 Zang–fu organs,namely the kidney,the liver and the spleen,can play an important role in the treatment of bone flaccidity.
基金Supported by Items of Institute of Basic Research in Clinical Medicine,China Academy of Chinese Medical Sciences Natural Science Fundation(No.30873339)
文摘Syndrome differentiation is the character of Chinese medicine （CM）. Disease differentiation is the principle of Western medicine （WM）. Identifying basic syndromes feature and structure of disease of WM is an important avenue for prevention and treatment of integrated Chinese and Western medicine. The idea here is first to divide all patients suffering from a disease of WM into several groups in the light of the stage of the disease, and secondly to identify basic syndromes feature in a distinct stage, and finally to achieve the purpose of syndrome differentiation. Syndrome differentiation is simply taken as a classifier that classifies patients into distinct classes primarily based on overall observation of their symptoms. Previous clustering methods are unable to cope with the complexity of CM. We therefore show a new multi-dimensional clustering method in the form of general latent structure （GLS） model, which is a suitable statistical learning technique of latent class analysis. In this paper, we learn an optimal GLS model which reflects much better model quality compared with other latent class models from the osteoporosis patient of community women （OPCW） real data including 40 65 year old women whose bone mineral density （BMD） is less than mean2.0 standard deviation （M 2.0SD）. Further, we illustrate a case analysis of statistical identification of CM syndromes feature and structure of OPCW from qualitative and quantitative contents through the GLS model. Our analysis has discovered natural clusters and structures that correspond well to CM basic syndrome and factors of osteoporosis patients （OP）. The GLS model suggests the possibility of establishing objective and quantitative diagnosis standards for syndrome differentiation on OPCW. Hence, for the future it can provide a reference for the similar study from the perspective of a combination of disease differentiation and syndrome differentiation.
基金Hong Kong Grants Council Grants #622105 and #622307the National Basic Research Program of China (aka the 973 Program) under project No.2003CB517106.
文摘The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.
基金Supported by National Natural Science Foundation of China(72222009,71991472)。
文摘Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.
基金The National Key Basic Research Program of China under contract No.2014CB953904the Natural Science Foundation of Guangdong Province under contract No.2015A030311026the National Natural Science Foundation of China under contract Nos 41275145 and 41275060
文摘The weather research and forecasting（WRF） model is a new generation mesoscale numerical model with a fine grid resolution（2 km）, making it ideal to simulate the macro-and micro-physical processes and latent heating within Typhoon Molave（2009）. Simulations based on a single-moment, six-class microphysical scheme are shown to be reasonable, following verification of results for the typhoon track, wind intensity, precipitation pattern, as well as inner-core thermodynamic and dynamic structures. After calculating latent heating rate, it is concluded that the total latent heat is mainly derived from condensation below the zero degree isotherm, and from deposition above this isotherm. It is revealed that cloud microphysical processes related to graupel are the most important contributors to the total latent heat. Other important latent heat contributors in the simulated Typhoon Molave are condensation of cloud water, deposition of cloud ice, deposition of snow, initiation of cloud ice crystals, deposition of graupel, accretion of cloud water by graupel, evaporation of cloud water and rainwater,sublimation of snow, sublimation of graupel, melting of graupel, and sublimation of cloud ice. In essence, the simulated latent heat profile is similar to ones recorded by the Tropical Rainfall Measuring Mission, although specific values differ slightly.
文摘As far as Chinese art,YIN YANG viewpoint and TAI JI thought derived from ZHOU YI have been embedded deeply into Chinese artistic creating for thousand years,the typical examples are traditional Chinese painting and mountains-and -waters painting.If people do not find the basis to explain the methodology of "Qi"and"Yun"from the origin,the know of traditional culture is all in vain."Qi"expresses an invisible space concept,it has raised to a cultural philosophical concept in Chinese eyes,the explanation of this problem will be of benefit to the search for the latent structure in Chinese national artistic form,and the modern signification of latent structure at present would be found.
基金supported by the National Natural Science Foundation of China(61472305)the Science Research Program,Xi’an,China(2017073CG/RC036CXDKD003)the Aeronautical Science Foundation of China(20151981009)
文摘Multi-label classification problems arise frequently in text categorization, and many other related applications. Like conventional categorization problems, multi-label categorization tasks suffer from the curse of high dimensionality. Existing multi-label dimensionality reduction methods mainly suffer from two limitations. First, latent nonlinear structures are not utilized in the input space. Second, the label information is not fully exploited. This paper proposes a new method, multi-label local discriminative embedding (MLDE), which exploits latent structures to minimize intraclass distances and maximize interclass distances on the basis of label correlations. The latent structures are extracted by constructing two sets of adjacency graphs to make use of nonlinear information. Non-symmetric label correlations, which are the case in real applications, are adopted. The problem is formulated into a global objective function and a linear mapping is achieved to solve out-of-sample problems. Empirical studies across 11 Yahoo sub-tasks, Enron and Bibtex are conducted to validate the superiority of MLDE to state-of-art multi-label dimensionality reduction methods.
基金National Natural Science Foundation of China(No.51467008)。
文摘Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is proposed.Orthogonal projections to latent structures(O-PLS)is a general linear multi-variable data modeling method.It can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel technique”to calculate predictive components and response-orthogonal components in the model.Therefore,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain extent.To verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.
基金Project supported by the National Natural Science Fund of China(No.11204046)the International Science and Technology Cooperation Project of China(No.2014DFA00670)the Guizhou Province International Science and Technology Cooperation Project of China(No.QKHG[2011]7001)
文摘This work studied the effect of differential temperatures on the latent heat in the nucleation of CdSe quantum dots（QDs）.The result showed that,by the formula of phase change,with increasing the reaction temperature,the latent heat in the nucleation of QDs reduced.CdSe QDs with the size-dispersion from 2.7 to 3.6 nm were synthesized via oleic acid-paraffin liquid system by controlling the reaction temperature from 180 to 220℃.Synthesized QDs were characterized by UV-vis absorption spectra and X-ray diffraction（XRD）.The result of UV-vis absorption spectra showed that with increasing of reaction temperature,the first absorption peak was red-shifted and the size of QD increased.The result of XRD showed that the synthesized QDs were zinc-blende structure.
基金financially supported by La Fondation de France(grant no.00117721/WB-2021-35937).
文摘Background Extreme rainfall and flooding events are projected to increase in frequency and disturb biogeochemical cycles such as the nitrogen(N)cycle.By combining trees and grasses,silvopastoral agroforestry is expected to increase the stability of this cycle in response to flooding.However,little is known about the response of nitrification to flooding in silvopastoral systems.Aim of this study was to assess nitrification stability in response to flooding and identify the main causal relations that drive it in temperate silvopastures.Methods The nitrification stability(i.e.,resistance and resilience)was assessed in two silvopastoral systems(i.e.,hedgerows and alley cropping)at three positions relative to the trees.The resistance and resilience of nitrification potential were measured in the laboratory after four weeks of flooding stress and four weeks after the end of the stress,respectively.For the first time,we used multigroup latent structural equation modeling(ML-SEM)to explore the spatial structure of causal relations between nitrification stability and soil properties across all positions of the two silvopastoral systems.Results Tree rows of both systems favored nitrification resistance,while the mean nitrification potential under flooded conditions was on average 27%and 35%higher as compared to non-stressed soils at the two positions assessed in the grass alleys.ML-SEM revealed that the causal relations that explained these results differed between the two systems.The ML-SEM models tested were unable to explain the causal relations in the hedgerow system.However,the model that considered a covariance between soil physical properties and soil resources availability(model A)was able to explain them in the alley-cropping system.It revealed that causal relations explaining nitrification stability varied according to the position relative to the trees:in the tree rows nitrification stability was associated with higher soil organic carbon concentration and earthworm abundance;in the grass alleys it was associated with higher soil organic carbon concentration and soil bulk density.Conclusions This study indicates that silvopastoral systems help regulate the N cycle near the trees.The results further imply that improvements in soil organic carbon concentration and soil bulk density favor the regulation of N-related processes in grasslands.
基金sponsored jointly by the National Natural Science Foundation of China(Grant No.30800158)the 11th Five-Year China Key Science&Technology Project on Silviculture for Carbon Sequestration in Subtropics(Grant No:2008BAD95B09)+3 种基金the Ford Foundation(Grant No.10850639)the National Research Council of Thailand(grant NRCT/55201020007)Mae Fah Luang University(grant MFU/54101020048)King Saud University for support.
文摘Climate change affects various facets of life but there is little data on its effects on wild mushroom fruiting.Yunnan Province in China is a rich source of wild mushrooms and has experienced a temperature rise over recent decades.This has resulted in warmer temperatures but the impacts of these changes on mushroom production lack documentation.We collected data on the fruiting of the highly prized matsutake mushroom(Tricholoma matsutake)in West Yunnan,China over an 11 year period from 2000 to 2010.Fruiting phenology and productivity were compared against the driving meteorological variables using Projection to Latent Structure regression.The mushrooms appeared later in the season during the observation period,which is most likely explained by rising temperatures and reduced rain during May and June.High temperature and abundant rain in August resulted in good productivity.The climate response of matsutake production results from a sequence of processes that are possibly linked with regulatory signals and resource availability.To advance the knowledge of this complex system,a holistic research approach integrating biology,ecology,genetics,physiology,and phytochemistry is needed.Our results contribute to a general model of fungal ecology,which can be used to predict the responses of fungi to global climate change.
基金the National Natural Science Foundationof China !( No. 6963 5 0 10 ) the State High-TechDevelopments Plan of China!( N0863-
文摘A new filtering method is presented which extends the SureShrink algorithm by eliminating the peak noise in the wavelet transformed signal to improve the overall filtering properties. Data from industrial plants always contain some peak noise, but ‘denoise’ algorithms such as ‘SureShrink’ can have difficulty in handling sudden large excursions in the corrupting noise. In the new algorithm the peak noise is reduced prior to filtering using the SureShrink algorithm. The pre screened data can be used to build a number of projections to latent structures regression models. Data from an industrial fluidized bed reactor is used to evaluate the new algorithm, which demonstrates improved performance in terms of improved modeling capability through use of the new data pre filtering algorithm.
